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Journal articles on the topic 'ID3 Decision tree'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Chen, Xiao Juan, Zhi Gang Zhang, and Yue Tong. "An Improved ID3 Decision Tree Algorithm." Advanced Materials Research 962-965 (June 2014): 2842–47. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2842.

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As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the

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M.Tamboli, N., A. M. Kamble, and P. S. Metkewar. "LCC Decision tree analysis using ID3." International Journal of Computer Applications 41, no. 19 (2012): 19–22. http://dx.doi.org/10.5120/5649-8007.

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Kirandeep and Neena Madan Prof. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 740–44. https://doi.org/10.31142/ijtsrd11073.

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This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The decision node is an attribute test with each branch to another decision tree being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The main aim of this paper is to identify relevant attributes based on quantitative and qualitative aspects of a students profile such as CGPA, academic performance, technical and communication skills and design a

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Avcı, Keziban. "Evaluation of Public Hospitals' Performance with Decision Tree Algorithms." Verimlilik Dergisi 59, no. 1 (2025): 133–42. https://doi.org/10.51551/verimlilik.1494277.

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Purpose: The study aims to evaluate a range of financial performance indicators calculated through structural, operational, and HVI measures for public hospitals in the Turkish healthcare sector using various decision tree algorithms. Methodology: The study comprises threa phases. In the first phase, financial ratios were calculated from the hospitals' financial statements using the ratio analysis method. In the second phase, these ratios were used to calculate the HVI. In the third phase, the selected operational and financial indicators were analyzed with decision tree algorithms. The ID3, C

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Fakir, Y., M. Azalmad, and R. Elaychi. "Study of The ID3 and C4.5 Learning Algorithms." Journal of Medical Informatics and Decision Making 1, no. 2 (2020): 29–43. http://dx.doi.org/10.14302/issn.2641-5526.jmid-20-3302.

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Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms

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Kirandeep, Kirandeep, and Prof Neena Madan. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 740–44. http://dx.doi.org/10.31142/ijtsrd11073.

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Li, Rong Xiang, Zeng Lei Zhang, Yun Liu, and Shan Chao Tu. "Applications of Data Mining Algorithm in Equipment Fault Diagnosis." Applied Mechanics and Materials 644-650 (September 2014): 2551–55. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2551.

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The Basic Principles of Data mining Decision-tree ID3 is opened out. The main deficiencies are analysed. An improved algorithm based on the ID3 is calculated. For fault diagnosis of engine exemple, traditional ID3 algorithm and the improved algorithm are applied to estimate the fault diagnosis of engine separately. Decision Trees of traditional ID3 algorithm and the improved algorithm are construct. Experiment result display the accuracy of improved algorithm is better than traditional ID3. The improved algorithm is more fit to applied to the equipment fault diagnosis.

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Ariestya, Winda Widya, Yulia Eka Praptiningsih, and Wahyu Supriatin. "DECISION TREE LEARNING UNTUK PENENTUAN JALUR KELULUSAN MAHASISWA." Jurnal Ilmiah FIFO 8, no. 1 (2016): 97. http://dx.doi.org/10.22441/fifo.v8i1.1304.

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Jalur kelulusan merupakan hal penentu seorang mahasiswa untuk memperoleh gelar jenjang pendidikan strata satu pada sebuah Perguruan Tinggi. Penelitian ini bertujuan untuk mengetahui pemanfaatan algoritma ID3 dalam penentu jalur kelulusan serta menghasilkan rule atau aturan pada penentuan jalur kelulusan mahasiswa tingkat akhir. Metode yang digunakan adalah Iterative Dichotomiser 3 (ID3) dengan menggunakan alat bantu perangkat lunak Rapidminer. Dari hasil penelitian diperoleh 7 rule atau aturan dalam penentuan jalur kelulusan yaitu 3 rule untuk jalur skipsi dan 4 rule untuk jalur non skripsi da

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Ilayani, Nangi Jumadil, and Purnamasari Pasrun Yuwanda. "APLIKASI DATA MINING UNTUK PENILAIAN KREDIT MENGGUNAKAN DECISION TREE ALGORITMA ID3 STUDI KASUS PT. MANDALA MULTI FINANCE CABANG KENDARI." semanTIK 4, no. 1 (2018): 65–76. https://doi.org/10.5281/zenodo.1402830.

