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1
Yanti, Roaida, Jundi Nourfateha Elquthb, Ira Promasanti Rachmadewi, and Qurtubi Qurtubi. "Bibliometric study of association rule-market basket analysis." International Journal of Advances in Applied Sciences 13, no. 2 (2024): 282. http://dx.doi.org/10.11591/ijaas.v13.i2.pp282-290.
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Association rule-market basket analysis (AR-MBA) is a data mining technique for finding distinguished relationship patterns from a collection of items. The application of AR-MBA is also increasingly widespread, starting from retail and hotels to hospitals. So, bibliometrics related to AR-MBA needs to be done to reveal what research opportunities can be later carried out by reviewing and analyzing publications about AR-MBA. 91 bibliographies in 1 decade from 2012-2022 were collected using Harzing's Publish or Perish (PoP). VOSviewer is also employed to map authorship and publication topic trends. This paper is innovative because it identifies trends and future research directions in data mining, specifically in association with AR-MBA. The findings show publication productivity, top authors, types of publications, annual topic trends within a decade, term distribution, most cited and most influential articles, and research gaps that can be opportunities for further research.
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Roaida, Yanti, Nourfateha Elquthb Jundi, Promasanti Rachmadewi Ira, and Qurtubi. "Bibliometric study of association rule-market basket analysis." International Journal of Advances in Applied Sciences (IJAAS) 13, no. 2 (2024): 282–90. https://doi.org/10.11591/ijaas.v13.i2.pp282-290.
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Association rule-market basket analysis (AR-MBA) is a data mining technique for finding distinguished relationship patterns from a collection of items. The application of AR-MBA is also increasingly widespread, starting from retail and hotels to hospitals. So, bibliometrics related to AR-MBA needs to be done to reveal what research opportunities can be later carried out by reviewing and analyzing publications about AR-MBA. 91 bibliographies in 1 decade from 2012-2022 were collected using Harzing's Publish or Perish (PoP). VOSviewer is also employed to map authorship and publication topic trends. This paper is innovative because it identifies trends and future research directions in data mining, specifically in association with AR-MBA. The findings show publication productivity, top authors, types of publications, annual topic trends within a decade, term distribution, most cited and most influential articles, and research gaps that can be opportunities for further research.
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Tanusha, Gorak. "STUDY AND ANALYSIS OF MARKET BASKET ANALYSIS USING APIRORI ALGORITHM." International Journal of Interpreting Enigma Engineers 01, no. 02 (2024): 20–27. http://dx.doi.org/10.62674/ijiee.2024.v1i02.004.
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One data mining technique that has been increasingly popular across several industries, especially in retail and e-commerce, is market basket analysis, or MBA. This technique examines transactional data to find patterns and relationships between the products that are frequently purchased. An MBA is essential for understanding consumer behavior, which helps companies place products more effectively, develop more effective marketing campaigns, and run their operations more efficiently overall. The MBA is still a vital tool for organizations to stay abreast of changing customer preferences, stimulate rapid decision-making, and extract relevant information from the massive amounts of transactional data they are dealing with. MBA is used in various industries outside of retail, including supply chain management, healthcare, and internet platforms.
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Tiwari, Shivam, Prem Prakash, and Vaishnavi Dixit. "Enhancing Market Basket Analysis Through the Interplay of Advertisement and Technology." Research & Review: Machine Learning and Cloud Computing 2, no. 1 (2023): 1–6. http://dx.doi.org/10.46610/rrmlcc.2023.v02i01.001.
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Market Basket Analysis (MBA) is a crucial technique used in the field of data mining to understand consumer purchasing patterns. The importance of advertisement in an MBA has been widely acknowledged as a key factor in influencing consumer behavior. With the advent of technology, MBA is undergoing a paradigm shift, with new tools and techniques being developed to improve its accuracy and efficiency. This research paper focuses on the various techniques used in MBA, the role of advertisement in MBA, and the impact of technology on the improvement of MBA. The paper discusses the various algorithms and data mining techniques used in MBA and their advantages and disadvantages. Additionally, it analyzes the importance of advertisement in MBA, its impact on consumer behavior, and the role of technology in enhancing MBA. The paper concludes by highlighting the potential of technology in revolutionizing the MBA field, providing more accurate and efficient results, and ultimately improving business outcomes.
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Anwar Arifin, Indra Wahyudi,. "PERBAIKAN TATA LETAK PASAR INDUK TRADISIONAL DI SANGATTA DENGAN METODE MARKET BASKET ANALYSIS (MBA)." Research Journal of Accounting and Business Management 5, no. 2 (2022): 113. http://dx.doi.org/10.31293/rjabm.v5i2.5784.
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ABSTRACTThe customer is one of the goals of the market. One of the factors that influence consumer satisfaction is the arrangement of products/products. An attractive display becomes the face of the market itself. The Traditional Main Market in Sangatta is a market that is satisfied with consumers. So far, the product arrangement of the Traditional Main Market in Sangatta has only been based on the intuition of the market management, while the wide market area causes consumers to feel tired when shopping.The Market Basket Analysis method aims to determine what products are usually purchased by consumers at the same time. In addition, the application of this method can provide convenience for consumers in shopping, because goods that are usually purchased simultaneously are nearby. In addition, it can improve efficiency in conducting promotions. Products purchased should not be produced simultaneously, promoting one product can increase sales of other products that are usually purchased simultaneouslyThis study uses the Market Basket Analysis (MBA) method. Product information that dominates sales can be used as a reference to determine consumer habits in making purchases. The habit in question is any product that consumers buy at the same time. The use of Market Basket Analysis (MBA) is expected to cause impulse buying/purchases that were not planned. In addition to using Market Basket Analysis (MBA), Activity Relationship Chart (ARC) is also used as a tool to get layout results that are by consumer habits in shopping and with government regulations. Keywords : Product layout, Market basket analysis, Activity relationship chart.
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Rizkybayunovrianto and Muslim. "Penerapan Metode Market Basket Analysis pada Minimarket Toko Baru." Indonesian Journal of Data and Science 1, no. 1 (2020): 1–5. http://dx.doi.org/10.33096/ijodas.v1i1.2.
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Toko Baru merupakan suatu Usaha Kecil dan Menengah (UKM) yang mengembangkan bisnis dibidang minimarket. Toko Baru selama ini hanya dijalankan pribadi oleh pemiliknya, pemasaran pun dilakukan secara sendiri dan manual. Oleh karena itu salah satu upaya untuk mempermudah penjualan pada toko Baru adalah membuat suatu sistem yang digunakan untuk memenuhi persaingan dalam mengimplemasikan metode Market Basket Analysis (MBA).
 Market Basket Analysis (MBA) merupakan salah satu metode atau teknik yang dapat digunakan dan dimanfaatkan untuk lingkungan marketing. Metode ini digunakan untuk menentukan produk-produk manakah yang akan dibeli konsumen secara bersamaan dengan analisa terhadap daftar transaksi pelanggan yang dilihat pada support dan confident setiap barang.Hasil dari proses pencarian dengan software membuktikan bahwa hubungan yang terjadi antar item sangat penting dan kuat, karena ada pembelian satu item terkait pada item lainnya.
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Verda, Damiano, and Marco Muselli. "Alternative Support Threshold Computation for Market Basket Analysis." AppliedMath 5, no. 2 (2025): 71. https://doi.org/10.3390/appliedmath5020071.
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This article aims to limit the rule explosion problem affecting market basket analysis (MBA) algorithms. More specifically, it is shown how, if the minimum support threshold is not specified explicitly, but in terms of the number of items to consider, it is possible to compute an upper bound for the number of generated association rules. Moreover, if the results of previous analyses (with different thresholds) are available, this information can also be taken into account, hence refining the upper bound and also being able to compute lower bounds. The support determination technique is implemented as an extension to the Apriori algorithm but may be applied to any other MBA technique. Tests are executed on benchmarks and on a real problem provided by one of the major Italian supermarket chains, regarding more than 500,000 transactions. Experiments show, on these benchmarks, that the rate of growth in the number of rules between tests with increasingly more permissive thresholds ranges, with the proposed method, is from 21.4 to 31.8, while it would range from 39.6 to 3994.3 if the traditional thresholding method were applied.
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Fageeri, Sallam Osman, Mohammad Abu Kausar, and Arockiasamy Soosaimanickam. "MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 15–21. http://dx.doi.org/10.17762/ijritcc.v11i5s.6591.
