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

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Application of the Naive Bayes Data Mining Algorithm to Predict Used Motorcycle Purchase Decisions Masdiyanto, Andreas; Kiyosaki, Robert Baz; Hakiki, Sudrajad; Akhdan, Farrel Muhammad Raihan; Peldon, Tshering
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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Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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 Akhdan, Farrel Muhammad Raihan Conteh, Alusine Hakiki, Sudrajad Kiyosaki, Robert Baz Peldon, Tshering Puspitarini, Erri Wahyu Wafa, Fachrian Muhammad Ahzami