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Credit is one of the most common payment mechanisms in the community. Data mining can search the pattern and new knowledge of these data so that it can facilitate in determination of debtor candidate. For that required a data mining applications that can process and provide the decision of the prospective debtor's eligibility status automatically. One method of data mining used for the classification of prospective borrowers is the decision tree using Iterative Algorithm Dichotomiser 3 (ID3). ID3 is one of the most popular types of Decision trees that attempt to build a decision tree top-d

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Nurtiani, Devy Natalia, and Rizal Adi Saputra. "KLASIFIKASI TINGKAT KEPUASAN MAHASISWA TERHADAP FASILITAS KAMPUS MENGGUNAKAN METODE ID3." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 8, no. 1 (2024): 123–28. http://dx.doi.org/10.46880/jmika.vol8no1.pp123-128.

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This research explores the level of student satisfaction with campus facilities using the ID3 (Iterative Dichotomiser 3) method in forming decision trees. The main focus of this research is to identify factors that influence student satisfaction with various facilities on campus such as libraries, computer laboratories, classrooms, sports facilities, and campus facilities in general. Data from 500 students were obtained from questionnaires and used as a basis for forming a decision tree model. The model visualization provides a clear picture of the satisfaction level classification pattern. Th

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Chen, Wenzheng, Guanglei Yang, and Dongkai Qi. "Comprehensive Evaluation of College Students’ Physical Health and Sports Mode Recommendation Model Based on Decision Tree Classification Model." Computational Intelligence and Neuroscience 2022 (July 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/5504850.

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Nowadays, more and more college students’ physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information los

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Xiaohu, Wang, Wang Lele, and Li Nianfeng. "An Application of Decision Tree Based on ID3." Physics Procedia 25 (2012): 1017–21. http://dx.doi.org/10.1016/j.phpro.2012.03.193.

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Li, Ye, Zoe L. Jiang, Xuan Wang, Junbin Fang, En Zhang, and Xianmin Wang. "Securely Outsourcing ID3 Decision Tree in Cloud Computing." Wireless Communications and Mobile Computing 2018 (October 4, 2018): 1–10. http://dx.doi.org/10.1155/2018/2385150.

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With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography-based privacy-preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which r

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Tetteh, Evans Teiko, and Beata Zielosko. "Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles." Entropy 27, no. 1 (2025): 35. https://doi.org/10.3390/e27010035.

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This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules th

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Saja, Taha Ahmed, Al-Hamdani Rafah, and Sadik Croock Muayad. "Developed third iterative dichotomizer based on feature decisive values for educational data mining." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 1 (2020): 209–17. https://doi.org/10.11591/ijeecs.v18.i1.pp209-217.

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Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement. The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT)

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Huang, Pingge. "Construction of College Chinese Blended Teaching Mode Based on Decision Tree Classification Model in New Media Context." Computational Intelligence and Neuroscience 2022 (September 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4608631.

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Based on the context of new media and big data, this article uses the decision tree classification model to construct the college Chinese hybrid teaching mode. In order to verify the accuracy of ID3 algorithm prediction, the comparison of the ID3 algorithm, K-means algorithm, and support vector machine classification algorithm was made, and the experimental results show that the ID3 decision tree classification algorithm has better prediction and classification ability, for the construction of college Chinese hybrid teaching mode provides certain practical value and reference basis.

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Li, Ju Fang, Jin Hui Lei, Xiao Xia Zhao, Chang Chang Zhang, and Xue Xue Han. "An Improved ID3 Algorithm." Applied Mechanics and Materials 444-445 (October 2013): 723–27. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.723.

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ID3 algorithm is the earliest and most influential of decision tree algorithm. This paper discusses the basic idea and implementation methods of the ID3 algorithm. A new algorithm based on attribute similarity for multivalued bias of ID3 algorithm was proposed, and the experimental results proved that the improved algorithm has strong predictive accuracy and better understandability.

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Aritonang, Milyani. "Penerapan Algoritma ID3 dalam Prediksi Kebutuhan Pupuk." Journal of Information System Research (JOSH) 2, no. 4 (2021): 247–53. http://dx.doi.org/10.47065/josh.v2i4.565.