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This Market Basket Analysis (MBA) is a data mining technique that uses frequent pattern mining algorithms to discover patterns of co-occurrence among items that are frequently purchased together. It is commonly used in retail and e-commerce businesses to generate association rules that describe the relationships between different items, and to make recommendations to customers based on their previous purchases. MBA is a powerful tool for identifying patterns of co-occurrence and generating insights that can improve sales and marketing strategies. Although a numerous works has been carried out to handle the computational cost for discovering the frequent itemsets, but it still needs more exploration and developments. In this paper, we introduce an efficient Bitwise-Based data structure technique for mining frequent pattern in large-scale databases. The algorithm scans the original database once, using the Bitwise-Based data representations as well as vertical database layout, compared to the well-known Apriori and FP-Growth algorithm. Bitwise-Based technique enhance the problems of multiple passes over the original database, hence, minimizes the execution time. Extensive experiments have been carried out to validate our technique, which outperform Apriori, Éclat, FP-growth, and H-mine in terms of execution time for Market Basket Analysis.
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Yoseph, Fahed, and Markku Heikkilä. "A new approach for association rules mining using computational and artificial intelligence." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7233–46. http://dx.doi.org/10.3233/jifs-200707.
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Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment.
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Aulia, Fauzan, and Kiki Yulianto. "Perancangan Market Basket Analysis dengan Model Casual Loop Diagram untuk Pengembangan Restoran." GreenTech 1, no. 2 (2024): 291–99. https://doi.org/10.25077/greentech.v1i2.27.
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Penelitian ini mengusulkan integrasi antara Market Basket Analysis (MBA) dan Causal Loop Diagram (CLD) untuk mengatasi tantangan dalam pengelolaan restoran yang kompleks. Pendekatan ini bertujuan menggali pola pembelian pelanggan serta memahami dinamika sistem yang memengaruhi operasional restoran, seperti waktu tunggu dan kepuasan pelanggan. Data transaksi dianalisis menggunakan algoritma Apriori dengan parameter minimum support 0,01 dan confidence 0,50 untuk mengidentifikasi hubungan antar produk. Hasil MBA menjadi dasar pengembangan CLD yang memetakan interaksi kausal antar variabel utama, termasuk promosi, popularitas produk, dan laba restoran. Penelitian ini menghasilkan rekomendasi strategis untuk optimalisasi promosi bundling, pengelolaan waktu tunggu, dan efisiensi perencanaan stok. Integrasi MBA dan CLD memberikan pendekatan holistik untuk meningkatkan laba serta kepuasan pelanggan secara berkelanjutan.
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Ursulum, Jr., Daniel T., and Thelma D. Palaoag. "MARKET 4.0: EXPLORING MARKET BASKET ANALYSIS (MBA) ALGORITHMS FOR CO-MARKET INTELLIGENT APPLICATION." Proceedings on Engineering Sciences 6, no. 4 (2024): 1523–30. https://doi.org/10.24874/pes06.04.011.
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Kalpana Salunkhe. "Uncovering Hidden Patterns: An Exploration of Market Basket Analysis in Retails." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 508–15. https://doi.org/10.52783/jisem.v10i4.9609.
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MBA stands for market basket analysis, a mathematical modelling technique used to analyze customer purchasing patterns and identify trends in transactional data. Retailers can increase sales and customer satisfaction by employing an MBA to better understand their consumers' purchasing patterns and develop targeted marketing strategies. The method concentrates on descriptive analysis of customer purchases, items bought in tandem, and highly purchased units in order to facilitate reordering and guarantee appropriate product stock. By creating association rules, correlations are determined and frequent item sets are discovered through data analysis.
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Isa, N., N. A.Kamaruzzaman, M. A. Ramlan, N. Mohamed, and M. Puteh. "Market Basket Analysis of Customer Buying Patterns at Corm Café." International Journal of Engineering & Technology 7, no. 4.42 (2018): 119–23. http://dx.doi.org/10.14419/ijet.v7i4.42.25692.
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Market Basket Analysis (MBA) is a technique in data mining used to seek the co-occurrence set of items in a large dataset or database. It is usually used in mining transactions or basket data, especially in retail. This technique has been proven beneficial in understanding customer buying patterns and preferences. It has been widely used in multinational companies. Current business trends have changed dramatically, parallel with the advancement of technology. Changes in customer demand requires an improvement in accuracy of business operations. This paper proposes the implementation of MBA at a Small Medium Enterprise business, a case study at Corm Café. Daily transaction data taken from customer order sheets has been used. A detailed implementation is demonstrated in the paper. The results identify a trend in customer buying patterns, which is useful information for the owner in planning their business operation. Â
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Ghous, Hamid, Mubasher Malik, and Iqra Rehman. "Deep Learning based Market Basket Analysis using Association Rules." KIET Journal of Computing and Information Sciences 6, no. 2 (2023): 14–34. http://dx.doi.org/10.51153/kjcis.v6i2.166.
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Market Basket Analysis (MBA) is a data mining technique assisting retailers in determining the customer's buying habits while making new marketing decisions as the buyer's desire frequently changes with expanding needs; therefore, transactional data is getting large every day. There is a demand to implement Deep Learning (DL) methods to manipulate this rapidly growing data. In previous research, many authors conducted MBA applying DL and association rules (AR) on retail datasets. AR identifies the association between items to find in which order the customer place items in the basket. AR is only used in mining frequently purchased items from retail datasets. There is a gap in classifying these rules and predicting the next basket item using DL on the transactional dataset. This work proposes a framework using AR as a feature selection while applying DL methods for classification and prediction. The experiments were conducted on two datasets, InstaCart and real-life data from Bites Bakers, which operates as a growing store with three branches and 2233 products. The AR classified at 80,20 and 70,30 splits using CNNN, Bi- LSTM, and CNN-BiLSTM. The results considering simulation at both splits show that Bi-LSTM performs with high accuracy, around 0.92 on the InstaCart dataset. In contrast, CNN-BiLSTM performs best at an accuracy of around 0.77 on Bites Bakers dataset.
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Muzakir, Ari, and Laili Adha. "MARKET BASKET ANALYSIS (MBA) PADA SITUS WEB E-COMMERCE ZAKIYAH COLLECTION." Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 7, no. 2 (2016): 459. http://dx.doi.org/10.24176/simet.v7i2.755.
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E-commerce menghubungkan antara produsen dengan produsen, produsen dengan konsumen, konsumen dengan produsen, konsumen dengan konsumen. Untuk mengimplementasi e-commerce dalam mendukung bisnis organisasi perlu di perhatikan 5 komponen utama yaitu ; pengembangan produk, promosi, transaksi online, product delivery dan after sales support. Hal ini yang tengah diterapkan pada Zakiyah Collection. Zakiyah Collection bergerak dibidang penjualan aneka macam kain khas Palembang seperti songket, blongket,tanjung, dan lain sebagainya. Untuk melakukan analisis terhadap pangsa pasar yang ada agar dapat bersaing dengan toko online lainnya dilakukan dengan strategi pemasaran dengan menggunakan pendekatan market basket analysis (MBA). MBA merupakan salah satu teknik dari data mining yang digunakan untuk menentukan produk-produk manakah yang akan dibeli oleh pelanggan secara bersamaan dengan melakukan analisa terhadap daftar transaksi pelanggan. Dengan mengetahui produk-produk tersebut, maka sebuah sistem e-commerce dapat membuat maupun mengembangkan sebuah sistem customer profiles dan dapat menentukan layout katalog pelanggannya sendiri. Model pengembangan sistem yang dilakukan menggunakan prototype dimana pelanggan dan pengguna akan dilibatkan secara langsung dalam proses ini. Hasil akhir dalam penelitian ini adalah berupa analisis data transaksi menggunakan market basket analysis dengan dilakukan 4 kali kombinasi produk yang berdasarkan nilai support x confidence terbesar dengan hasil berupa angka-angka kemungkinan transasksi yang berkaitan dengan produk yang dijual. Jika dengan menggunakan 1 kali kombinasi, maka didapatkan blongket dengan nilai support sebesar 0.5625. Jika dilakukan 2 kali kombinasi diperoleh kombinasi blongket dan songket dengan nilai support 0.375. Kata kunci: e-commerce, market basket analysis, association rules.
