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1
Hari Purnomo, Dian Eko. "Perancangan Model Matematis Untuk Penentuan Jumlah Produksi di PT. XZY." Science Tech: Jurnal Ilmu Pengetahuan dan Teknologi 3, no. 1 (2017): 21–28. http://dx.doi.org/10.30738/jst.v3i1.1137.
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Determining the amount of production in an industry is a very important thing before starting the production process. Therefore we need a mathematical model that can help industry players in performing production planning. Mathematical model is a formula that helps industry players in PT. XYZ in solving semi-structured problems in the form of production planning. Many techniques are used to create a mathematical model, one of them with Fuzzy Logic. Fuzzy logic is one of the problem solving techniques where membership degrees are usually represented by values between 0 and 1, so they can be more balanced. One of the fuzzy methods that can be used in solving the problem is the Fuzzy Tsukamoto Method which applies weighted average to calculate the amount of production at PT. XYZ as the end result. The mathematical model of determining the amount of production using the Fuzzy Tsukamoto Method is able to produce a more optimal and balanced quantity of production compared to the calculation without using the method.
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Ardiansyah, Irvan, and Dewi Handayani. "E-Transaction Point of Sales (POS) with Fuzzy Tsukamoto Algorithm at PT. Samihasa Kita." JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) 6, no. 2 (2023): 67–76. http://dx.doi.org/10.26905/jeemecs.v6i2.7407.
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PT. Samihasa Kita is a glass product distributor company that was founded in 1989, which is located in the city of Semarang, Central Java, with sales area coverage in Central Java, D.I Yogyakarta, Jakarta, and Kalimantan. So far, PT Samihasa Kita has an erratic amount of demand for goods, as a result, sometimes the number of goods produced for sale with the goods purchased by consumers is not balanced. This resulted in PT Samihasa Kita not getting the maximum profit. Based on this problem, Fuzzy Tsukamoto logic was chosen to determine the optimal daily production amount. By using fuzzy logic can be determined the relative size of the production of goods. The Fuzzy Tsukamoto method can also be used to forecast sales in the coming month based on the amount of inventory. Thus the amount of production and demand for goods PT. Samihasa Kita is predictable and balances transactions. E-transaction Point Of Sales (POS) with the Fuzzy Tsukamoto algorithm at PT. Samihasa aims to be able to predict demand and procurement of goods that will have an impact on optimizing revenue.
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Ardiansyah, Irvan, and Dewi Handayani Untari Ningsih. "E-Transaction Point Of Sales (Pos) With Fuzzy Tsukamoto Algorithm At Pt. Samihasa Kita." JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) 7, no. 1 (2023): 1–10. http://dx.doi.org/10.21070/jeeeu.v7i1.1636.
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PT. Samihasa Kita is a glass product distributor company that was founded in 1989, which is located in the city of Semarang, Central Java, with sales area coverage in Central Java, D.I Yogyakarta, Jakarta, and Kalimantan. So far, PT Samihasa Kita has an erratic amount of demand for goods, as a result, sometimes the number of goods produced for sale with the goods purchased by consumers is not balanced. This resulted in PT Samihasa Kita not getting the maximum profit. Based on this problem, Fuzzy Tsukamoto logic was chosen to determine the optimal daily production amount. By using fuzzy logic can be determined the relative size of the production of goods. The Fuzzy Tsukamoto method can also be used to forecast sales in the coming month based on the amount of inventory. Thus the amount of production and demand for goods PT. Samihasa Kita is predictable and balances transactions. This research aims to be able to predict demand and procurement of goods that will have an impact on optimizing revenue at PT. Samihasa Kita.
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Tundo, Tundo, and Fajar Mahardika. "Fuzzy Inference System Tsukamoto–Decision Tree C 4.5 in Predicting the Amount of Roof Tile Production in Kebumen." JTAM (Jurnal Teori dan Aplikasi Matematika) 7, no. 2 (2023): 533. http://dx.doi.org/10.31764/jtam.v7i2.13034.
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Tile is a product that is in great demand by many people. This has become a trigger for producers to improve their management. The company's tile production management is still experiencing problems, namely frequent miscalculations in determining the agreement that must be issued in making tile production from customer requests. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a big profit. In this study, to obtain a prediction of the amount of tile production, computerized calculations were carried out using the Tsukamoto fuzzy logic method. This method uses the concept of rules from the C 4.5 decision tree in the building to make it easier to determine the rules that are built without having to consult an expert because C 4.5 will study existing datasets to serve as a reference in forming these rules according to conditions that often occur. The modeling results produce relevant rules after being compared with the actual results. The results of the comparison of predictions with actual production have an error percentage of 29.34%, with a truth of 70.66% (based on the calculation of the Average Forecasting Error Rate (AFER)). Therefore when implemented in the Tsukamoto Fuzzy Inference System it can produce predictions of tile production that are quite optimum. It is said to be quite optimum because all customer requests are met, either generated by the production prediction itself or the prediction results are added up with inventory data, and all predictions are close to actual production.
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Yudi Abdul Syawari, Muhammad, and Hartono. "Sistem Inferensi Fuzzy Tsukamoto Untuk Menentukan Tingkat Kualitas Air Pada Kolam Budidaya Ikan Lele." Sienna 5, no. 1 (2024): 95–109. https://doi.org/10.47637/sienna.v5i1.1358.
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This study explores the position of catfish as a major aquaculture commodity in Indonesia. In 2013, Indonesia dominated global catfish production with a market share of 75.6%. Despite the higher growth rate of catfish farming compared to other commodities, production did not meet the set targets. The government is working to strengthen national catfish production and enhance its role as a leading commodity in Indonesia. The advantages of catfish lie in its content of leucine and lysine, which are important for children's growth and nitrogen balance. Environmental factors such as freshwater pH (6.5-8.6), optimal temperature (23-30°C), oxygen levels (2-5 mg/L), and ammonia levels (5-7 mg/L) affect the water quality in catfish farming ponds. Natural and human factors such as extreme weather and overfeeding can impact the living conditions and growth of catfish. This study aims to investigate the relationship between pond water quality and catfish growth. The methods used include confusion matrix and Tsukamoto fuzzy logic to determine the accuracy of the water quality assessment system. The main objective of this research is to provide solutions and benefits for the community or the field of study.
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Hari Purnomo, Dian Eko, Yogi Akbar Sunardiansyah, and Amelia Nur Fariza. "PENERAPAN METODE FUZZY TSUKAMOTO DALAM MEMBANTU PERENCANAAN PERSEDIAAN BAHAN BAKU KAYU PADA INDUSTRI FURNITUR." Industry Xplore 5, no. 2 (2020): 59–68. http://dx.doi.org/10.36805/teknikindustri.v5i2.1125.
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The furniture industry is experiencing the problem of uncertainty in determining the optimal amount of wood raw material inventory. Fuzzy logic is one of the methods used to analyze systems that contain uncertainty. This study discusses the application of fuzzy logic in solving the problem of supplying wood raw materials in the furniture industry with the Fuzzy-Mamdani approach. The data used in this study are data on the entry, distribution, and supply of wood raw materials from January to December 2018. The design of the system to obtain output is carried out in the following stages: (a) Formation of fuzzy sets, (b) Application of functions implications, (c) Composition of rules, (d) Affirmation (defuzzification). Solving the problem using the Fuzzy Tsukamoto method is carried out by the Tsukamoto method with the help of Visual Basic software so that the desired results will be obtained on the output variable. From the results of calculations using the Fuzzy Tsukamoto method, it can be analyzed that the comparison between the realized wood raw material inventory with the Fuzzy Tsukamoto approach looks different and the results from the Fuzzy Tsukamoto approach are more optimal.
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Nasution, Jabal Yasir, and Granita. "Implementation of fuzzy logic using the tsukamoto method in forecasting the amount of bolu cake production." Journal Focus Action of Research Mathematic (Factor M) 7, no. 1 (2024): 123–38. http://dx.doi.org/10.30762/f_m.v7i1.2516.