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The need for fertilizer at the Plant Protection Development Unit (UPPT) is uncertain depending on the demand of farmers, therefore it is necessary to predict fertilizer needs. There are five types of fertilizers predicted by the Plant Protection Development Unit (UPPT), including Urea fertilizer, ZA fertilizer, SP-36 fertilizer, NPK fertilizer, and Organic fertilizer, so fertilizer needs can be predicted. In predicting data mining on fertilizer needs using the ID3 algorithm. Where it works is calculating the value of entropy and gain to get the final result in the form of a tree to the decisio

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Abdi, Nabil Fahlevi, and Maulana Fikri Ahmadi. "Klasifikasi Penyakit Ginjal Kronis (CKD) dengan Algoritma KNN dan Decision Tree ID3." Journal of Informatics and Advanced Computing (JIAC) 5, no. 2 (2024): 8–13. https://doi.org/10.35814/jiac.v5i2.7189.

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Penyakit Ginjal Kronis merupakan masalah kesehatan global yang memerlukan diagnosis untuk pencegahankomplikasi. Menurut Direktur Pencegahan dan Pengendalian Penyakit Tidak Menular Kemenkes RI, diIndonesia sendiri, Penyakit Gagal Ginjal Kronis menjadi penyebab kematian urutan ke-10 dengan angkakematian lebih dari 42 ribu per tahun. Gagal ginjal kronis adalah kondisi ketika fungsi ginjal menurun secarabertahap. Penyakit gagal ginjal bisa terjadi karena berbagai faktor, termasuk hipertensi, diabetes, penyakitautoimun, infeksi ginjal, serta batu ginjal yang tidak diobati secara tepat. Langkah yang

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Priyanka, Saini. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 1, April (2021): 31–41. https://doi.org/10.5281/zenodo.5105645.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Priyanka, Saini1 Sweta Rai2 and Ajit Kumar Jain3 1. 2. M.Tech Student Banasthali University Tonk Rajasthan. "Decision Tree Algorithm Implementation Using Educational Data." International Journal of Computer-Aided technologies (IJCAx) 01, dec (2014): 01–11. https://doi.org/10.5281/zenodo.1450276.

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There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

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Huang, Ke, and Tingxuan Wang. "Optimized Application of the Decision Tree ID3 Algorithm Based on Big Data in Sports Performance Management." International Journal of e-Collaboration 20, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijec.350022.

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In the rapidly evolving landscape of sports performance management, the integration of Big Data analytics has become a game-changer. This paper established a sports performance management system on big data decision tree ID3 algorithm and analyzed the accuracy and time of the improved decision tree ID3 algorithm in sports performance management. The research results show that the data query and data display functions are widely used in the system, and the utilization rate of each function of big data in the system can be clearly seen. In the accuracy of sports performance management, the impro

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Atallah, Dalia, Ali Eldesoky, Amira H., and Mohamed Ghoneim. "One-year renal graft survival prediction using a weighted decision tree classifier." International Journal of Engineering & Technology 3, no. 3 (2014): 327. http://dx.doi.org/10.14419/ijet.v3i3.2334.

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This study introduces a weighted decision tree algorithm for prediction of graft survival in renal transplantation using preoperative patient's data. The objective was to identify the preoperative attributes that affect the graft survival. Between the years 2000-2009, renal allotransplantation was carried out for 889 patients at Urology and Nephrology Center which is the subject matter of this study. The ID3 algorithm was chosen to build up the decision tree using the weka machine learning software. A modification was made on ID3 to refine the results. A weighted vector was introduced. The ele

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Navya, Vattikonda Anuj Kumar Gupta Achuthananda Reddy Polu Bhumeka Narra Dheeraj Varun Kumar Reddy Buddula and Hari Hara Sudheer Patchipulusu. "Machine Learning-Based Approaches for Detecting and Mitigating Distributed Denial of Service (DDoS) Attacks to Improved Cloud Security." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN MULTIDISCIPLINARY EDUCATION 3, no. 12 (2024): 1993–64. https://doi.org/10.5281/zenodo.15340752.