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Rizaldi, Deni, and Arisman Adnan. "Market Basket Analysis Menggunakan Algoritma Apriori: Kasus Transaksi 212 Mart Soebrantas Pekanbaru." Jurnal Statistika dan Aplikasinya 5, no. 1 (2021): 31–40. http://dx.doi.org/10.21009/jsa.05103.
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Market Basket Analysis (MBA) merupakan salah satu teknik penemuan aturan asosiasi dalam data mining. MBA memanfaatkan data transaksi pada suatu toko untuk menentukan strategi penjualan. Konsep utama analisis ini adalah menentukan barang yang dibeli secara bersamaan oleh konsumen. Penentuan asosiasi dalam MBA berdasarkan kriteria minimum support dan confidence. Penelitian ini menggunakan algoritma apriori untuk data transaksi 212 Mart Soebrantas Pekanbaru periode Januari-Desember 2020. Algoritma apriori merupakan algoritma yang efisien untuk menentukan kandidat aturan asosiasi pada data dengan jumlah besar. Aturan asosiasi yang akan dibangkitkan adalah aturan asosiasi antar kelompok item dan asosiasi antar item. Berdasarkan hasil analisis ditemukan aturan asosiasi antar kelompok yang terbaik berdasarkan nilai lift tertinggi yaitu asosiasi antara clothing care dan body care dengan support 6,1% dan confidence 45,88 %. Aturan asosiasi terbaik untuk item yaitu asosiasi Lemonilo Mie Instan Ayam Bawang 7 dan Lemonilo Mie Instan Kari Ayam dengan support 0,17% dan confidence 42,11%.
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Soleh, Muhamad, Nurul Hidayati, and Tri Dedi Pamungkas. "Revitalization Strategy for Traditional Markets Using Market Basket Analysis (AR-MBA) And Service Quality (SERVQUAL) Approaches." Jurnal Sains dan Teknologi Industri 22, no. 1 (2024): 17. https://doi.org/10.24014/sitekin.v22i1.32787.
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The rise of modern markets in Purwokerto provides benefits such as diverse shopping options and increased competition in pricing and services. However, this trend negatively impacts traditional markets, which are experiencing a decline in customers. Understanding customer behaviour and needs is essential for enhancing the competitiveness of traditional markets. The AR-MBA method analyses shopping behaviour by identifying frequent item set combinations. The findings from the AR-MBA analysis can inform business strategies, including promotions, market re-layouts, and marketing campaigns aimed at boosting sales in traditional markets. To enhance competitiveness, traditional markets must be revitalised by improving service quality. A Servqual Model analysis will assess the gap between customer expectations and the current state of these markets. This study aims to restore the vital role of traditional markets by enhancing service quality to compete effectively with modern markets. This research is important for policymakers and market managers as it provides insights that can be used to formulate effective strategies to strengthen the competitiveness of traditional markets and ensure local economic sustainability. This strategy enhances customer convenience by positioning frequently purchased items close together based on the Association Rule analysis. It encourages impulse buying, as shoppers are likelier to notice complementary products. Additionally, a sales strategy focused on bundling products can increase perceived value and motivate customers to purchase more items together, ultimately leading to higher sales and a better shopping experience. By maintaining high-performing areas while addressing gaps in service quality, traditional markets can remain strong competitors against modern retail outlets. Keywords: Traditional market, revitalise, AR-MBA, shopping behavior, Market layout, Servqual Model
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Karim, Zahidul, Sumaya Fatema Binte Shahid, and Shahpar Shams. "Measuring the Impacts of Consumers’ Demographics on Spending Propensity and Expenses on Firms Profitability using Market Basket Optimization Model." International Journal of Business & Economics (IJBE) 7, no. 2 (2022): 173–90. http://dx.doi.org/10.58885/ijbe.v07i2.173.zk.
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Market Basket Analysis (MBA) and its importance in selecting the right basket of goods have obtained considerable interest among the managers and executives of many retail stores. The present study has explored the literature gap in measuring the determinants for managing the optimum market basket of goods for consumers. The study found that some customer demographic and firm specific expense variables provide important prediction power to measure the customer spending propensity and firms’ profitability respectively. We have used two different market basket optimization models to measure and analyze the results. The results show significant influence of age and advertising expenses on consumers’ propensity to expense and firms’ profitability respectively. The study has used two regression models to analyze the market basket using WarpPLS and R Software. Finally, the study suggests for future study to measure the impacts of consumer behavioral and psychological aspects and industry types on the spending propensity and firms’ profitability through market basket analysis.
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Umayah, Binti, and Fachrul Kurniawan. "Analisa Perilaku Konsumen Melalui Data Transaksi Berbasis Pendekatan Market Basket Analysis." Sains, Aplikasi, Komputasi dan Teknologi Informasi 1, no. 2 (2019): 30. http://dx.doi.org/10.30872/jsakti.v1i2.2603.
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Data transaksi merupakan sekumpulan data hasil pencatatan yang berhubungan dengan kegiatan transaksi jual beli pada sebuah perusahaan.Pada tahun terakhir ini, data transaksi sudah banyak digunakan sebagai bahan penelitian dengan tujuan untuk mendapatkan informasi baru.Salah satu usaha yang dapat dilakukan adalah dengan pembuatan aplikasi yang dapat digunakan untuk menganalisis data transaksi yang ada. Aplikasi tersebut adalah aplikasi yang bersifat market basket analysis (MBA). Aplikasi dibangun dengan berbasis desktop, yang didalamnya mampu mengolah serta melakukan pendataan ulang data transaksi yang ada. Metodologi yang digunakan dalam pembuatan aplikasi ini adalah dengan mengikuti tahapan-tahapan yang ada pada teknik data mining. Hasil yang diperoleh dari uji coba yang dilakukan bahwa pembangunan dan penerapan aplikasi MBA dengan metode assocition rule (AR) menggunakan algoritma Apriori dapat berjalan dengan baik. Dengan rata-rata nilai confidence yang diperoleh sebesar 46.69% dan nilai support sebesar 1.78% dan rule yang dihasilkan sebanyak 30 rule.
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Haerah, Kahar, Daryanto Daryanto, Hadi Jatmiko, and Faozen Faozen. "Market Basket Analysis (MBA) Pada Penentuan Daerah Wisata Di Kabupaten Jember." Sadar Wisata: Jurnal Pariwisata 7, no. 1 (2024): 47–51. http://dx.doi.org/10.32528/sw.v7i1.592.
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Pariwisata merupakan salah satu industri yang punya peran besar dalam pengembangan ekonomi di Indonesia. Hal tersebut juga didukung dengan hadirnya para pelaku usaha di industri pariwisata baik dalam skala besar maupun SME. Di era digital saat ini, perkembangan industri pariwisata pun bergerak semakin cepat. Hubungan antara para pelaku industri secara digital maupun di lapangan yang kini terjalin menawarkan kemudahan bagi para wisatawan domestik maupun mancanegara untuk bisa menikmati wisata di Indonesia.Market basket analysis adalah teknik penambangan data yang digunakan untuk mengungkap pola pembelian dalam bidang ritel apa pun. Tujuannya adalah memahami perilaku konsumen dengan mengidentifikasi hubungan antara barang-barang yang dibeli. Contohnya, konsumen yang membeli green tea juga akan cenderung membeli madu. Jadi, teknik ini secara kuantitatif akan menetapkan bahwa ada hubungan antara green tea dan madu
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Jyoti, Upadhay, Rajiv Jain Dr., and Anju Bharti Dr. "Market Basket Analysis: Trend Analysis of Association Rules in Different Time Periods." International Journal of Contemporary Research in Multidisciplinary 4, S2 (2025): 50–56. https://doi.org/10.5281/zenodo.15333750.
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Market Basket Analysis (MBA) is a crucial data mining technique used to identify associations between products based on consumer purchasing behavior. This research aims to examine how association rules evolve over different periods, revealing trends that can optimize retail-marketing strategies. Using a dataset from a retail company, the study employs the Apriori algorithm to extract frequent item sets and assess temporal variations in purchasing patterns. Findings demonstrate significant seasonal shifts and promotional effects, underscoring the importance of time-sensitive marketing strategies in retail. The research contributes to the development of dynamic pricing, targeted advertising, and efficient inventory management based on real-time consumer insights.
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Ferdousi, Reza, Ali Akbar Jamali, and Reza Safdari. "Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis." BioImpacts 10, no. 2 (2019): 97–104. http://dx.doi.org/10.34172/bi.2020.12.
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Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45 000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2 000 000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200 000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.