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Rumah produksi berkah bolu merupakan salah satu UMKM yang memproduksi kue bolu di Kota Pekanbaru. Pemilik usaha tersebut kesulitan dalam menentukan jumlah produksi kue karena hanya berdasarkan pada jumlah permintaan yang ada. Berdasarkan permasalahan tersebut, maka dipilihlah metode fuzzy Tsukamoto karena menggunakan penalaran monoton dalam setiap aturannya. Terdapat 4 tahapan yang dipakai dalam perhitungan metode Tsukamoto yaitu fuzzifikasi, inferensi, komposisi/Agregasi dan defuzzyfikasi. Hasil MAPE yang diperoleh dengan metode fuzzy Tsakomoto adalah 6,91% dan tingkat keakuratan sebesar 93,01%, yang memiliki arti bahwa metode fuzzy Tsukamoto sangat baik dalam memprediksi jumlah produksi, sehingga dapat digunakan sebagai sistem untuk mendukung keputusan dalam penentuan jumlah produksi kue bolu di rumah produksi Berkah Bolu. The Berkah Bolu Production House is one of the Small and Medium Enterprise (SME) that produces bolu cakes in Pekanbaru City. The business owner has difficulty in determining the amount of cake production because it is only based on the number of existing requests. Based on these problems, the Tsukamoto fuzzy method was chosen because it uses monotonous reasoning in each rule. There are 4 stages used in the calculation of the Tsukamoto method, namely fuzzification, inference, composition or aggregation, and defuzzification. The MAPE result obtained by the fuzzy Tsukomoto method is 6.91% and the accuracy level is 93.01%, which means that the fuzzy Tsukamoto method is very good at predicting the amount of production, so it can be used as a decision support system in determining the amount of bolu cake production in the Berkah Bolu Production House.
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Farismana, Riyan, Debi Nabilah Sholihah, Dian Pramadhana, and Sonty Lena. "IMPLEMENTASI FUZZY TSUKAMOTO DALAM SISTEM PREDIKSI PANEN PADI DI KABUPATEN INDRAMAYU." Jurnal Teknoif Teknik Informatika Institut Teknologi Padang 12, no. 2 (2024): 100–110. http://dx.doi.org/10.21063/jtif.2024.v12.2.100-110.
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Strategic efforts to maintain rice production, which is the staple food of the Indonesian people, must continue to be carried out, one of which is maintaining production in rice-producing areas such as Indramayu Regency is a step that must be prioritized. Knowing the prediction of the upcoming rice harvest is the right effort to make. Fuzzy Tsukamoto logic is a method that can be used as a calculation to make predictions, the selection of fuzzy Tsukamoto is due to its ability to predict with factors that have uncertainty such as rainfall, in addition, land area and harvest yields can also be used as variables to obtain predictions through fuzzy Tsukamoto. This study was conducted to implement fuzzy Tsukamoto for predicting rice harvest yields, where the results of the fuzzy Tsukamoto logic process that have been carried out produce six rules at the inference stage in the category of increasing or decreasing harvests, which are used as the basis for the defuzzification process to find the output value of the harvest. The calculation results are then applied in a website-based system using the Laravel framework and MySql DBMS so that they can be used to predict rice harvest yields. Based on the prediction process carried out through a system based on the fuzzy Tsukamoto method, out of 31 sub-districts, 16 sub-districts have the potential to experience a decline, and 15 sub-districts have experienced an increase in rice harvest production in Indramayu Regency.
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Norvindes Dellas, Dominggus, Ika Purnamasari, and Nanda Arista Rizki. "Fuzzy Inference System Using Tsukamoto Method For Making Decision of Production (Case Study: PT Waru Kaltim Plantation)." METIK JURNAL 4, no. 2 (2020): 76–82. http://dx.doi.org/10.47002/metik.v4i2.171.
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The decision-making process using a fuzzy inference system (FIS) logic can use one of the methods called the Tsukamoto method. The process carried out in this method is the same as the fuzzy method in general, namely the formation of fuzzy sets, the fuzzification process, defuzzification, and measuring the accuracy of the result. The purpose of this study was to apply the Tsukamoto method to predict the yield of oil palm production at PT. Waru Kaltim Plantation. Based on the analysis using the Tsukamoto method, 36 fuzzy rules were obtained for each data from February 2013 to December 2015. The prediction results of palm oil production in 2013 did not change, except for May and August. In February, March, June, and August 2014 the level of production is constant, and almost throughout 2015, there was constant. The predicted MAPE for oil palm production was 31,522%, or in the fairly good category.
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SUARDIKA, KOMANG WAHYUDI, G. K. GANDHIADI, and LUH PUTU IDA HARINI. "PERBANDINGAN METODE TSUKAMOTO, METODE MAMDANI DAN METODE SUGENO UNTUK MENENTUKAN PRODUKSI DUPA (Studi Kasus : CV. Dewi Bulan)." E-Jurnal Matematika 7, no. 2 (2018): 180. http://dx.doi.org/10.24843/mtk.2018.v07.i02.p201.
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This research aims at knowing the comparison among Tsukamoto method, Mamdani method, and Sugeno method in deciding the production of incense at CV. Dewi Bulan. The research discussed about Tsukamoto method, Mamdani method and Sugeno method which consisted of four step, they are: fuzzyfication, forming a fuzzy rules, fuzzy logic analysis, and defuzzyfication. In conclusion, Sugeno method was found to be the best to be used in deciding the number of incense sticks production, comparing with the others. Sugeno method has probability of error value about 1,314%.
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Tundo, Tundo, and Enny Itje Sela. "Application of The Fuzzy Inference System Method to Predict The Number of Weaving Fabric Production." IJID (International Journal on Informatics for Development) 7, no. 1 (2018): 19. http://dx.doi.org/10.14421/ijid.2018.07105.
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In this study discusses the application of fuzzy logic in solving production problems using the Tsukamoto method and the Sugeno method. The problem that is solved is how to determine the production of woven fabric when using three variables as input data, namely: stock, demand and inventory of production costs. The first step is to solve the problem of woven fabric production using the Tsukamoto method which is to determine the input variables and output variables which are firm sets, the second step is to change the input variable into a fuzzy set with the fuzzification process, then the third step is processing the fuzzy set data with the maximum method. And the last or fourth step is to change the output into a firm set with the defuzzification process with a weighted average method, so that the desired results will be obtained in the output variable. The solution to the production problem using the Sugeno method is almost the same as using the Tsukamoto method, it's just that the system output is not a fuzzy set, but rather a constant or a linear equation. The difference between the Tsukamoto Method and the Sugeno Method is in consequence. The Sugeno method uses constants or mathematical functions of the input variables.
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Darmawan, Yogi Dwiki, Deden Hardan Gutama, Dita Danianti, and Wahit Desta Prastowo. "Sistem Monitoring Tumbuh Kembang Balita (Studi Kasus : Puskesmas Mertoyudan II)." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 7, no. 6 (2024): 1722–29. https://doi.org/10.32672/jnkti.v7i6.8116.
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Abstrak - Peran orang tua dalam perawatan sehari-hari balita, terutama dalam hal pemantauan dan pemberian makanan, sangat krusial. Namun, studi kasus di Puskesmas Mertoyudan II menunjukkan adanya kendala dalam pemantauan tumbuh kembang balita akibat keterbatasan waktu orang tua dan kurangnya informasi mengenai pentingnya pemantauan ini. Akibatnya, pemantauan tumbuh kembang sering kali tidak optimal, yang meningkatkan risiko masalah serius seperti stunting, yaitu kondisi gagal tumbuh akibat kurangnya asupan gizi yang memadai. Penelitian ini bertujuan mengi mplementasikan metode Fuzzy Logic Tsukamoto pada Sistem Monitoring dan Rekomendasi Makanan Sehat Bagi Tumbuh Kembang Balita untuk membantu mempermudah proses pemantauan. Fitur-fitur yang dihadirkan meliputi rekam medis, rekomendasi makanan berbasis Fuzzy Logic Tsukamoto, jadwal imunisasi, dan riwayat rekomendasi makanan, yang diharapkan dapat membantu orang tua dan petugas kesehatan dalam melakukan pemantauan yang lebih efektif. Hasil penelitian menunjukkan bahwa sistem ini berhasil mempermudah proses pengawasan tumbuh kembang balita serta memberikan rekomendasi makanan yang tepat, sehingga dapat mencegah terjadinya stunting. Sistem juga memudahkan akses informasi bagi semua pihak yang berkepentingan, mengurangi risiko kesalahan dalam pemilihan nutrisi, dan meningkatkan efektivitas pemantauan kesehatan balita di lingkungan puskesmas.Kata kunci: Sistem Monitoring, Tumbuh Kembang Balita, Fuzzy Logic Tsukamoto, Stunting, Puskesmas, Peran Orang Tua. Abstract - The role of parents in the daily care of toddlers, especially in terms of monitoring and providing food, is very crucial. However, a case study at the Mertoyudan II Community Health Center shows that there are obstacles in monitoring the growth and development of toddlers due to parents' limited time and lack of information regarding the importance of this monitoring. As a result, growth and development monitoring is often not optimal, which increases the risk of serious problems such as stunting, which is a condition of failure to grow due to a lack of adequate nutritional intake. This research aims to implement Tsukamoto's Fuzzy Logic method in the Healthy Food Monitoring and Recommendation System for Toddler Growth and Development to help simplify the monitoring process. The features presented include medical records, Fuzzy Logic Tsukamoto-based food recommendations, immunization schedules, and history of food recommendations, which are expected to help parents and health workers carry out more effective monitoring. The research results show that this system has succeeded in simplifying the process of monitoring the growth and development of toddlers and providing appropriate food recommendations, thereby preventing stunting. The system also makes it easier to access information for all interested parties, reduces the risk of errors in nutritional selection, and increases the effectiveness of monitoring children's health in the health center environment.Keywords: Monitoring System, Toddler Growth and Development, Tsukamoto Fuzzy Logic, Stunting, Health Center, Parents' Role.