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Cloud environments encounter massive service disruptions together with security breaches and substantial financiallosses through Distributed Denial of Service (DDoS) attacks. Detecting and mitigating DDoS assaults is the focus of this research,which examines the efficacy of ML models, particularly the CNN-LSTM model and the ID3 decision tree method. The CICDDoS2019dataset was used for both training and evaluation, employing a train-test data split of 80:20. The hybrid CNN-LSTMmodel achieved superior performance than the ID3 decision tree method when subjected to comparison because it integrate

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HAN, SANG-WOOK, and JAE-YEARN KIM. "ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE." International Journal of Information Technology & Decision Making 07, no. 02 (2008): 275–90. http://dx.doi.org/10.1142/s0219622008002946.

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Decision trees are widely used in machine learning and artificial intelligence. In this paper, we extend previous research and present a new decision tree classification algorithm that uses a rough set theory to produce classification rules. Our algorithm is based on core attributes and on comparing the values of attributes between objects. Our experiments compared the performance of the Iterative Dichotomiser 3 (ID3) algorithm, C4.5, and the proposed decision tree algorithm to demonstrate its accuracy and ability to simplify rules.

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Natasya, Salsabila Vebi, Rolly Maulana Awangga, and M. Yusril Helmi Etyawan. "Penerapan Decision Tree (ID3) Untuk Profiling Mahasiswa dan Alumni." SMATIKA JURNAL 13, no. 02 (2023): 250–59. http://dx.doi.org/10.32664/smatika.v13i02.898.

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Profiling mahasiswa dan alumni merupakan sebuah studi yang bertujuan untuk mengidentifikasi karakteristik individu yang memasuki dan keluar dari sebuah institusi pendidikan. Melalui analisis data yang dikumpulkan, studi ini dapat memberikan informasi yang berharga bagi institusi yaitu Universitas Logistik dan Bisnis Internasional untuk meningkatkan program-program yang ditawarkan dan membantu mahasiswa serta alumni untuk merencanakan karir mereka. Data yang digunakan pada penelitian ini didapatkan dari scraping pada web linkedin yang berjumlah 300 data. Dengan rasio pembagian data testing dan

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Huo, Li, Bo Jiang, and Ya Xin Liu. "Analysis of Customer Behavior in Automatic Investment Plan Based on Decision Tree." Applied Mechanics and Materials 644-650 (September 2014): 5741–44. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.5741.

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Firstly, this paper introduces the decision tree and then the basic ideas and implementation methods of ID3 algorithm are discussed. Next, processes the data of customer behavior in the AIP business based on ID3 and constructs the decision tree meanwhile the rules are established. Finally, gives the results of the prediction and advices on how to design marketing strategy of fund business. This study can be used to make marketing strategy for fund companies and improve service quality of fund companies consequently.

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Mirza Yogy Kurniawan and Muhammad Edya Rosadi. "OPTIMASI DECISION TREE MENGGUNAKAN PARTICLE SWARM OPTIMIZATION PADA DATA SISWA PUTUS SEKOLAH." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 2, no. 1 (2017): 7–14. http://dx.doi.org/10.20527/jtiulm.v2i1.13.

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Education is the right of every citizen, even government makes program to promote the compulsory education of 12 years. Drop out of school has become an obstacle to the government program where the dropout is caused by many factors, including economic factors, geographical conditions, and students' own desires. ID3 is able to generate a decision tree from a very large data set. This decision tree can be used as a reference for possible drop out of students. In order to be a good reference then the resulting classification must have a high accuracy. PSO is known to increase the accuracy of vari

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Andrea Lesmana, Rafi, Saiful Nur Budiman, Alfian Anwar Shodiqi, Jihan Khansa Nadhila, Muhammad Fauzi Nur Aziz, and Aldy Ibnu Faizal. "IMPLEMENTASI ALGORITMA DECISION TREE-ID3 UNTUK PREDIKSI KELAYAKAN KREDIT BERBASIS WEB DENGAN MENGGUNAKAN NEXT.JS DI KSU SYARIAH MUHAMMADIYAH." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 2 (2025): 2045–53. https://doi.org/10.36040/jati.v9i2.12930.

