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

JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Vol. 1 No. 2 (2024): June

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.