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Adrial, Rico. "Analisis Perbandingan Kalkulasi Manual Fuzzy Logic Metode Mamdani Dan Tsukamoto Pada Penentuan Tipe Diabetes Melitus." Journal of Education Informatic Technology and Science 2, no. 3 (2020): 12–23. http://dx.doi.org/10.37859/jeits.v2i3.1922.
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Terdapat beberapa metode pada Fuzzy Logic diantaranya Mamdani dan Tsukamoto. Kedua metode ini memiliki hubungan yang menarik dimana output yang dihasilkan ditampilkan dalam bentuk grafik akan tetapi dengan kalkulasi yang berbeda. Artikel ini akan menjabarkan bagaimana perbandingan antar kedua metode tersebut dalam kasus penentuan tipe diabetes melitus. Diabetes itu sendiri terdiri atas dua tipe, dimana perbedaan antara kedua tipe tersebut sering terabaikan. Penggunaan fuzzy logic menggunakan metode Mamdani lebih optimal dalam kasus penentuan tipe diabetes dibandingkan dengan metode Tsukamoto. Hasil kalkulasi manual menunjukan bahwa metode Mamdani lebih mendekati keadaan yang sebenarnya. Berdasarkan kalkulasi yang telah dilakukan perbedaan hasil keluaran yang signifikan ini disebabkan oleh beberapa hal, yaitu Output yang ada pada Tsukamoto hanya berkisar pada nilai 45 sampai dengan 55. Hal ini membuat jika sedikit saja kalkulasi salah maka hasil yang diperoleh akan berdampak besar. Sedangkan hal ini tidak akan berlaku pada metode Mamdani.
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Erwin, Angelika Simbolon, Agnes Batubara, and Julhanna Br Ginting. "Application of Fuzzy Linear Programming to Optimize the Amount of Chicken Production Using Tsukamoto Method (Case Study: Ayam Geprek XYZ)." Journal of Research in Mathematics Trends and Technology 5, no. 2 (2023): 45–52. http://dx.doi.org/10.32734/jormtt.v5i2.16700.
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Fuzzy linear Programming is the development of a linear program model to find and determine the optimal value containing fuzzy values. To optimize the amount of raw chicken production, this research uses the Tsukamoto method and Fuzzy Linear Programming (FLP). This case study is focused on Ayam Geprek Campus Habibi. Market demand, raw material availability, and production capacity are some of the factors that affect raw chicken production. The FLP method with Tsukamoto approach is used to handle diversity and uncertainty in decision making about chicken production. A case study was conducted to show that this technique is effective in improving production efficiency and resulting in more optimized raw chicken production management decisions. The results of this study are expected to increase our understanding of the application of FLP with Tsukamoto method in the food industry, particularly on how to optimize raw chicken production.
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Sadi, Sumardi SPd, and Sri Mulyati. "Kontrol Fuzzy Logic Dalam Menentukan Produksi Air Demin Pada PLTU." JEECOM Journal of Electrical Engineering and Computer 3, no. 2 (2021): 78–81. http://dx.doi.org/10.33650/jeecom.v3i2.2843.
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Demineralized water production that is not in accordance with the supplies needed in a power plant results in less effective and efficient operation of the power plant. This requires careful control. The control system can use a fuzzy logic-based control system. In this article, we describe the trial of controlling the production of demin water with the aim of determining the production of electricity for consumer needs. The method used is fuzzy logic control. The result can adjust the supply of demin water according to the available stock and the number of requests. The electricity production resulting from fuzzy calculations in generating electricity production is the Tsukamoto method of 698.3 MW/day, the Mamdani method of 254.16 MW/day, and the Sugeno method of 537.3 MW/day.
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Sapura, Lena, Agiffuddinsyah Sinaga, and Firdaus Siahaan. "Penerapan Sistem Fuzzy Tsukamoto Dalam Memperkirakan Hasil Produksi Padi." Brahmana : Jurnal Penerapan Kecerdasan Buatan 1, no. 2 (2020): 126–30. http://dx.doi.org/10.30645/brahmana.v1i2.29.
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Plantations are all activities that commercialize certain plants on the soil and / or other growing media in suitable ecosystems; process and market the goods and services produced by these plants, with the help of science and technology, capital and management to create prosperity for plantation businesses and the community. In ordinary or more dominant plantations, rice fields with sufficient yields and even processing with a fairly short period of time, especially in rice. Rice is the main or basic need and source of calories for humans. Factors that affect rice production include land, seeds, weather, and fertilizer, these factors clearly affect the quality and amount of production produced by farmers. The fuzzy logic method applies Tsukamto's fuzzy inference system in estimating rice production with the variables that influence it. The research objective is to estimate how much rice production with the Tsukamoto method of fuzzy inference using AND operations based on land variables, rice seed material, fertilizer, and the amount of production.
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Nur, Akbar. "The Effect of Neighboring Cells on Handover Decision Making Based on Fuzzy in the WCDMA Network." Jurnal Jartel: Jurnal Jaringan Telekomunikasi 5, no. 2 (2017): 52–58. http://dx.doi.org/10.33795/jartel.v5i2.205.
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In channel transfer (handover) from one Base Station to another Base Station. The purpose of this final project is to analyze the effect of neighboring cells on handover decisions on WCDMA networks based on fuzzy, in this handover process, handover decisions use several parameters related to handovers and supported by fuzzy logic. Relatively high user mobility demands a guarantee until the use of the service ends, the impact of user mobility results in the output being analyzed for this handover decision to help give consideration to the optimal handover decision. The method used is Tsukamoto fuzzy logic, for decision making, while the measurement method In the field, the drive test method is carried out by measuring the signal level around the base station area, and comparing the results of the two methods. Comparison of handover decisions between the results of fuzzy logic and measurements, for example for the results of no proper in fuzzy logic, yields a rate value of 0% for soft handovers and 100% for hard handovers, and for proper results in fuzzy logic, yields a rate value for measurement. 95.22% for soft / soft handover and 4.72% for hard handover
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Pambudi, Wahyu, Yudhi Darmawan, and Priska Choirina. "Rancang Bangun Penstabil Drone S2GA Berbasis Metode Fuzzy Logic Menggunakan Arduino." TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol 6, no. 2 (2020): 104–12. http://dx.doi.org/10.15575/telka.v6n2.104-112.
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UAV merupakan wahana teknologi canggih yang sering digunakan di bidang militer untuk misi pengintaian. UAV terdiri dari beragam jenis, salah satunya yaitu quadcopter. Quadcopter yang digunakan dalam misi militer biasanya mempunyai masalah ketidakstabilan ketika quadcopter tersebut terbang membawa senjata. Oleh karena itu, maka diperlukan sebuah sistem untuk mengatur kestabilan dari percepatan motor quadcopter. Pada paper ini dipaparkan sebuah desain system dari stabilizer drone dengan metode logika fuzzy menggunakan 3 derajat. Penelitian ini bertujuan untuk mengkonfigurasikan kontrol kestabilan quadcopter yang optimal setelah diterapkan metode fuzzy logic inferensi Tsukamoto. Input dari system ini adalah percepatan dan perubahan percepatan. Sedangkan output yang dihasilkan berupa kecepatan motor. Untuk mengetahui error dilakukan pengujian ketepatan posisi 5 kali pada ketinggian 1-3 meter. Sedangkan untuk mendapatkan waktu quadcopter untuk kembali ke posisi semula dapat menggunakan stopwatch. Penelitian ini bertujuan untuk mengkonfigurasikan kontrol kestabilan quadcopter yang optimal setelah diterapkan metode fuzzy logic inferensi Tsukamoto. Hasil penelitian dengan logika fuzzy untuk kestabilan menunjukan nilai rise time sebesar 0,7 detik, settling time 2,55 detik, overshoot sebesar 15 % ketika menerima gangguan sebesar 45cm, dan nilai steady-state 69,55 cm dengan simpangan baku sebesar ± 1,775 cm. Hasil tersebut memberikan akurasi dalam menentukan kestabilan yang lebih baik pada quadcopter. UAV is one of the advanced technology that used in the military for reconnaissance missions. UAV consists of various types, one of them is a quadcopter. Since the quadcopter in military missions has an instability problem when they fly with a weapon, they needed to stabilize the acceleration of a quadcopter motor. This paper presents a design system of drone stabilizer using fuzzy logic method based on 3 degrees of freedom to improve stability. Fuzzy logic that used to configure optimal quadcopter stability control is Tsukamoto's inference fuzzy logic method. The input of this system are acceleration and acceleration change. While, the output of this system is the speed of motor. We did 5 times experiment to find out the accuracy of this system at an altitude of 1-3 meters. Furthermore, to get the quadcopter time from return to its original position we used a stopwatch. Based on the experiments, we obtained a rise time value of 0.7 seconds, settling time of 2.55 seconds, overshoot of 15% when receiving interference of 45cm, and a steady-state value of 69.55 cm with a standard deviation of ± 1.775 cm. These result show that fuzzy logic provide a better accuracy in determining stability on quadcopter.