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Proses penilaian kelayakan kredit merupakan aspek penting dalam mendukung keberlanjutan operasional lembaga keuangan, terutama koperasi syariah. Dalam konteks KSU Syariah Muhammadiyah, analisis kelayakan kredit masih dilakukan secara manual, sehingga membutuhkan waktu lama dan berisiko menghasilkan keputusan yang tidak konsisten. Penelitian ini bertujuan untuk mengembangkan sistem prediksi kelayakan kredit berbasis web menggunakan algoritma Decision Tree-ID3 dan framework Next.js, guna meningkatkan efisiensi dan akurasi proses penilaian. Metode pengembangan sistem mengikuti model waterfall, me

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Mellina, Ayu Dian Fitri, Suhartono Suhartono, and M. Ainul Yaqin. "Prediksi Deteksi Penyakit Kanker Payudara dengan Menggunakan Algoritma Decision Tree." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 1 (2024): 70–78. http://dx.doi.org/10.14421/jiska.2024.9.1.70-78.

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Cancer is a deadly disease that is difficult to cure. Early cancer detection can be done through laboratory tests to identify the cancer type. Breast cancer is a type of cancer with initial symptoms in the form of a lump. Data mining and classification methods, such as decision trees with ID3 and C5.0 algorithms, are used to categorize breast cancer. The dataset used is Breast Cancer Coimbra, which was downloaded from UCI Machine Learning in 2018. ID3 has limitations in handling unstructured data and continuous attributes, while C5.0 is better. Both algorithms produce tree models with differen

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Jiang, Ming Hua, and Xiao Suo Luo. "Classification of Student Achievement Using ID3 Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 2540–45. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2540.

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Anggita Kristanti, Putri, and Singgih Jatmiko. "KLASIFIKASI PEMILIHAN PRODUK PERBANKAN DENGAN ALGORITMA DECISION TREE ID3 DAN C4.5." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 4 (2025): 5999–6005. https://doi.org/10.36040/jati.v9i4.13907.

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Seiring meningkatnya kebutuhan nasabah, bank berusaha menarik nasabah dengan menawarkan produk yang paling sesuai. Produk merupakan faktor dalam mempengaruhi keputusan nasabah dalam memilih layanan yang tepat. Penelitian ini bertujuan untuk melakukan klasifikasi produk bank dengan membandingkan pengukuran akurasi, presisi dan recall algoritma decision tree ID3 dan C4.5 menggunakan tools RapidMiner. Dalam penelitian ini dataset tersedia dengan jumlah 16.848 terdiri dari 13 atribut, 12 atribut prediksi antara lain age, gender, religion, ntb, household, education, salary, realiation, potential, r

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Yong, Hu. "The Water Conservancy Water Electricity Construction Engineering Professional Result Analysis of Application Base on C4.5 Algorithm." Advanced Materials Research 926-930 (May 2014): 703–7. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.703.

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Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclu

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S.V.S., Ganga Devi. "FUZZY RULE EXTRACTION FOR FRUIT DATA CLASSIFICATION." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 12 (2013): 400–403. https://doi.org/10.5281/zenodo.14613549.

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Decision Tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic Decision Tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible Decision Trees have been designed and then rules are extracted for perfect symbolic data. Over the years, additional methodologies have been investigated and proposed to deal with continuous or multi-valued data and with missing or noisy features. Recently, with the growing popularity of fuzzy representation in Decision Trees are introd

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Arao, Masaki, Masahito Tanaka, and Shigeyasu Kawaji. "Knowledge Acquisition by Improved Fuzzy ID3 Algorithm and Stability Analysis for Jacket Tank Temperature Control." Journal of Robotics and Mechatronics 12, no. 6 (2000): 682–88. http://dx.doi.org/10.20965/jrm.2000.p0682.

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The extraction of knowledge from operation data is an important theme in an autonomous control system. An efficient method for making a decision tree for classification from data is the fuzzy ID3 algorithm using fuzzy sets. However, the definition of fuzzy sets greatly affects the generation of fuzzy trees. In this paper, we propose a new version of fuzzy ID3 algorithms to generate a fuzzy decision maximizing the expected value of transferred information by applying a random search method for determining the fuzzy set, and by using the improved fuzzy ID3 algorithm an automatic extraction of co

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TEJASHRI, SHEGADAR. "COMPARING VARIOUS TECHNIQUES OF DETECTION OF SIX FACIAL EXPRESSION WITH THE ALGORITHM ID3( DECISION TREE BASED)." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 2 (2017): 14–21. https://doi.org/10.5281/zenodo.1462259.