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Dio, Rafi, Juliza Hidayati, Riski Arifin, Dimas Akmarul Putera, and Aulia Agung Dermawan. "Analisis Data Mining Pembelian dengan Association Rule Market Basket Analysis menggunakan algoritma FP-Growth." Jurnal PASTI (Penelitian dan Aplikasi Sistem dan Teknik Industri) 17, no. 2 (2023): 187. http://dx.doi.org/10.22441/pasti.2023.v17i2.005.
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Pertumbuhan ekonomi di Indonesia menunjukkan kenaikkan dari tahun ke tahun. Salah satu bentuk dari peningkatan pertumbuhan ekonomi di Indonesia adalah dengan meningkatnya daya beli masyarakat. Kenaikan daya beli masyarakat berbanding lurus dengan meningkatnya kehadiran retail di Indonesia. Menghadapi persaingan yang dilakukan dari setiap retail berlomba-lomba untuk melakukan penjualan barang yang sering dibeli oleh masyarakat. Untuk mengetahui data historis ada retail dilakukan dengan AR-MBA untuk memodelkan hubungan produk yang dibeli secara bersamaan. Sehingga tujuan penelitian ini yaitu melihat pembelian antar produk pada suatu retail dengan AR-MBA menggunakan FP-Growth. Penelitian ini menggunakan 450 data dari hasil transaksi pada suatu retail. Hasil penelitian yang didapatkan adalah terdapat 8 associatioan rule terbentuk dengan nilai lift rasio > 1 serta tingkat kepercayaan minimal 30% dari setiap hubungan terbentuk. Berdasarkan 8 rule selanjutnya dilakukan perancangan layout usulan untuk meningkatkan efiseiensi pelanggan saat berbelanja.
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Andy Hermawan, Bayu Wicaksono, Tigfhar Ahmadjayadi, Bagas Surya Prakasa, and Jasico Dacomoro Aruan. "Implementasi Algoritma Apriori pada Market Basket Analysis terhadap Data Penjualan Produk Supermarket." Algoritma : Jurnal Matematika, Ilmu pengetahuan Alam, Kebumian dan Angkasa 2, no. 5 (2024): 95–105. http://dx.doi.org/10.62383/algoritma.v2i5.137.
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Market Basket Analysis (MBA) is an analytical technique used to identify relationships between items in purchasing transactions. This notebook uses retail transaction datasets and the Apriori algorithm to discover hidden associations and patterns that retailers can leverage in optimizing marketing strategies, store layouts, and product recommendations. Through initial data processing, data exploration, and application of the Apriori algorithm, this analysis succeeded in identifying various significant associations between items that are frequently purchased together. The results provide valuable insights for retailers to develop targeted promotions and improve customer shopping experiences, while emphasizing the importance of selecting the right parameters to obtain accurate and relevant results.
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Weulartafella, Mifthahul Pratama, and Nurtriana Hidayati. "Analisis Barang Outbound Di Warehouse Dengan Metode Association Rules Market Basket Analysis Di Pt Indosat Ooredoo Hutchison Gombel." Jurnal Pengembangan Rekayasa dan Teknologi 8, no. 2 (2025): 60–66. https://doi.org/10.26623/jprt.v8i2.11372.
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PT Indosat Ooredoo Hutchison Gombel merupakan salah satu perusahaan penyedia jasa telekomunikasi dan jaringan di Indonesia. Perusahaan ini berada di Jl. Bukit Raya, Kelurahan Ngesrep, Kecamatan Banyumanik Kota Semarang. PT Indosat Ooredoo Hutchison Gombel ini memiliki barang gudang yang digunakan untuk disewakan. Penelitian ini bertujuan untuk mengimplementasikan metode Association Rules Market Basket Analysis dalam menentukan barang keluar secara bersamaan tiap bulannya berdasarkan Rules yang terbentuk dengan menggunakan metode MBA di gudang PT. Indosat Ooredoo Hutchison Gombel dengan menggunakan Google Collab. dan menganalisis barang yang keluar untuk diusulkan pada PT. Indosat Ooredoo Hutchison Gombel. Market Basket Analysis (MBA) merupakan suatu metode analisis atas perilaku konsumen secara spesifik dari suatu golongan/kelompok tertentu. Hal ini berpengaruh bagi pihak PT. Indosat Ooredoo Hutchison Gombel untuk merencanakan pengadaan barang baru di masa depan agar dapat menyesuaikan dengan minat ataupun selera dari pelanggan mereka. Untuk Memenuhi Kebutuhan Tersebut, dilakukanlah pengembangan gudang data dilanjutkan proses analisis memanfaatkan algoritma apriori dalam bahasa pemrograman, Hasil dari penelitian, microwave dan connector memiliki support 50% sebagai barang yang sering keluar secara bersamaan dan network interface and module dan network devices (LAN/WAN) memiliki support 18% sebagai barang yang jarang keluar secara bersamaan .dari hasil ini dapat membantu pihak PT. Indosat Ooredoo Hutchison Gombel Untuk lebih efisien dalam melakukan pengadaan barang untuk inventori Gudang.
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Wulandari, Umi Meganinditya, Akrim Teguh Suseno, and Muhammad Kholilurrahman. "Market Basket Analysis Using FP-Growth and Apriori on Distro Store Sales Transaction." MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) 17, no. 1 (2025): 12–18. https://doi.org/10.18860/mat.v17i1.28820.
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Market Basket Analysis analyzes consumer buying habits by finding relationships between items in the consumer's shopping basket. This Market Basket Analysis can provide success to the retail industry with the ability to understand consumer behavior and the speed of response to information obtained by retail business owners. This understanding is the result of an analysis that can help business owners improve marketing and sales strategies while utilizing transaction data. Sales transaction data that has been accumulated so far has only become data warehouses, while large amounts of transaction data can bring major changes to the level of competition in business and business actors in order to survive in the business world. In addition, after the COVID-19 outbreak, Indonesia experienced a slowdown in economic growth of 5.31%. This can be overcome by utilizing Market Basket Analysis to increase sales from their businesses. MBA with the methods used are FP-Growth and Apriori to analyze store transaction data in order to obtain association rules that can be used in improving marketing strategies. This analysis was carried out with 3 scenarios for 3 different minimum support values (1%, 2% and 3%) but the same minimum confidence value of 0.6 (60%). The comparison of the two methods is that 2 out of 3 scenarios produce the same association rule, namely 1 final association rule result with a lift value of 1.42. The three scenario results from both methods can be used by business owners as a consideration in determining sales strategies.
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Das, Dr Soumitra. "Enhancing Retail Decision-Making through Market Basket Analysis: An Apriori Algorithm Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42142.
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Retail businesses constantly seek data-driven insights to optimize sales strategies and enhance customer experience. Market Basket Analysis (MBA), a key data mining technique, uncovers hidden patterns in consumer purchasing behavior by identifying associations between products. This study employs the Apriori algorithm, a widely used approach for frequent itemset mining, to analyze transactional data and extract meaningful correlations. By leveraging real-world retail datasets, this research highlights how retailers can optimize product placement, crossselling, and promotional strategies. The findings demonstrate the effectiveness of Apriori in improving decision-making by providing actionable insights into consumer buying patterns. Additionally, the study discusses computational efficiency challenges and potential enhancements to the algorithm for large-scale data processing. The results offer valuable implications for retailers, enabling data-driven inventory management, pricing strategies, and personalized recommendations to enhance customer satisfaction and profitability.
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Bagaskara, Septembri Rio, and Dwi Hosanna Bangkalang. "Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data." Jurnal Sistem Komputer dan Informatika (JSON) 4, no. 4 (2023): 612. http://dx.doi.org/10.30865/json.v4i4.6351.
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Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule. Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.
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Sigit Arianto and Andung Jati Nugroho. "ASSOCIATION RULE-MARKET BASKET ANALISIS (AR-MBA) UNTUK MENGANALISIS KEPUTUSAN DALAM PEMBELIAN SAYUR." Jurnal Cakrawala Ilmiah 1, no. 10 (2022): 2637–48. http://dx.doi.org/10.53625/jcijurnalcakrawalailmiah.v1i10.2595.