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Graha Prakarsa and Vani Maharani Nasution. "Pengembangan Sistem Pendukung Keputusan Menggunakan Metode Tsukamoto." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 3 (2019): 414–21. http://dx.doi.org/10.29207/resti.v3i3.1224.
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Hanger maintenance process this time based on the circulation from hanger give to the production side. No standard calculation, looking for risk percentage for hanger go into maintenance, become a problem. The hanger must have a standard calculation for percentage value, where the value can provide a clear decision. There is a method in Fuzzy Logic, that the Tsukamoto method, can be utilized in making a decision. This research is based on the problem of how to make a standard calculation, to looking for the risk percentage level for hanger go into maintenance, by applying Fuzzy Logic Tsukamoto method, so that the calculation becomes faster, accurate, and precise. The result from the application of the Tsukamoto method, to find the risk percentage level for hanger enter maintenance, for example at hanger Back Caesar, the resulting level of percentage hanger requirement is 91%, and hanger maintenance risk level 70,375%. The final result shows hanger Back Caesar has a high maintenance risk level (range between 54,6-100%) and well plan maintenance action. Application of Tsukamoto method that has been done shows that to find the risk level percentage for hanger go into maintenance, the first must be looking for output crisp from the percentage level of hanger that needed with the Tsukamoto method.
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Agnes Putria Mukti and Tri Widodo. "Prediction of Tempe Raw Material Needs for Home Industry Using Tsukamoto Fuzzy Logic Algorithm." Journal of Humanities and Social Sciences Studies 6, no. 11 (2024): 65–76. http://dx.doi.org/10.32996/jhsss.2024.6.11.6.
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The home tempeh industry is faced with the challenge of accurately predicting raw material needs. Implementing an effective prediction system can help estimate the amount of tempeh production in the future, so as to avoid stock shortages of raw materials. In addition, when tempeh producers have accurate predictions, they can manage soybean stocks better, especially when there is a stock shortage, so that production is not affected by rising soybean prices. This research aims to develop a Tsukamoto Fuzzy Logic based prediction system to overcome this problem. Research methods include collecting historical production data, designing fuzzy models, and system testing. Interim results show that the developed system is able to increase prediction accuracy by up to 95%, which in turn can reduce production costs and minimize waste of raw materials.
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Wijaya, Dhina Puspasari, Salis Nizar Qomaruzaman, Andri Pramuntadi, and Dita Danianti. "Implementasi Logika Fuzzy Tsukamoto Terhadap Pengambilan KRS Mahasiswa Informatika Universitas Alma Ata." Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) 7, no. 6 (2024): 1737–48. https://doi.org/10.32672/jnkti.v7i6.8277.
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Abstrak - Proses pengisian Kartu Rencana Studi (KRS) merupakan langkah krusial dalam perjalanan akademik mahasiswa di perguruan tinggi, yang tidak hanya mencakup pemilihan mata kuliah tetapi juga penjadwalan dan pengaturan kelas. Dalam konteks ini, Universitas Alma Ata berupaya meningkatkan efisiensi dan efektivitas pengisian KRS melalui implementasi sistem berbasis Fuzzy Tsukamoto. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi KRS yang dapat memberikan saran berdasarkan variabel seperti Indeks Prestasi Kumulatif (IPK), mata kuliah yang diulang, dan peminatan. Dengan sistem ini, mahasiswa diharapkan dapat merancang rencana studi yang lebih optimal, mengurangi beban kerja dosen pembimbing akademik (DPA), serta meminimalkan kesalahan dalam pengisian KRS yang sering terjadi pada sistem manual saat ini. Selain itu, sistem ini dirancang untuk mengatasi tantangan seperti keterlambatan pengisian KRS, kelalaian mahasiswa dalam mengetahui mata kuliah yang mengulang, serta memberikan fleksibilitas dalam aksesibilitas. Penelitian ini juga mengidentifikasi pentingnya konsultasi dengan DPA dalam proses perencanaan studi, serta menekankan perlunya sistem yang dapat beradaptasi dengan berbagai skenario akademis, termasuk program Merdeka Belajar Kampus Merdeka (MBKM) untuk kedepannya. Dengan demikian, implementasi logika Fuzzy Tsukamoto diharapkan dapat meningkatkan akurasi, efisiensi, dan personalisasi dalam pengisian KRS, serta mendukung mahasiswa dalam mencapai tujuan akademik dan profesional mereka secara tepat waktu..Kata kunci : Kartu Rencana Studi, Logika Fuzzy Tsukamoto, Sistem Rekomendasi Berbasis Website, Perencanaan Studi, Akademik Mahasiswa Abstract - The process of filling out the Study Plan Card (KRS) is a crucial step in the academic journey of students in higher education, which includes not only course selection but also scheduling and class arrangements. In this context, Alma Ata University seeks to improve the efficiency and effectiveness of filling KRS through the implementation of a Fuzzy Tsukamoto-based system. This research aims to develop a KRS recommendation system that can provide suggestions based on variables such as Cumulative Grade Point Average (GPA), repeated courses, and specializations. With this system, students are expected to be able to design a more optimal study plan, reduce the workload of academic supervisors (DPA), and minimize errors in filling out KRS that often occur in the current manual system. In addition, this system is designed to overcome challenges such as delays in filling out KRS, student negligence in knowing which courses are repeated, and providing flexibility in accessibility. This study also identifies the importance of consultation with DPA in the study planning process, and emphasizes the need for a system that can adapt to various academic scenarios, including the Independent Learning Independent Campus (MBKM) program for the future. Thus, the implementation of Fuzzy Tsukamoto's logic is expected to improve accuracy, efficiency, and personalization in filling out KRS, as well as support students in achieving their academic and professional goals in a timely manner. Keywords - Study Plan Card, Fuzzy Tsukamoto Logic, Website-Based Recommendation System, Study Planning, Student Academic
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Rifansyah, Mhd Roji, and Rakhmat Kurniawan R. "Prototype of Microcontroller Based Water Pump Control System for Lettuce Plants Using Fuzzy Tsukamoto." Journal La Multiapp 5, no. 4 (2024): 401–10. http://dx.doi.org/10.37899/journallamultiapp.v5i4.1462.
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This research aims to design and implement a water pump control system for lettuce plants using a microcontroller based on the Tsukamoto fuzzy method. The system utilizes soil moisture sensors and DHT11 temperature sensors to monitor and control the water supply for optimal plant growth. The fuzzy logic control involves three stages: fuzzification, rule evaluation, and defuzzification. Experimental results demonstrate the system's effectiveness in maintaining the desired soil moisture levels, thus ensuring optimal conditions for lettuce plant development. The prototype includes components such as Arduino Uno, relays, water pumps, and LCD displays, all of which integrate seamlessly to achieve the desired control outcomes. The study concludes that the designed system can significantly aid in automating water supply processes, thus benefiting small-scale agricultural practices.
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Andryadi, Aan Ansen, and Ginanjar Nugraha. "Optimalisasi Kualitas Pencahayaan dalam Suatu Ruangan Berdasarkan pada Keseimbangan Kebutuhan Manusia, Efisiensi Energi, dan Pertimbangan Arsitektur dengan Menggunakan Metode Fuzzy Logic Control." Media Informatika 20, no. 1 (2021): 41–48. http://dx.doi.org/10.37595/mediainfo.v20i1.56.