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<strong><strong> </strong>Identifying the feelings of others is additionally basic in building up connections and in creating enthusiastic correspondence. Precise acknowledgment and elucidation of outward appearances help people choose when to put forth socially satisfactory expressions and give direction in deciding methodology or withdrawal techniques in interpersonal exchanges. Understanding feelings amid early adolescence relates decidedly to the advancement of versatile social conduct. This paper points of interest the ID3 arrangement calculation. Simply,ID3 fabricates a choice tree

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Li, Yue, Wusheng Xu, Wei Li, Ang Li, and Zengjin Liu. "Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms." Mathematical Biosciences and Engineering 19, no. 2 (2021): 2030–42. http://dx.doi.org/10.3934/mbe.2022095.

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<abstract> <p>Intrusion detection system plays an important role in network security. Early detection of the potential attacks can prevent the further network intrusion from adversaries. To improve the effectiveness of the intrusion detection rate, this paper proposes a hybrid intrusion detection method that utilizes ADASYN (Adaptive Synthetic) and the decision tree based on ID3 algorithm. At first, the intrusion detection dataset is transformed by coding technology and normalized. Subsequently, the ADASYN algorithm is applied to implement oversampling on the training set, and the

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Phu, Vo Ngoc, Vo Thi Ngoc Tran, Vo Thi Ngoc Chau, Nguyen Duy Dat, and Khanh Ly Doan Duy. "A decision tree using ID3 algorithm for English semantic analysis." International Journal of Speech Technology 20, no. 3 (2017): 593–613. http://dx.doi.org/10.1007/s10772-017-9429-x.

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Duan, Ru-jiao, Wei Zhao, Song-ling Huang, and Kuan-sheng Hao. "High-speed corner detection based on fuzzy ID3 decision tree." Journal of Central South University 19, no. 9 (2012): 2528–33. http://dx.doi.org/10.1007/s11771-012-1306-9.

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Baldwin, J. F., J. Lawry, and T. P. Martin. "A mass assignment based ID3 algorithm for decision tree induction." International Journal of Intelligent Systems 12, no. 7 (1997): 523–52. http://dx.doi.org/10.1002/(sici)1098-111x(199707)12:7<523::aid-int3>3.0.co;2-n.

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Fathul Hafidh, Muhammad Edya Rosadi, and Rahmadi Agus. "PENERAPAN METODE ITERATIVE DICHOTOMIZER (ID3) UNTUK DIAGNOSA HAMA TANAMAN ANGGREK." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 2, no. 1 (2017): 29–32. http://dx.doi.org/10.20527/jtiulm.v2i1.16.

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Orchid plants is one component of the aesthetic aspect and become a part of human life. Diagnostics system for classification orchid pests were attack plants still using the manual model, it’s result difficulty identifying pests orchid that has a variety of symptoms are almost the same. As to distinguish the symptoms of damage to the leaves, roots and flowers caused by pests. ID3 able to generate a decision tree of large data sets. This decision tree can be used as a reference for the diagnosis of pests on orchids. In this research, pest classification of orchids do with ID3 method with the hi

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Asrianda, Asrianda, Herman Mawengkang, Poltak Sihombing, and Mahyuddin K. M. Nasution. "Optimization of marketing campaigns using a modified ID3 decision tree algorithm." Eastern-European Journal of Enterprise Technologies 2, no. 13 (134) (2025): 58–70. https://doi.org/10.15587/1729-4061.2025.327158.

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The object of this study is the strategy of online retail marketing campaigns, particularly in the context of utilizing a modified ID3 decision tree algorithm to improve predictive effectiveness regarding consumer responses. It addresses challenges in audience segmentation, campaign evaluation, and market adaptation, while also tackling technical issues such as overfitting, prediction errors, and data imbalance. These challenges often hinder businesses from accurately identifying and targeting potential customers, leading to inefficient marketing strategies and resource allocation. The dataset

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Khin, Khin Lay, and San Nwe San. "Using ID3 Decision Tree Algorithm to the Student Grade Analysis and Prediction." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 1392–95. https://doi.org/10.5281/zenodo.3590845.

