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All Journal JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Wafa, Fachrian Muhammad Ahzami
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering

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Clustering Of Informatics Study Program Based On Understanding The Material Using The K-Means Algorithm Prasetyo, Naufal Ibra; Bria, Dionisia Kasilda; Paratu, Jeki Bani; Wafa, Fachrian Muhammad Ahzami; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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The level of student understanding in coursework is a crucial determinant of academic success, reflecting both teaching quality and the effectiveness of applied learning methods. In the context of Informatics, challenges often stem from the complexity of subjects such as algorithms, programming, and data analysis, which require analytical and in-depth comprehension. However, differences in learning abilities, backgrounds, and styles often result in varying levels of understanding among students. This study investigates the application of k-means clustering as an innovative method to analyze academic data and classify students based on their understanding of course materials. By utilizing data such as exam scores, quiz results, and classroom engagement, k-means clustering identifies patterns in students’ comprehension levels, offering educators insights to tailor teaching strategies effectively. The findings of this study are expected to aid educators in designing targeted interventions, enhance learning processes, and support an inclusive and effective academic environment.
Application of the Naive Bayes Algorithm to Predict The Purchase Decisions Puspitarini, Erri Wahyu; Masdiyanto, Andreas; Kiyosaki, Robert Baz; Hakiki, Sudrajad; Conteh, Alusine; Wafa, Fachrian Muhammad Ahzami
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
Publisher : Lumina Infinity Academy Foundation

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This study applies the Naive Bayes algorithm to predict the decision to purchase used motorcycles based on attributes such as model, year of manufacture, price, engine capacity, and transaction results. Utilizing the Gaussian Naive Bayes approach for continuous data, this research aims to develop a reliable predictive model and understand the most significant attributes influencing purchasing decisions. The test results show that the predictive model achieves an accuracy rate of 75%, indicating the effectiveness of the Naive Bayes algorithm in handling data classification. This study provides insights that can help industry players enhance their sales strategies based on accurate data analysis.
Co-Authors Bria, Dionisia Kasilda Conteh, Alusine Hakiki, Sudrajad Kiyosaki, Robert Baz Masdiyanto, Andreas Paratu, Jeki Bani Prasetyo, Naufal Ibra Puspitarini, Erri Wahyu Salisu, Imam Auwal