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Kelayakan pencahayaan dalam ruangan menjadi hal yang penting untuk diperhatikan agar mata orang yang berada di dalam ruangan tersebut dapat terjaga kesehatannya. SNI 03- 6575-2001 telah memberikan batas kelayakan pencahayaan dalam ruangan. Penelitian ini membandingkan metode fuzzy logic Tsukamoto dan Sugeno untuk menentukan intensitas kompensasi cahaya di dalam ruangan agar kualitas pencahayaan dalam ruangan menjadi optimal. Penelitian yang dilakukan mengambil beberapa sample data dari ruangan tempat penulis bekerja. Pengambilan data menggunakan sensor intensitas cahaya yang diletakan di atas meja di tengah ruangan dengan menghadap keatas/plafon ruangan. Waktu pengambilan sample data dimulai pada pukul 7 pagi sampai dengan pukul 4 sore, metode pengambilan sample data adalah dengan mengukur dua kondisi pencahayaan ruangan dan mengukur intensitas cahaya ruangan mengandalkan cahaya luar yang masuk melalui jendela dan cahaya lampu dalam ruangan dengan mengaturnya melalui saklar lampu. Hasilnya didapat kesimpulan bahwa metode fuzzy logic Sugeno memberikan hasil yang lebih baik dalam menentukan nilai intensitas kompensasi cahaya yang dibutuhkan agar ruangan tempat bekerja mendapatkan pencahayaan yang sesuai dengan standar yang disyaratkan dibandingkan metode fuzzy logic Tsukomoto.
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Nalurita, Alisa. "PREDICTING THE AMOUNT OF PRODUCTION OF TERI USING FUZYY LOGIC TSUKAMOTO METHOD IN CV.MAHERA." Antivirus : Jurnal Ilmiah Teknik Informatika 14, no. 1 (2020): 27–37. http://dx.doi.org/10.35457/antivirus.v14i1.1080.
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Production is one of the activities carried out in a company, especially CV. Mahera engaged in the processing of export quality dried anchovy. Anchovy is one of the fisheries commodities that have high economic value and is a commodity in the processing of fishery products in CV. Mahera. But the system that is done on the CV. Mahera is still done manually, namely through several processes of weighing, cooking, drying, framing and sorting. so in determining the amount of anchovies exports take a long time and income the amount of production is uncertain in each process. Thus in every time exports sometimes can not meet the desired target by consumers. Therefore, the development of this system aims to apply the Tsukamoto fuzzy method to predict the amount of exported anchovy production based on the number of catches and the dry amount as input variables. The process is carried out by inputting the amount of catch and the amount of dry in the system will then display the results of production as output. The accuracy of the results of the assessment of this system between the original data (115 & 101) with the calculated results (102.5 & 99.25) the highest difference is 12.5 and the lowest difference is 1.75. Based on testing this application it can be seen that the prediction results from the application of the Tsukamoto fuzzy method meet the existing production amount. By using this application the company can predict production results faster than the manual process.
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Safarudin, Zaenul Mutaqin, and Lily Wulandari. "Sistem Pendukung Keputusan Pemilihan Perguruan Tinggi Swasta Program Studi Teknologi Informasi (Komputer) Di Provinsi DKI Jakarta Menggunakan Metode Fuzzy Inference System (FIS) Tsukamoto." EXPLORE 12, no. 1 (2022): 6. http://dx.doi.org/10.35200/explore.v12i1.499.
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Banyak calon Mahasiswa yang merasa kesulitan untuk menentukan perguruan tinggi mana yang akan dipilih khususnya Perguruan Tinggi Swasta (PTS) yang membuka Prodi Teknologi Informasi (Komputer). Setidaknya terdapat lebih dari 50 Perguruan tinggi swasta di DKI Jakarta yang membuka Prodi Teknologi Informasi (BAN-PT), Hal ini bisa berdampak pada kesulitan bagi calon dalam menentukan perguruan tinggi yang tepat sesuai kebutuhan. Banyak Pendekatan atau metode yang digunakan dalam rangka penyelesaian permasalahan pengambilan keputusan. Salah satunya adalah dengan memanfaatkan konsep fuzzy logic melalui Fuzzy Inference System (FIS). pada FIS dikenal beberapa metode diantaranya metode Tsukamoto. metode ini dipilih karena mampu menyeleksi alternatif terbaik dari sejumlah alternatif, dalam hal ini alternatif yang dimaksudkan yaitu alternatif pemilihan PTS Prodi TI berdasarkan kriteria-kriteria yang ditentukan. Penelitian dilakukan dengan mencari nilai bobot untuk setiap atribut kemudian dilakukan proses perangkingan yang akan menentukan alternatif yang optimal. dimana sebelumnya terdapat masalah antara alternatif dan kriteria. dengan menggunakan metode ini diharapkan masalah tersebut dapat terpecahkan sehingga didapatkan alternatif pilihan perguruan tinggi yang sesuai. Kata kunci: FIS Tsukamoto,PTS,IT,UML,PHP,MySQL
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Seen, Xie Shone, Darvishi Mondragon Ortiz-Barrios, and Osei Scott Kant. "A novel stochastic fuzzy decision model for optimizing decision-making in the manufacturing industry." International Journal of Enterprise Modelling 17, no. 1 (2023): 15–23. http://dx.doi.org/10.35335/emod.v17i1.69.
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In unpredictable and imprecise production environments, this research introduces a stochastic fuzzy decision model for the manufacturing industry. Decision-makers can use the stochastic and fuzzy logic model to capture uncertainties, variability, and language representations of industrial factors. The choice problem, fuzzy input variables, and crisp outcome variables are identified to start the research. Linguistic terms related with fuzzy input variables are represented by fuzzy sets and membership functions. Fuzzy rules link fuzzy input variables to crisp output variables based on expert knowledge or historical data. Objective function, restrictions, and fuzzy rules are incorporated into the stochastic fuzzy decision model's mathematical formulation. Decision-makers can maximize outcomes by considering stochastic factors and fuzzy logic with the model. The model uses an optimization technique to find the optimal choice variable values. A numerical example of manufacturing production planning illustrates the model's use. The results show that the stochastic fuzzy decision model may minimize production costs by calculating optimal production quantities depending on demand. The research concludes that the proposed approach helps manufacturing companies make decisions. Decision-makers can use the model to make educated judgments despite uncertainties and inaccurate information. Future study will explore additional aspects and integrate the model into decision support systems or industrial software. In dynamic and uncertain manufacturing contexts, the stochastic fuzzy decision model empowers manufacturing decision-makers to make optimal decisions
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Santosa, Sesar Husen, Suhendar Sulaeman, Agung Prayudha Hidayat, and Ilham Ardani. "Fuzzy Logic Approach to Determine the Optimum Nugget Production Capacity." Jurnal Ilmiah Teknik Industri 19, no. 1 (2020): 70–83. http://dx.doi.org/10.23917/jiti.v19i1.10295.
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This research aimed to present the Fuzzy approach in determining the optimal production capacity of the Nugget Production. The Fuzzy Model was developed by using two membership sets; i.e. the Overall Equipment Effectiveness (OEE) variable membership set of the production machine and Nugget Demand Forecasting. Nugget demand forecasting uses an exponential smoothing method due to its time-series type of query history data. OEE values were calculated using Availability, Performance and Quality Yield. In the Fuzzy approach, the Forecasting Membership Set uses the Triangular Membership Function type, and the OEE Membership Set uses the Trapezoidal Membership Function type. The fuzzy set model produced can be used as a tool for the company in determining the value of the Optimal Capacity production so that demands can be fulfilled and the product stocks decrease.
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Nurpa, Murni Zaliah, Masrizal Masrizal, and Marnis Nasution. "Decision Support System for Determining the Quantity of Brick Production Using the Fuzzy Tsukamoto Method." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 1250–56. https://doi.org/10.47065/bits.v6i2.5497.
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Maximum profits are obtained from maximum sales. Maximum sales are those that can meet existing demands. There is a determination of the planned production amount to meet production levels to meet planned sales levels or market demand levels. Factors that need to be considered in determining production quantities include: the amount of inventory and the amount of demand. The amount of demand and supply is an uncertainty. Fuzzy logic is a science that can analyze uncertainty. One of the fuzzy rule methods is Tsukamoto, which is a method that is often used to build a system whose reasoning resembles human intuition or feelings. The calculation process is quite complex so it takes a relatively long time, but this method provides results with quite high accuracy. Ratu Batubata Refinery is a factory that produces large quantities every day. Therefore, planning the amount of brick production is very important. In order to meet market demand appropriately and in appropriate quantities. By using this application, it is hoped that the company can make it easy for the company to predict production quantities based on the amount of demand and existing inventory data, in order to achieve maximum profits.
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Istiqomah, Iklima, Hafiz Agi Alfasih, Herti Herti, et al. "Optimization of Cassava Production Management using Fuzzy Logic to Enhance Efficiency and Production Yield." Journal of Applied Science, Technology & Humanities 1, no. 4 (2024): 382–93. http://dx.doi.org/10.62535/3pxptb43.