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Data mining techniques play an important role in data analysis. For the construction of a classification model which could predict performance of students, particularly for engineering branches, a decision tree algorithm associated with the data mining techniques have been used in the research. A number of factors may affect the performance of students. Data mining technology which can related to this student grade well and we also used classification algorithms prediction. In this paper, we used educational data mining to predict students final grade based on their performance. We proposed st

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Zouggar, Souad Taleb, and Abdelkader Adla. "Proposal for Measuring Quality of Decision Trees Partition." International Journal of Decision Support System Technology 9, no. 4 (2017): 16–36. http://dx.doi.org/10.4018/ijdsst.2017100102.

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To compute a partition quality for a decision tree, we propose a new measure called NIM “New Information Measure”. The measure is simpler, provides similar performance, and sometimes outperforms the existing measures used with tree-based methods. The experimental results using the MONITDIAB application (Taleb & Atmani, 2013) and datasets from the UCI repository (Asuncion & Newman, 2007) confirm the classification capabilities of our proposal in comparison to the Shannon measure used with ID3 and C4.5 decision tree methods.

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Manna, Subhankar, and Malathi G. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research -GRANTHAALAYAH 5, no. 8 (2017): 260–66. http://dx.doi.org/10.29121/granthaalayah.v5.i8.2017.2229.

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Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using

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Subhankar, Manna, and G. Malathi. "PERFORMANCE ANALYSIS OF CLASSIFICATION ALGORITHM ON DIABETES HEALTHCARE DATASET." International Journal of Research - Granthaalayah 5, no. 8 (2017): 260–66. https://doi.org/10.5281/zenodo.890581.

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Healthcare industry collects huge amount of unclassified data every day. For an effective diagnosis and decision making, we need to discover hidden data patterns. An instance of such dataset is associated with a group of metabolic diseases that vary greatly in their range of attributes. The objective of this paper is to classify the diabetic dataset using classification techniques like Naive Bayes, ID3 and k means classification. The secondary objective is to study the performance of various classification algorithms used in this work. We propose to implement the classification algorithm using

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Yang, Weiwei, and Jie Yang. "Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm." Mathematical Problems in Engineering 2022 (September 27, 2022): 1–11. http://dx.doi.org/10.1155/2022/3315872.

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With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorith

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Kazhan, D. V., T. O. Levytska, and O. V. Kryvenko. "Determining the type of explosive item using a decision tree." Reporter of the Priazovskyi State Technical University. Section: Technical sciences, no. 47 (December 28, 2023): 66–72. http://dx.doi.org/10.31498/2225-6733.47.2023.299981.

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The present circumstances in Ukraine delineate specific areas of study. The imperative to identify an explosive device is increasingly paramount for adults and particularly for children. Failing to recognize this danger poses a threat to health and life. The paper presents a decision tree method for classifying the type of explosive objects. This allows you to draw conclusions about the type of explosive object hidden depending on the type of soil. This problem is relevant, as it allows to reasonably explain the type of danger depending on various external factors. The paper offers a general r

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Santoso, Heri Bambang. "Fuzzy Decision Tree to Predict Student Success in Their Studies." International Journal of Quantitative Research and Modeling 1, no. 3 (2020): 135–44. http://dx.doi.org/10.46336/ijqrm.v1i3.59.

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The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this stud

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Jiang, Wu Xue, Yin Zhen Zhong, and Huan Liang. "An Evaluation Model of Polytechnic Teaching Quality Based on ID3 Decision Making Tree." Applied Mechanics and Materials 651-653 (September 2014): 2437–40. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2437.

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For improving the quality of vocational education evaluation validity, this paper introduces the research progress of teaching evaluation, use data mining technology to put forward a feasible model of higher vocational teaching quality evaluation, construct a decision tree has practical significance. By the method used, the traditional Apriori algorithm is improved decision tree to improve the teaching contents to the location of the second master node, reflecting the characteristics of vocational teaching practice. Case analysis indicated, the decision tree has a good operability and high cre

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