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Produk Sayur termasuk kedalam salah satu jenis perishable product (produk atau bahan pangan dengan masa hidup yang terbatas dan mudah rusak). Masalah limbah sayur di Indonesia semakin meningkat tiap tahunnya, terbanyak berisi sayuran dan buah. Salah satu toko sayur tosaga di Yogyakarta kabupaten seleman memiliki masalah yang sama, oleh sebab itu toko harus mampu menentukkan bagaimana strategi yang harus diterapkan untuk mengurangi masalah limbah yang menumpuk. Salah satu caranya dengan memahami perilaku konsumen. Penelitian ini bertujuan untuk menganalisis keputusan perilaku konsumen dalam membeli produk sayur dan non sayur yang dibeli pada waktu yang bersamaan tiap bulan berdasarkan Rules yang terbentuk dengan menggunakan metode MBA, menganalisis produk sayur dan non sayur yang sering muncul di transaksi tiap bulan, dan menganalisis strategi pemasaran yang dapat diterapkan pada toko sayur tersebut. Metode Market Basket Analysis (MBA) merupakan suatu metode analisa yang menganalisis perilaku konsumen secara spesifik dari suatu golongan/kelompok tertentu. penelitian ini diolah dengan Phython. Dari Hasil penelitian ini didapatkan rule yang sering dibeli bersamaan mengandung item wortel yang dapat dikatakan sebagai strong consequent.
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Cvrček, Václav, and Masako Fidler. "No keyword is an island: in search of covert associations." Corpora 17, no. 2 (2022): 259–90. http://dx.doi.org/10.3366/cor.2022.0256.
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This paper describes how corpus-assisted discourse analysis based on keyword identification and interpretation can benefit from employing Market Basket Analysis (mba) after keyword extraction. mba is a data mining technique used originally in marketing that can reveal consistent associations between items in a shopping cart, but also between keywords in a corpus of many texts. By identifying recurring associations between keywords, we can compensate for the lack of wider context which is a major issue impeding the interpretation of isolated keywords (especially when analysing large data). To showcase the advantages of mba in ‘re-contextualising’ keywords within the discourse, we conducted a pilot study on the topic of migration, contrasting anti-system and centre-right Czech Internet media. The results show that mba is useful in identifying the dominant strategy of anti-system news portals: to weave in a confounding ideological undercurrent and connect the concept of migrants to a multitude of other topics (i.e., flooding the discourse).
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Mukarim, Rifki Nurul, Dandy Fri Subagja, Demas Emirbuwono Basuki, and Ratna Agil Apriani. "Improving Marketing Strategies Using a Clustering and AR-MBA Methods at Indomaret Kaliurang." Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri 10, no. 1 (2024): 42. http://dx.doi.org/10.24014/jti.v10i1.29288.
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Marketing is the main activity carried out by entrepreneurs to maintain the viability of their business, develop the company, and get maximum profit. Companies need to know the right and appropriate marketing strategy so that the products to be sold on the market. Marketing strategy is one of the strategies applied to the retail industry in Indonesia, one of which is Indomaret. The purpose of this research is to increase market competitiveness so that it can survive and develop, and Indomaret in Kaliurang hopes to make a strategy to increase sales by conducting an analysis of consumer segments and product categories so that they can create sales strategies that can increase sales. The method used is clustering to find potential target markets that have similar characteristics and association rules &; Market basket analysis (AR-MBA) is used to find best practices for product marketing. The results of the Cluster Method with Non-Hierarchical Algorithm K-Means are 3 clusters. Based on the analysis conducted, it was found that the results of the AR-MBA method, there could be 6 rules for goods purchased simultaneously. Marketing strategies that can be applied by Indomaret Kaliurang such as promotions can be created by making products that are rarely purchased as bonuses for buying products that are often purchased. In addition, vouchers can be created as a promotional tool to encourage greater consumer purchases. Keywords: AR-MBA, clustering, sales strategy, strategy marketing
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Audiana, Wini, and Imam Tahyudin. "Application Of Market Basket Analysis For Sales Transaction Analysis Using Association FP-Growth Algorithm." CSRID (Computer Science Research and Its Development Journal) 17, no. 1 (2025): 33–49. https://doi.org/10.22303/csrid-.17.1.2025.33-49.
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Dalam dunia bisnis ritel yang semakin kompetitif, pemanfaatan data transaksi menjadi sangat penting untuk memahami perilaku konsumen dan merancang strategi pemasaran yang efektif. Penelitian ini menggunakan algoritma FP-Growth dalam analisis keranjang belanja (Market Basket Analysis/MBA) untuk mengidentifikasi pola pembelian konsumen di KS Swalayan. Data transaksi yang dianalisis diambil dari bulan Oktober 2024 dan mencakup berbagai atribut, seperti kode barang, nama barang, jumlah, harga satuan, total harga, dan diskon. Proses analisis mengikuti kerangka Knowledge Discovery in Databases (KDD), yang terdiri dari pemilihan, pembersihan, transformasi data, pencarian pola, dan evaluasi hasil. Hasil penelitian menunjukkan bahwa algoritma FP-Growth berhasil mengidentifikasi hubungan asosiatif yang signifikan antara produk-produk. Salah satu contohnya adalah hubungan kuat antara produk "Snack dan Roti" dengan "Susu," yang memiliki nilai lift sebesar 1,414, menunjukkan adanya keterkaitan yang erat. Temuan ini dapat mendukung strategi pemasaran seperti penggabungan produk, pengoptimalan tata letak rak, dan pengelolaan stok yang lebih efisien. Selain meningkatkan pengalaman belanja konsumen, penerapan FP-Growth dapat berfungsi sebagai alat strategis bagi pengelola toko untuk meningkatkan daya saing dan kinerja bisnis berbasis data.
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Dr B.Anjan Kumar, Dr B. Anjan Kumar. "Examining Consumer Buying Behavior in Grocery Retail through Market Basket Analysis in Indian Retail Industry." International Journal of Business and Management Invention 14, no. 6 (2025): 42–45. https://doi.org/10.35629/8028-14064245.
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An understanding of buyer behaviour essential to optimize the strategies in the competitive world where there in the field of Indian grocery market. The authors employs Market Basket Analysis(MBA) and data mining techniques to unearth the hidden patterns and associations that help retailers to bundle the products and increase the sales volumes. Data mining techniques support to analyse the transaction records and identify frequently co-purchased product combinations, seasonal buying trends and consumption patterns. These insights assist retailers make product placements, inventory management and set targeted promotional strategies. The study further explores the implications that impact cultural, economic conditions of Indian grocery customers. The findings provide actionable recommendations for retailers to drive customer satisfaction, increase purchase volumes, improve value chain and strengthen market competitiveness in the ever evolving offline and online landscape.
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Omol, Edwin Juma, Dorcas Awino Onyango, Lucy Waruguru Mburu, and Paul Anyango Abuonji. "Apriori Algorithm and Market Basket Analysis to Uncover Consumer Buying Patterns: Case of a Kenyan Supermarket." Buana Information Technology and Computer Sciences (BIT and CS) 5, no. 2 (2024): 51–63. https://doi.org/10.36805/bit-cs.v5i2.6082.
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This article presents a study on utilizing the Apriori algorithm and Market Basket Analysis (MBA) to reveal consumer buying patterns in supermarkets. The aim of this research is to explore the effectiveness of these data mining techniques in revealing valuable insights that can inform marketing strategies and enhance the overall shopping experience for customers. This study centered on improving customer loyalty within the supermarket setting through the utilization of cutting-edge information technology and programming applications, including Python. Specifically, the Apriori algorithm libraries of the Python language were employed to identify frequent item sets and derive 42 association rules, which shed light on product affinities and co-purchasing patterns. By deriving association rules from the frequent item sets, the study identified the significance of strategically placing frequently purchased products to enhance revenue generation. In conclusion, the application of the Apriori algorithm and Market Basket Analysis in this case of a Kenyan supermarket has proven to be a valuable approach for uncovering consumer buying patterns, providing a competitive edge in the dynamic retail industry.
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SyahruRomadhon, Muhammad, and Achmad Kodar. "IMPLEMENTASI METODE MARKET BASKET ANALYSIS (MBA) MENGGUNAKAN ALGORITMA APRIORI DALAM TRANSAKSI PENJUALAN (STUDI KASUS: KAFE RUANG TEMU)." Jurnal SAINTEKOM 10, no. 2 (2020): 138. http://dx.doi.org/10.33020/saintekom.v10i2.137.