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This research aims to optimize cassava production using fuzzy logic to enhance efficiency and productivity. Cassava is an important agricultural crop in Indonesia with great potential due to its ability to thrive in various types of soil and climates. Despite being considered a secondary food, cassava has numerous health benefits and serves as a good source of energy. However, crop failures and low yields hinder cassava production. Farmers can achieve optimal yields by maximizing output with minimal input costs. In addition to meeting market demand, pricing strategies are also crucial in determining the best products. Therefore, manufacturing companies need to plan the quantity of products to meet the expected demand. Factors such as product supply and demand need to be considered. It is challenging to monitor production elements when manual calculations are used. Hence, a method to accurately predict product availability is needed. The method used is fuzzy logic computation. We employ the Mamdani fuzzy logic method in this study. The outcome of this research is the ability to enhance cassava production yields based on Mamdani fuzzy logic calculations. The computations conducted enable farmers to determine production levels based on consumer demand.
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Sugianti, Sugianti, Angga Prasetyo, and Agnes Triananda. "Bussiness Management System Of Catfish Cultivation Using Fuzzy Inference System Tsukamoto Methods." Brilliance: Research of Artificial Intelligence 3, no. 2 (2024): 494–505. http://dx.doi.org/10.47709/brilliance.v3i2.3619.
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Catfish is a type of freshwater fish that is in great demand among people because it has high nutritional value. The high demand for catfish on the market is a promising business opportunity. The relatively fast maintenance period makes this cultivation much in demand. Management of a catfish farming business requires good strategy and planning so that the business process can provide optimal profits. Appropriate management practices, good planning can predict crop yields with minimal error rates. Based on past data from catfish farming businesses, catfish pond production results are influenced by several factors including pond area, number of seeds, and amount of feed. The catfish cultivation management system produces predictions of catfish harvest but ignores weather conditions, natural disasters and infectious diseases. The method used in crop yield prediction management is the Tsukamoto Fuzzy inference system. The Tsukamoto method applies monotonous reasoning and rules are built using expert knowledge, enabling the system to be able to conclude and manage predictions of catfish harvest based on data regarding pond size, number of seeds and amount of feed. System testing using 10 data shows prediction results obtained through manual calculations and system calculations, resulting in identical results. Further testing uses the white box method to ensure that the data implemented in the Tsukamoto fuzzy management system accurately produces logical decisions. Hence, it can be concluded that the management system using the Tsukamoto method is able to show effective performance in predicting harvest results based on data on pond area, number of seeds and amount of feed consumption. This management system is expected to be able to provide recommendations for catfish cultivation business planning for the community.
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Afi, Mochamad Amirul, Hindarto Hindarto, and Ade Eviyanti. "Optimization of Onion Cracker Production Using Fuzzy Mamdani Logic." JICTE (Journal of Information and Computer Technology Education) 6, no. 2 (2022): 72–78. https://doi.org/10.21070/jicte.v6i2.1642.
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By using minimal cst in the maximum product sales will generate optimal profits. The factor of using cost greatly affects the optimal result. Therefore, it is necessary to plan the number of products in a medium and above business in order to continue to produce products to meet market demand. The demand and inventory of goods are factors that need to be considered. Inventory is very difficult to find out when using manual calculations. Therefore, a method is needed to ensure the availability of production goods. In this study, the method used was Mamdani's fuzzy logic. The results of this study are to predict the amount of onion cracker products in the calculation of fuzzy mamdani logic. It is hoped that the results of this study can help medium and upper businesses to determine the number of products that are in accordance with consumer demand. From the calculation results, by entering an input variable of 810 packs and an inventory of 150 packs, the output is 700 packs. Highlights: Demand and Inventory Management: Efficient production planning is crucial for meeting market demand while minimizing waste. Fuzzy Logic Advantage: Mamdani's method helps address uncertainty and complexity in production optimization effectively. Result Validation: Both manual calculations and MATLAB simulations yield consistent results for optimized output prediction. Keywords: Optimization, Fuzzy Logic, Mamdani, Onion Crackers, Production
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Vaca Vargas, Sergio Alejandro. "Automated greenhouse, instrumentation and fuzzy logic." Visión electrónica 14, no. 1 (2020): 119–27. http://dx.doi.org/10.14483/22484728.15907.
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Crops are vulnerable to climatic conditions; therefore, their quality may vary according to environmental behavior. Under optimal conditions the crop can have a good productive development and an increase in the yield per unit area, reducing the risks caused by climatic changes, pests and diseases. With the electronic application in greenhouses it is possible to make efficient use of the resources since these can be controlled according to each stage of the development of the crop. Being in an isolated environment, in other words, independent of the external environment, it is possible to carry out production at any time of the year, thanks to the microclimates. The following paper shows the development of an automated greenhouse using electronic instrumentation to control its irrigation, lighting, humidification and ventilation systems using fuzzy logic.
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Tiara Safitrah, Divo Wibowo Adi, Irfan Tigranaufal Nugraha, et al. "Mamdani's Fuzzy Logic-Based Tapioca Optimal Production Amount Prediction System." Journal of Applied Science, Technology & Humanities 1, no. 3 (2024): 265–79. http://dx.doi.org/10.62535/ws0haa49.
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This research focuses on the imbalance between supply and demand in the tapioca industry, especially on the scale of Small and Medium Industries (SMI). Such challenges involve price fluctuations and lack of efficiency in determining the optimal production amount. Using fuzzy logic as an artificial intelligence method, this research aims to develop a system for predicting the optimal amount of tapioca production that can increase efficiency, reduce waste of raw materials and energy, and stabilize prices. The data used in this study was obtained through interviews with resource persons who are involved in the field of tapioca SMI. Furthermore, the data was processed using the Mamdani Fuzzy Inference System method. The results showed that in the case of production when demand is 400 kg and cassava availability is 2000 kg, the optimal production amount of tapioca is 630 kg. This value is also consistent when proven using the Matlab R2015a application. This shows that the model can be relied upon in determining the decision on the amount of tapioca production by considering demand factors and raw material availability.
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Deswinda, Novi, Rika Ampuh Hadiguna, and Nikorn Sirivongpaisal. "Hybrid Model of Fuzzy Logic and Genetic Algorithm for Product Assembly Sequence Optimization." Andalasian International Journal of Applied Science, Engineering and Technology 4, no. 1 (2024): 20–30. http://dx.doi.org/10.25077/aijaset.v4i1.104.
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The sequence of product assembly affects the efficiency and effectiveness of production because it reduces cycle time and production costs and reduces production errors. The application of artificial intelligence is growing to optimize the problem of assembly sequences of components or products, including genetic algorithms and fuzzy logic. These two models can complement each other to produce the best assembly sequence. This research consists of several stages: model formulation, model analysis, solution, and model verification and validation. A hybrid fuzzy and genetic algorithms model can optimize product assembly sequences more effectively and efficiently. Fuzzy logic can help determine the variables that must be optimized, while genetic algorithms can help find the optimal solution by combining these variables. Experiments using hybrid fuzzy logic and genetic algorithms to minimize assembly cycle time have resulted in product part assembly sequences that accommodate all geometric constraints, including assistive devices.
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Panjaitan, Bosar, M. Kom, Hernalom Sitorus, and M. Kom. "DECISION SUPPORT SYSTEM TO DETERMINE THE OPTIMAL PRODUCTION QUANTITY USING FUZZY TSUKAMOTO METHOD ON LAMOS GARMENT." International Journal of Advanced Research 10, no. 10 (2022): 814–19. http://dx.doi.org/10.21474/ijar01/15550.
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Lamos garment is a clothing factory that focuses on producing pants, with anuncertain amount every month. However, in determining the amount of production, there isno calculation or method used so that the number of products produced by garmentssometimes has advantages and disadvantages. This encourages the author to make adecision support system application to make it easier for the owner to determine the optimalamount of production each month. To design a decision support system application theauthor uses the Tsukamoto fuzzy method. Testing the system used in this study using a blackbox. Based on the results of the study, it can be concluded that the designed application canrunand function as expected.
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Alim, S. A., M. Sumaila, and I. Y. Ritkangnga. "Design of a Fuzzy Logic Controller for Optimal African Catfish Water Production." MEKATRONIKA 3, no. 2 (2021): 42–48. http://dx.doi.org/10.15282/mekatronika.v3i2.7352.