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Jakarta is one of the culinary attractions, many tourist attractions every year become creative in business. One of them is a cafe. Cafe Ruang Temu has sales transaction data but is not used to see associations between one product and another. In this case there needs to be a system for finding menu combinations by processing sales transactions. One of the data mining techniques is association rule or Market Basket Analysis (MBA) with apriori algorithm. Apriori algorithm aims to produce association rules to form menu combinations. The sales dataset for January 2019 to July 2019 is determined by the minimum support and minimum confidence values that have been set.
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Andini Mega Putri, Refina, Kurnia Paranita Kartika, and Filda Febrinita. "PENERAPAN METODE MARKET BASKET ANALISIS DALAM PENENTUAN PEMASARAN IKAN KOI (STUDI KASUS: SUMBER KOI BLITAR)." Jurnal Mnemonic 4, no. 2 (2021): 57–63. http://dx.doi.org/10.36040/mnemonic.v4i2.4161.
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Semakin berkembangnya teknologi internet, petani koi di Desa Sumber Blitar masih mempunyai suatu kendala dalam penjualan. Usaha koi “Sumber Koi” Blitar bergerak di bidang pembesaran bibit dan penjualan ikan koi. Penjualan sebelumnya dilakukan dengan cara membuka website sumber koi blitar di internet, instagram dan juga whatssap sehingga hanya customer tetap yang dapat mengakses. Petani di “Sumber Koi” membutuhkan suatu metode cepat untuk memperkirakan jumlah bibit, jenis bibit, dan rekapitulasi penjualan untuk mempermudah proses pemasaran ikan koi. Untuk mengatasi permasalahan ini digunakan metode (MBA) yang dapat menganalisis produk yang dibeli secara bersamaan, produk yang sering dibeli oleh pelanggan serta jumlah produk yang terbeli. Metode penelitian yang digunakan pada penelitian ini adalah metode prototype meliputi yang dimulai dari pengumpulan kebutuhan, membangun prototype, mengkodekan sistem, dan menguji sistem. Pada penelitian ini diperoleh hasil suatu aplikasi yang dapat digunakan untuk memperkirakan pemasaran ikan koi menggunakan metode Market Basket Analysis (MBA). Pengujian yang dilakukan meliputi, pengujian Black Box, pengujian ahli validator, serta pengujian pengguna. Dari pengujian Blacx Box diperoleh hasil keseluruhan fungsional aplikasi berfungsi dengan baik. Dari pengujian validasi yang dilakukan oleh 2 validator diperoleh porsentase kesesuaian hasil 77,5%. Hasil pengujian aplikasi oleh pengguna diperoleh porsentase kesesuaian hasil 89%
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Khasanah, Annisa Uswatun, and Muhammad Rafly Qowi Baihaqie. "Analysis of consumer characteristics on retail business with clustering analysis method and association rule for selling improvement strategy recommendations." OPSI 17, no. 1 (2024): 249. http://dx.doi.org/10.31315/opsi.v17i1.11411.
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In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.
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Nugraheni, Wahyu, and Adi Nugroho. "Penerapan Metode Market Basket Analysis (MBA) dengan Algoritma Apriori Untuk Menganalisis Pembelian Jajanan Khas Lebaran Pada Warung Sembako di Toko Win." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 7, no. 4 (2023): 639–41. http://dx.doi.org/10.35870/jtik.v7i4.1083.
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Market Basket Analysis merupakan strategi pemasaran untuk memenuhi produk yang akan dibeli secara bersamaan oleh konsumen yang yang sering digunakan dan paling bermanfaat untuk lingkungan marketing yang menerapkan pola “jika-maka". Bertujuan untuk mengidentifikasi pola beli konsumen yang dapat dijadikan acuan dalam menentukan penyusunan letak item dengan kombinasi barang yang sering dibeli dan saling berhubungan agar dapat meningkatkan penjualan dengan strategi pemasaran yang tepat. Penelitian dilakukan di Toko Win yang mana selama ini banyak data menumpuk yang hanya dijadikan arsip atau pembukuan sehingga untuk membantu meningkatkan penjualan dengan menjadikan data transaksi penjualan sebagai informasi baru dengan mengolah dengan menggunakan Metode Basket Analysis dan Algoritma Apriori menggunakan Rapid Miner Studio. Disini menggunakan 7 atribut data yaitu Astor, Kastangel, Keripik Bayam, Keripik Seblak, Keripik Usus, Monde, dan Nastar, dan 50 record data, dengan batas minimum support = 0.4 dan minimum confidence = 0.6 yang menghasilkan 14 rules. Dan pola kombinasi item set tertinggi yang diperoleh dalam penelitian ini adalah adalah [NASTAR, MONDE] => [KASTANGEL] menghasilkan confidence 85,7 %.
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Purnama, Dwi Adi. "Data Mining Menggunakan Association Rules-Market Basket Analysis untuk Peningkatan Kinerja Ritel Tradisional." STRING (Satuan Tulisan Riset dan Inovasi Teknologi) 9, no. 3 (2025): 354. https://doi.org/10.30998/string.v9i3.28707.
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<p><em>Traditional retail, as well as micro, small, and medium-sized firms, play an important part in the Indonesian economy. However, with the rise of progressive business competition, such as competition from modern retail, traditional retail requires a strategy to better its business and performance. The purpose of this study is to identify consumer behavior in traditional retail based on data mining using Association rules-market basket analysis (AR-MBA). Data were gathered by collecting 150 shopping transactions. Furthermore, the pre-processing stage involved data cleansing, transformation, and reduction. The study's findings revealed that several association rules were established and validated. Based on these findings, various insights were obtained, including the fact that department 3 (snacks) is the most purchased item and is associated with items in other departments; there are association rules between powdered drinks and snacks, candy and snacks, toiletries, snacks, instant noodles and snacks, cigarettes and flavored drinks, and mineral water and flavored drinks. The findings are used to improve the performance and to expand the retail industry. This study recommends product stock management by increasing the number of products that consumers frequently purchase, product marketing strategies such as discounts, product bundling, and other promotions, and layout proposals based on association rules.</em><em></em></p>
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Setiawan, Danang, Raka Shidqi Fadlika, and Qurtubi Qurtubi. "Designing Animal Market Layout by Considering Consumer Purchase Behaviors." Spektrum Industri 22, no. 1 (2024): 51–59. http://dx.doi.org/10.12928/si.v22i1.164.
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Sales transaction data contains rich information and can support company competitiveness. However, this transaction data is initially unstructured and needs to be processed into insight for the company's decision-making. Market Basket Analysis (MBA) is a data mining technique that can be used to study consumer purchasing patterns. This paper presents a case study on using an MBA to obtain consumer buying behaviors where the result of the MBA is then used to design a proposed layout. The animal market governed by Yogyakarta Province, known as Pasty Market, was used as a case study. Pasty Market is an animal trading center with around 30,000 square meters area and 255 sellers that sell various kinds of animals such as songbirds, dove birds, rabbits, cats, dogs, iguanas, turtles, ornamental chickens, and ornamental fish as well as animal food and cage. With this enormous area and number of merchants, the layout of Pasty Market becomes crucial in customer satisfaction. Association rules result in four priority levels in proposed layout planning, where these rules are used to determine the proximity among items in the proposed layout. These four levels of priority, ordered by the confidence value, are (1) “songbirds” and “bird food” (confidence value 91%), (2) “ornamental fish” and “turtles” (confidence value 80-90%), (3) “birdcages” and “songbirds” (confidence value 70-80%), and (4) “cats” and “dog” as well as “birdcages” and “birds” (confidence value 50-70%). Association rules were then used as the basis for determining the proximity value between merchants, where the proximity rules were then used for designing a proposed layout.
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Rizki, Muhammad, Desi Devrika, Isnaini Hadiyul Umam, and Fitriani Surayya Lubis. "Aplikasi Data Mining dalam penentuan layout swalayan dengan menggunakan metode MBA." Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri 5, no. 2 (2020): 130. http://dx.doi.org/10.24014/jti.v5i2.8958.
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Data mining merupakan salah satu cara untuk mendapatkan informasi yang tersimpan pada dabased yang berjumlah besar. Data transaksi penjualan pada sebuah swalayan sering kali hanya digunakan sebagai laporan penjualan saja. Dalam kenyataannya, data tersebut dapat memberikan informasi yang lebioh dari sekedar laporan penjualan saja. Salah satu informasi yang dapat kita ambil dari data transaksi penjualan adalah hubungan antar item. Kita dapat mengetahui kelompok item yang cenderung dibeli bersamaan oleh pelanggan dalam satu transaksi pembelian.. Market Basket Analysis (MBA) merupakan salah satu metode untuk menentukan kelompok item yang cenderung dibeli oleh pelanggan dalam satu waktu atau dalam satu transaksi pembelian. Informasi keterkaitan antar kelompok item tersebut dapat kita jadikan sebagai referensi untuk menentukan layout, dimana item yang sering dibeli bersamaan kita dekatkan dalam penataan layoutnya sehingga pelanggan tidak perlu lagi susah payah untuk mencari item tersebut. Berdasarkan studi kasus awal pada salah satu swalayan yang berada di Pekanbaru, penataan layout per clusternya dilakukan secara acak, sehingga pelanggan kesulitan untuk mencari item-item yang biasanya dibeli dalam satu kali transaksi. Pemilik swalayan menginginkna penataan layout ulang mengikuti pola pembelian pelanggan. Pettern growth merupakan salah satu Teknik dari MBA, dimana hasil analisis dapat diketahui kelompok item yang memiliki kecendrungan untuk dibeli bersamaan oleh pelanggan. Kata Kunci: Data mining, MBA, Association rule, pattern growth, layout Data mining merupakan salah satu cara untuk mendapatkan informasi yang tersimpan pada dabased yang berjumlah besar. Data transaksi penjualan pada sebuah swalayan sering kali hanya digunakan sebagai laporan penjualan saja. Dalam kenyataannya, data tersebut dapat memberikan informasi yang lebioh dari sekedar laporan penjualan saja. Salah satu informasi yang dapat kita ambil dari data transaksi penjualan adalah hubungan antar item. Kita dapat mengetahui kelompok item yang cenderung dibeli bersamaan oleh pelanggan dalam satu transaksi pembelian.. Market Basket Analysis (MBA) merupakan salah satu metode untuk menentukan kelompok item yang cenderung dibeli oleh pelanggan dalam satu waktu atau dalam satu transaksi pembelian. Informasi keterkaitan antar kelompok item tersebut dapat kita jadikan sebagai referensi untuk menentukan layout, dimana item yang sering dibeli bersamaan kita dekatkan dalam penataan layoutnya sehingga pelanggan tidak perlu lagi susah payah untuk mencari item tersebut. Berdasarkan studi kasus awal pada salah satu swalayan yang berada di Pekanbaru, penataan layout per clusternya dilakukan secara acak, sehingga pelanggan kesulitan untuk mencari item-item yang biasanya dibeli dalam satu kali transaksi. Pemilik swalayan menginginkna penataan layout ulang mengikuti pola pembelian pelanggan. Pettern growth merupakan salah satu Teknik dari MBA, dimana hasil analisis dapat diketahui kelompok item yang memiliki kecendrungan untuk dibeli bersamaan oleh pelanggan. Kata Kunci: Data mining, MBA, Association rule, pattern growth, layout
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Kurniawan, Albert. "Sequential Pattern Mining Data Transaksi Penjualan Supermarket menggunakan Algoritme Generalized Sequential Pattern." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 1 (2022): 126–36. http://dx.doi.org/10.35957/jatisi.v9i1.1460.
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Data transaksi penjualan supermarket online merupakan sequence dataset. Data ini menyimpan data transaksi pembelian yang dilakukan oleh pelanggan, sehingga dapat dianalisis menggunakan pendekatan Market Basket Analysis (MBA). Masalah yang sering dialami oleh pihak supermarket adalah sulitnya menerapkan strategi penjualan yang akurat kepada para konsumen. Berdasarkan masalah tersebut, pada penelitian ini akan dilakukan analisis terhadap dataset supermarket West Superstore berdasarkan pendekatan MBA. Algoritme yang digunakan adalah algoritme Generalized Sequential Pattern (GSP), di mana algoritme ini dapat membangkitkan frequentitem dan sequencepattern, sehingga aturan yang dihasilkan dapat lebih akurat. Algoritme GSP pada penelitian ini diimplementasikan dalam bahasa pemrograman Python. Hasil pengujian menunjukkan bahwa keluaran dari Python sudah sesuai dengan keluaran dari perhitungan algoritme GSP. Waktu komputasi yang diperlukan untuk pembangkitan aturan pada algoritme GSP juga bergantung pada jumlah record yang ada. Semakin banyak jumlah transaksi penjualan yang akan dianalisis, maka waktu komputasinya juga semakin lama. Analisis yang dilakukan pada dataset penjualan di West Superstore menghasilkan 391 aturan, di mana aturan tersebut dapat dimanfaatkan oleh pihak supermarket untuk penerapan strategi penjualan.
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Febryantahanuji and Nanik Lestari. "SIMPLE ADDITIVE WEIGHTING UNTUK PENENTUAN PEMBERIAN INSENTIF KEPADA KARYAWAN TERBAIK DI PT. CAMPUS DATA MEDIA BERBASIS WEB MVC." Jurnal Akuntansi dan Bisnis 1, no. 1 (2021): 28–38. http://dx.doi.org/10.51903/jiab.v1i1.8.
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This study aims to design the build of auto parts on the CV. Auto Mobilindo which aims to meet customer demand for a product in the form of the amount of inventory stored to anticipate customer demand and determine the minimum stock that must be met. The use of the market basket analysis (MBA) method is to determine what products are most often purchased by consumers. Market basket analysis is to analyze consumers by finding associations between different products that consumers place in their shopping baskets. This study selects objects in the automotive sector, CV Auto Mobilindo Batam, which is located at Komplek Nagoya 2000, JL. Comp. Business Center. Steel Hole. Batam City, Kep. Riau. Research results that can be used as decision making in meeting customer needs so that there is no excess or stock of goods in the warehouse.
 This study aims to apply the Simple Additive Weighting (SAW) method for selecting the best employees who are entitled to receive incentives. PT. Campus Data Media in selecting the best employees still uses Microsoft Excel in evaluating employee performance, so it requires a long process to report and recommend the best employees for each branch to HRD. In addition, the parameters used for employee assessment can be different for each branch. From the existing problems, a web-based decision support system MVC (Model View Controller) was made using the Simple Additive Weighting (SAW) method. The system development method uses the Research and Development (R&D) method with a sample of 64 employees to be assessed. The results of this study are expected to assist in determining the best employees to get incentives at PT. Media Data Campus.
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Ibrahim, Faisal, Bagas Swardhana Putra, Fariza Halidatsani Azhra, and Najib Fadhlurrohman. "Analysis of marketing strategy at setia stores using ahp, clustering, and ar-mba method." International Journal of Industrial Optimization 2, no. 2 (2021): 125. http://dx.doi.org/10.12928/ijio.v2i2.4369.
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A company can survive and thrive when the strategies and processes applied in its business are correct. One of the processes in determining strategy in decision making. The owner of Setia Store has difficulty in choosing a marketing strategy. The product layout shows this in the Setia Store, which confuses customers. Setia Store also rarely offers a promotion, making it difficult to compete with competitors. This study aims to help Setia Store increase sales by determining the right marketing strategy. To determine the right marketing strategy, there are three methods developed. First of all, the analytical hierarchy process (AHP) is employed to find the customer priorities. Then, clustering is proposed to find potential marketing targets that have similar characteristics from the results of the AHP method. Third, association rule-market basket analysis (AR-MBA) is developed to find the best rules for product marketing strategy. The first method shows that the housewives (EV=0.6270) are Setia Store's priority customers from the three methods. Second, cluster 3 (which has three characteristics in common) is a very potential target market. Third, the best rule is to buy products from departments 2 and 3 (Confidence 60%, Support 12%). From these results, the right marketing strategy is to create a buy 1 get 1 promo banner or label for products that are rarely purchased, such as household appliances. Then, create a catalog by bringing together frequently purchased products such as spices and food ingredients. Finally, improve the layout by bringing the departmental shelves closer to frequently purchased products.
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Mustofa, Irfan, Agus Hindarto Wibowo, Kartinasari Ayuhikmatin Sekarjati, Nafis Sinta Makhulina, and Rio Dewangga. "Penerapan Association Rule-Market Basket Analysis (AR-MBA) Dalam Menentukan Strategi Product Bundling: Studi Kasus Pada Minimarket AKPRIND MART." Jurnal Teknik Industri Terintegrasi 7, no. 1 (2024): 379–86. http://dx.doi.org/10.31004/jutin.v7i1.24873.