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Fish, unlike other animals, feed and defecate inside the same water where they live. When water quality depreciates, consumed feed is not properly converted into body flesh, poor growth is recorded, fish survival is affected and ultimately massive fish deaths may occur. In fish production, key water quality parameters which need to be continually monitored and controlled are temperature, dissolved oxygen (DO), pH, and ammonia. These parameters are highly non-linear, and thus difficult and expensive to use conventional controllers. Fuzzy logic controllers can suitably adapt to non-linearity because it uses sentences for control actions rather than equations. FisPro (Fuzzy Inference System Professional) is used to create fuzzy inference system (FIS)/ fuzzy logic controller (FLC) for simulating physical or biological systems. The selected water quality parameter are temperature, 14 to 45oC, potency of hydrogen (pH), 0 to 14, and turbidity, 1 to 5. At a pH of 3.5 and 39oC, the aerator speed is 6982 rpm. Similarly, at a pH of 3.5 and turbidity of 1.5, the valve position is 47o. The results obtained shows the completeness, consistency and continuity of the designed rule system. Nine rules were designed for each control action giving a total of eighteen rules. The control actions are; water discharge/refill in form of valve actuation and aeration in form of air pump activation.
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Adek, R. T., M. Ula, and B. Bustami. "Efficient hygro-thermal and ammonia control in day-old chick brooding box using internet of things and Tsukamoto Fuzzy controller." IOP Conference Series: Earth and Environmental Science 1356, no. 1 (2024): 012119. http://dx.doi.org/10.1088/1755-1315/1356/1/012119.
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Abstract Day-old chick brooding box systems play a pivotal role in optimizing chicken production by providing ideal environmental conditions. Controlling hygro-thermal parameters (temperature and relative humidity) and ammonia levels in poultry buildings is paramount. In this study, we introduce a novel approach where an Internet of Things (IoT) based prototype for day-old chick brooding box management is integrated with an Android application for real-time monitoring and control. The prototype incorporates an innovative hybrid control strategy, combining a Tsukamoto (TSUKAMOTO) fuzzy logic controller (FLC) with IoT. This approach is rigorously tested through experimental measures and studies over a 90-day period, encompassing both rainy and drought seasons. Comparative analysis reveals that the T-FLC controller outperforms conventional methods, exhibiting lower root mean square errors for temperature and relative humidity response (0.9°C, 1.35%) compared to the FLC (1.18°C, 1.89%) and On/Off controller (2.08°C, 3.07%). Importantly, all controllers maintain ammonia concentrations below 4 ppm. Furthermore, the T-FLC system demonstrates superior efficiency, achieving a daily weight gain rate of 95%, surpassing the FLC (89%) and On/Off controller (81%). Additionally, the T-FLC controller significantly reduces energy consumption, saving up to 40% compared to the On/Off controller and 16% compared to the fuzzy controller. These findings underscore the exceptional efficiency and effectiveness of the proposed control strategy for day-old chick brooding box applications, promising enhanced poultry production and sustainability.
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Marrouchi, Sahbi, Moez Ben Hessine, and Souad Chebbi. "Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations." Engineering, Technology & Applied Science Research 14, no. 2 (2024): 13305–12. http://dx.doi.org/10.48084/etasr.6894.
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This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.
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Pramesti, Almaida, Aura Fadhilah, Belvana Fazwa Athallah, et al. "Implementation of Fuzzy Logic in Yeast Concentration and Fermentation Time for Tempeh Quality." Journal of Applied Science, Technology & Humanities 2, no. 3 (2025): 349–64. https://doi.org/10.62535/1am4xb61.
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This study investigates the application of fuzzy logic to optimize yeast concentration and fermentation time in tempeh production. Tempeh, a traditional Indonesian food made by fermenting soybeans, requires precise conditions to ensure high quality, including optimal yeast concentration and incubation duration. Fuzzy logic provides a flexible approach by allowing variable inputs within ranges, rather than fixed values, making it ideal for controlling uncertain factors in fermentation. The study used MATLAB software for fuzzy logic modeling, incorporating yeast concentration and fermentation time as input variables and tempeh quality as the output. Key parameters for each input were defined, and fuzzy rules were applied to predict tempeh quality under varying conditions. Results indicate that a yeast concentration of 30% and a fermentation time of 53 hours yield a quality rating of 6.9, indicating satisfactory tempeh. The fuzzy model proved accurate, with close alignment between predicted and actual results, underscoring fuzzy logic's effectiveness in refining fermentation processes.
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Nasution, Vani Maharani, and Graha Prakarsa. "Optimization of Items Production using Fuzzy Logic Mamdani Methods." Rekayasa 13, no. 1 (2020): 82–87. http://dx.doi.org/10.21107/rekayasa.v13i1.5893.
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The availability of a production item is still difficult to monitor by the company, because the system used still relies on manual calculations from employees of the company. Assisting the company in predicting the availability of production goods effectively then used the fuzzy logic calculations. During this time the availability of production goods in Salman Collection is seen from customer's request. This makes the company not get the maximum profit because there is no planning the amount of production of goods, if the number of products manufactured by the company is less than the number of requests then the company will lose the opportunity to Gain maximum profit. Conversely, if the number of products produced is much more than the number of requests then the company will suffer losses. Application of goods production optimization using Fuzzy logic Mamdani method is an application intended to solve the problem of goods production in Salman Collection is uncertain. Implementing Fuzzy logic on solving the problem of production goods can help the company to optimize the production of goods. The construction of this application the company can determine the amount of production that corresponds to consumer demand and by applying Fuzzy logic Mamdani methods Most of the demand in Salman Collection is fulfilled and more optimal than With the old system or the amount in production by the company.
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Kamunge Moses Muriuki, Peter Okemwa, and Isaac Wanjala Nangendo. "Fuzzy logic optimization of moisture content in boiler wood fuel used in tea factories in Kenya." World Journal of Advanced Engineering Technology and Sciences 14, no. 3 (2025): 259–66. https://doi.org/10.30574/wjaets.2025.14.3.0124.
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This research investigates the use of fuzzy logic to optimize the moisture content of wood fuel used for boilers in Kenya's tea processing factories. Moisture levels are crucial as they influence combustion efficiency, energy production, and emissions levels. Integration of a fuzzy logic control system into the wood-fired boiler significantly reduced the moisture content from an initial 23% to a more optimal 18%, as determined by simulation outcomes. This 5% reduction in moisture content was accomplished through the dynamic adjustment of various boiler operating parameters, in this case, fuel feed rate, combustion airflow, and steam pressure, integrating a fuzzy logic algorithm that drew insights from both expert and real-time sensory data. The simulated operation of the fuzzy logic control system showed an enhancement in boiler efficiency of up to 81%, a decrease in emissions of up to 178g/kWh, and an overall improvement in system reliability, thereby demonstrating the efficacy of integrating fuzzy logic in wood-fired boiler for enhancing performance and addressing moisture-related challenges.
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Olteanu, Marius, Nicolae Paraschiv, and Petia Koprinkova-Hristova. "Genetic Algorithms vs. Knowledge-Based Control of PHB Production." Cybernetics and Information Technologies 19, no. 2 (2019): 104–16. http://dx.doi.org/10.2478/cait-2019-0018.
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Abstract The paper proposes an approach using Genetic Algorithm (GA) for development of optimal time profiles of key control variable of Poly-HydroxyButyrate (PHB) production process. Previous work on modeling and simulation of PHB process showed that it is a highly nonlinear process that needs special controllers based on human experience, as such fuzzy logic controller proved to be a good choice. Fuzzy controllers are not totally replaced, due to the specific process knowledge that they contain. The achieved results are compared with previously proposed knowledge-based approach to the same optimal control task.
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Bayyinah, Aulia Nabil, and Sesar Husen Santosa. "LB Lamp Production Planning Based on Fuzzy Logic Approach at PT XYZ." IJIEM - Indonesian Journal of Industrial Engineering and Management 6, no. 1 (2025): 128. https://doi.org/10.22441/ijiem.v6i1.26757.
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Increasingly tight competition in the industrial market encourages companies to optimize production planning to meet customer demand efficiently. Effective production planning is key in achieving this goal, as it guides entities towards achieving their goals appropriately. This research aims to develop a fuzzy method for production planning at PT XYZ, a lamp manufacturing company. This method is designed to assist PT XYZ The approach used in this research includes historical data analysis, forecasting techniques using the time series method, calculating raw material inventory, and applying fuzzy logic to determine optimal production quantities. Based on the results of fuzzy calculations, the production amount for the next period of 7,000 product units can be a suggestion for companies to avoid production shortages and excesses due to uncertainty in product demand.
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Suryatini, Fitria, Wahyudi Purnomo, and Raiany R. Harlanti. "Sistem kendali penyemaian bersusun pada tanaman hidroponik berbasis logika fuzzy Tsukamoto dengan aplikasi Blynk." JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) 3, no. 1 (2023): 37–46. http://dx.doi.org/10.35313/jitel.v3.i1.2023.37-46.