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Data has become the most valuable component to be processed in order to provide usable information in the modern world of rapidly developing technology. When it comes to more in-depth or clear data analysis, technology is quite useful. Real governmental, social, and commercial operations use this technology; in the case of business, this is demonstrated by the quantity of minimarkets operating throughout Indonesia. As a result, it greatly increases commercial competition. Consequently, in order to compete, a study using the available data must be conducted. The Association Rule-Market Basket Analysis method was employed in this study to ascertain the shopping interests of the participants. According to the study's findings, two rules 60% (washing equipment and foodstuffs) and 62% (medicine and bottled drinks) showed the greatest confidence values. Based on these findings, the minimarket can decide what has to be done in terms of Product Bundling, setting up the layout and other tasks
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Nugroho, Dony Satriyo, Nur Islahudin, Vivi Normasari, and Salsabiila Zaiima Al Hakiim. "PENERAPAN MARKET BASKET ANALYSIS (MBA) DATA MINING MENGGUNAKAN METODE ASOSIASI APPRIORI DAN FP-GROWTH PADA WAN CAFFEINE ADDICT YOGYAKARTA." JISI: Jurnal Integrasi Sistem Industri 11, no. 1 (2024): 121. http://dx.doi.org/10.24853/jisi.11.1.121-134.
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Banyaknya coffe shop baru yang bermunculan setiap tahunnya, membuat para pelaku usaha coffe shop harus berinovasi dan menyediakan produk yang sesuai dengan preferensi konsumen, dimana salah satu preferensi konsumen adalah adanya paket produk serta diskon terhadap produk tertentu. Dengan penerapan teknologi informasi memiliki peran penting di dalamnya pemilik bisnis coffe shop, yaitu memudahkan pemilik bisnis dalam melakukan rekapitulasi dan pengolahan data yang akan membantu pemilik bisnis kafe mengambil keputusan yang berkaitan dengan peningkatan bisnisnya. salah satu metode yang digunakan untuk pengambilan keputusan adalah data mining Asosiasi. Asosiasi dapat digunakan untuk mencari keterkaitan antar produk sehingga mampu meningkatkan minat beli konsumen dengan cara bundling dan penetapan diskon terhadap pembelian produk-produk yang saling terkait. Wan Caffeine Addict yang merupakan sebuah coffe shop di Yogyakarta memiliki 684 transaksi dan 50 jenis produk terjual pada bulan desember 2022. Menggunakan metode asosiasi apriori dan FP-growth dengan tingkat support 3% dan confidence 10% diperoleh produk yang paling berkaitan adalah produk Flavored Latte Hazelnut Large dengan produk Wannabe dengan tingkat confidence 0.7 atau 70% dan lift ratio 3.84.
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M, Vinayababu, and M. Sreedevi. "Clustering of Non-Associated Item Sets for Analyzing Show Room Sales Dataset." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 461–68. http://dx.doi.org/10.17762/ijritcc.v11i11s.8175.
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Market basket analysis (MBA) is a well-liked method for identifying relationships between products that people purchase in a database. It is predicated on association rule mining (ARM), a data mining technique that pulls valuable data from huge databases. Due to consumers using internet applications for online shopping and insurance, an ever-increasing amount of data is generated online. It produces large amounts and, if mined effectively, will greatly benefit society as a whole as well as individuals. So, numerous data science and machine learning-related techniques have been created to gradually unlock the potential. The Clustering of Non-Associated Item Sets (CNAIS) of the Sales dataset used in the Showroom for choosing customers for benefits and web application design is discussed in this study. The CNAIS algorithm implementation process and dataset for this study are discussed.
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Aisyah, Aisyah, Leon Andretti Abdillah, Suyanto Suyanto, and Deni Erlansyah. "WEB E-COMMERCE RKU COMPUTER PALEMBANG." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 10, no. 1 (2025): 611–21. https://doi.org/10.29100/jipi.v10i1.6734.
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In the increasingly developing digital era, the role of the internet and information technology is increasingly important in supporting various aspects of business, including marketing and product sales. Especially the presence of a website has become an important need for a business that wants to compete in this increasingly competitive market, to expand market reach and increase their brand visibility. RKU Computer Palembang is a business engaged in computer sales and services. Currently, the RKU Computer sales system is still manual, namely prospective buyers come directly to the RKU Computer store to buy its products. To overcome this problem, the author tries to provide a solution to RKU Computer, namely by building an e-commerce website as an online sales media, to help RKU Computer sell its products, and to help RKU Computer compete with other businesses that have implemented an online sales system. The research methodology used in this study is a software development method using extreme programming consisting of 4 stages, namely planning, design, coding, and testing, and a process method using market basket analysis (MBA). The desired result of this study is to create an RKU Computer Palembang e-commerce website as an online sales media
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Jayadi, Jayadi, and Andi Patombongi. "IMPLEMENTASI APLIKASI DATA MINING PADA APOTEK KIMIA FARMA BAHTERAMAS MENGGUNAKAN ALGORITMA APRIORI." Simtek : jurnal sistem informasi dan teknik komputer 2, no. 1 (2017): 87–95. http://dx.doi.org/10.51876/simtek.v2i1.37.
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Apotek Kimia Farma juga sudah menerapkan aplikasi dalam sistem penjualannya, seiring dengan berjalannya waktu data yang dihasilkan aplikasi penjualan pada apotek semakin melimpah dan membuat tumpukan data yang tidak bermanfaat, Sehingga dibutuhkan aplikasi yang dapat mempermudah pihak apotek dalam menganalisis data tranksaksi tersebut. Metode yang digunakan dalam pembuatan aplikasi Data mining yaitu metode MBA (market basket analysis ), dengan bantuan Algoritma Apriori. Proses yang dilakukan dalam implementasi Algoritma Apriori yaitu dengan cara mengambil data history penjualan dari Apotek Kimia Farma, kemudian menghitung nilai persentase tiap barang yang dibeli dalam database (support ), selanjutnya memangkas data yang tidak memenuhi syarat dari nilai minimum support, setelah semua pola frekuensi tinggi ditemukan, barulah dicari aturan asosisasi yang memenuhi syarat minimum confidence. Hasil dari aplikasi yang menggunakan teknik Data Minig dan Algoritma Apriori ini yaitu mampu menampilkan pola pembeliaan konsumen dengan menganalisa data transaksi yang ada, dan membantu pihak apotek untuk mengetahui pola konsumsi konsumen sehingga dapat meningkatkan strategi penjualan.
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Turgay, Safiye, Metehan Han, Suat Erdoğan, Esma Sedef Kara, and Recep Yilmaz. "Evaluating the Predictive Modeling Performance of Kernel Trick SVM, Market Basket Analysis and Naive Bayes in Terms of Efficiency." WSEAS TRANSACTIONS ON COMPUTERS 23 (April 9, 2024): 56–66. http://dx.doi.org/10.37394/23205.2024.23.6.
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Among many corresponding matters in predictive modeling, the efficiency and effectiveness of the several approaches are the most significant. This study delves into a comprehensive comparative analysis of three distinct methodologies: Finally, Kernel Trick Support Vector Machines (SVM), market basket analysis (MBA), and naive Bayes classifiers invoked. The research we aim at clears the advantages and benefits of these approaches in terms of providing the correct information, their accuracy, the complexity of their computation, and how much they are applicable in different domains. Kernel function SVMs that are acknowledged for their ability to tackle the problems of non-linear data transfer to a higher dimensional space, the essence of which is what to expect from them in complex classification are probed. The feature of their machine-based learning relied on making exact confusing decision boundaries detailed, with an analysis of different kernel functions that more the functionality. The performance of the Market Basket Analysis, a sophisticated tool that exposes the relationship between the provided data in transactions, helped me to discover a way of forecasting customer behavior. The technique enables paints suitable recommendation systems and leaders to make strategic business decisions using the purchasing habits it uncovers. The research owes its effectiveness to processing large volumes of data, looking for meaningful patterns, and issuing beneficial recommendations. Along with that, an attempt to understand a Bayes classifier of naive kind will be made, which belongs to a class of probabilistic models that are used largely because of their simplicity and efficiency. The author outlines the advantages and drawbacks of its assumption in terms of the attribute independence concept when putting it to use in different classifiers. The research scrutinizes their effectiveness in text categorization and image recognition as well as their ability to adapt to different tasks. In this way, the investigation aims to find out how to make the application more appropriate for various uses. The study contributes value to the competencies of readers who will be well informed about the accuracy, efficiency, and the type of data, domain, or problem for which a model is suitable for the decision on a particular model choice.