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Penyemaian merupakan tahap awal pada budidaya tanaman hidroponik, yang ditandai dengan pertumbuhan aktif dari embrio yang akan mengakibatkan pecahnya kulit benih dan munculnya tanaman muda. Tahap ini merupakan tahap yang penting karena menyangkut kelangsungan pertumbuhan tanaman pada tahap selanjutnya. Pertumbuhan tanaman pada tahap penyemaian dipengaruhi oleh beberapa faktor diantaranya kadar air, suhu, nutrisi, dan intensitas cahaya. Sedangkan, penyiraman merupakan sebuah hal yang tidak dapat dilepaskan dalam menjaga serta merawat tanaman. Oleh karena itu, tujuan dari penelitian ini untuk mengendalikan kelembapan pada media tanam, rockwool, dengan melakukan penyiraman secara otomatis menggunakan metode fuzzy logic control (FLC). Pengendalian bertujuan untuk memberi nutrisi yang sesuai dengan kebutuhan nutrisi, menjaga TDS yang berkisar antara 300-500 ppm, dan memastikan tanaman mendapatkan intensitas cahaya matahari yang tidak terlalu tinggi dan tidak terlalu rendah sehingga tanaman yang merupakan hasil semai dapat tumbuh secara optimal. Selain itu, agar dapat mengefisienkan waktu dan tenaga, Arduino Mega 2560 akan mengakuisisi data dari sensor kelembapan media tanam, suhu ruangan, sensor LDR, sensor TDS, sensor ultrasonik, dan mengirimkan data tersebut melalui internet menuju aplikasi Android. Hasil penelitian menunjukan bahwa sistem dapat menjaga kelembapan media tanam dengan kelembapan rata-rata sebesar 98,58%. Sedangkan, pengendalian kadar nutrisi dapat dilakukan dengan rata-rata galat sebesar 13,36%. Selain itu, sistem berhasil dipantau dan dikendalikan melalui aplikasi Blynk yang terdapat pada smartphone Android.
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Imelda Nasrul, Tiara, Amri Amri, and Muhammad Sayuti. "Application of Fuzzy Mamdani Method to Predict the Number of Blood Bags Based on Demand and Supply Data Using Matlab." International Journal of Engineering, Science and Information Technology 4, no. 4 (2024): 29–37. http://dx.doi.org/10.52088/ijesty.v4i4.567.
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Fuzzy logic is a control system technique in solving problems and is applied to systems, from basic systems to difficult or complex systems. Fuzzy logic is the proper method to plan an input space into an output space using MATLAB's mathematical theory of fuzzy sets. The reason for using fuzzy logic is because it is related to uncertainty. The unstable demand for blood bags in hospitals makes the supply of blood bags excessive or lacking from demand. The lack of blood supply results in the unfulfilled demand for blood needed by the hospital, while the excess blood supply worsens the quality of blood. In this study, we will predict the number of bags produced using the Mamdani Fuzzy Inference System (FIS) method based on the minimum demand and maximum demand values and the minimum supply and maximum supply that produce output from the defuzzification process. Applying the Mamdani Fuzzy Inference System (FIS) method based on demand and supply data obtains optimal output with MATLAB in predicting the number of blood bags produced. The results of the study showed that the Mean Absolute Percentage Error (MAPE) fuzzy logic Mamdani error value was 24%, the accuracy value of the Fuzzy Inference System (FIS) Mamdani in determining the number of blood bag production was 76%, and the production output generated through the Fuzzy Inference System (FIS) Mamdani was 4,774 blood bags. The number of blood requests at the hospital is 4,443 blood bags, so the amount of blood that must be produced to meet the hospital's demand is 4,774 bags.
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Sim, Hock Kheng, and Zhi Xin Wong. "Fuzzy Decision Tree Approach for Optimal Supplier Base." Applied Mechanics and Materials 315 (April 2013): 283–87. http://dx.doi.org/10.4028/www.scientific.net/amm.315.283.
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Minimizing supplier base allows better supplier relationship management, but any supplier failure will interrupt the supplies chain. Earth quake in Fukushima, Japan and flood in Bangkok, Thailand, caused supply chain breakdown and affect the global production. Decision tree method is recommended to determine optimal number of supplier in supplier base with the risk of supplier failure. This paper presented an approach to integrate fuzzy logic in decision tree approach by rating the supplier risk in linguistic terms for imperfect environment. Numerical example and sensitivity are carried out and presented, and optimal supplier base was identified.
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Tordecilla, Rafael D., Leandro do C. Martins, Javier Panadero, Pedro J. Copado, Elena Perez-Bernabeu, and Angel A. Juan. "Fuzzy Simheuristics for Optimizing Transportation Systems: Dealing with Stochastic and Fuzzy Uncertainty." Applied Sciences 11, no. 17 (2021): 7950. http://dx.doi.org/10.3390/app11177950.
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In the context of logistics and transportation, this paper discusses how simheuristics can be extended by adding a fuzzy layer that allows us to deal with complex optimization problems with both stochastic and fuzzy uncertainty. This hybrid approach combines simulation, metaheuristics, and fuzzy logic to generate near-optimal solutions to large scale NP-hard problems that typically arise in many transportation activities, including the vehicle routing problem, the arc routing problem, or the team orienteering problem. The methodology allows us to model different components–such as travel times, service times, or customers’ demands–as deterministic, stochastic, or fuzzy. A series of computational experiments contribute to validate our hybrid approach, which can also be extended to other optimization problems in areas such as manufacturing and production, smart cities, telecommunication networks, etc.
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Nasution, Vani Maharani, and Graha Prakarsa. "Optimasi Produksi Barang Menggunakan Logika Fuzzy Metode Mamdani." JURNAL MEDIA INFORMATIKA BUDIDARMA 4, no. 1 (2020): 129. http://dx.doi.org/10.30865/mib.v4i1.1719.
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Maximum profit gained from maximum sales. Maximum sales means that they can meet the demands. If the products produced by the company is less than requests then the company will lose the opportunity to get maximum profit. Therefore, planning the Amount products in a company is very important in order to meet the market demand precisely and with the appropriate amount. Factors that need to be considered in determining the amount products, such as the demand and supply of old periods. The availability of production goods is still difficult to monitor by the company, because the system is still relying on manual calculations of the company employees, to assist the company in predicting the availability of production goods Effective then used the fuzzy logic calculations. During this time the availability of production goods in Salman Collection is seen from customer request. This makes the company not get the maximum profit because there is no planning the amount of production of goods. This study uses methods with a descriptive approach using the fuzzy logic Mamdani technique. The results of this research is an application of calculation of goods production based on the fuzzy manual calculations of the "logic Mamdani," Built applications can help the company determine the amount of production in accordance with consumer demand so that The demand at Salman Collection is fulfilled and the more optimal the amount that the company will produce.
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Dizaji, Mostafa Rahimi, Aghil Yusefi Koma, Nasser Ghassembaglou, and Sina Shakoorzadeh. "Wind Turbine Control with Fuzzy Supervisory in the Partial Load Region." Advanced Materials Research 433-440 (January 2012): 2332–37. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2332.
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Improvement of the performance of wind turbines as a source of clean energy production depends on the ideal control. In this paper a controller design based on fuzzy logic and for partial load region of wind turbine has been discussed. Here, fuzzy logic is used as a gain scheduler for classical controller. In the structure of the Controller, feedback signal of angular velocity of generator rotor is used to adjust the torque on the generator, while the pitch angle was kept constant on the optimal value. Fuzzy rule-base of supervisory system was derived based on responses of the turbine system to the controller with various gains which lead to track the ideal power curve. Simulation results confirmed the improvement of system response in comparison to the controlling without fuzzy supervisory.
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Kuppulakshmi, V., C. Sugapriya, J. Kavikumar, and D. Nagarajan. "Fuzzy Inventory Model for Imperfect Items with Price Discount and Penalty Maintenance Cost." Mathematical Problems in Engineering 2023 (January 5, 2023): 1–15. http://dx.doi.org/10.1155/2023/1246257.
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The present paper considers the fuzzy economic manufacturing model (FEMM) for an inventory model with an imperfect production process that has been studied along with rework. During the pandemic, it is evident that the products accumulated without a sale, which has increased the maintenance cost of the products. This research paper compares a special sale of products with discount and without discount prices both in the fuzzy environment and in the crisp case. New computing methods based on fuzzy logic are being utilized to enhance identification, decision making, and optimization. A triangular fuzzy number is applied in the economic production quantity to emphasize the importance of optimal manufacturing. The EPQ model’s optimal total cost is obtained in the crisp version. It is to be noted that this model is developed in the fuzzy sense by using the deterioration as a triangular fuzzy number. The applications of this model in the fields are constructing customized industrial machinery or heavy-duty construction equipment, specific chemicals, and processed food. By using MATLAB R2021, a numerical example of the optimal solution is provided. Finally, the present research discusses how changing several parameters affects the optimum total cost.