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All Journal JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Akhdan, Farrel Muhammad Raihan
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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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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.
PREDICTION OF INFORMATICS ENGINEERING STUDENTS' GRADUATION USING THE NAIVE BAYES METHOD BASED ON VALUES ASSIGNMENTS AND ATTENDANCE Akhdan, Farrel Muhammad Raihan; Koten, Antonius Suban; Bouk, Anggela M; Rozi, Fatchulloh Reza Ar; Agustina, Rini
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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Student graduation is one of the indicators of the success of the educational process in higher education. This study aims to predict the graduation of students in the Informatics Engineering study program using the Naive Bayes method, by considering the Final Semester Exam (UAS), Mid-Semester Exam (UTS), assignments, and attendance as the main variables. The Naive Bayes method was chosen because of its simplicity in handling multivariable data and its ability to produce accurate classification models.
Utilizing Datamining to Predict Sales Trends Based on Historical Data Junda, Alby Afifuddin; Trisna, Maria Rosalina; Genohon, Yustino Prami; Akhdan, Farrel Muhammad Raihan; Salisu, Imam Auwal
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 3 (2024): October
Publisher : Lumina Infinity Academy Foundation

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This study aims to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in predicting sales trends based on historical data. The results of the study show that SVM is more effective than Naïve Bayes with an accuracy of 34.74% compared to 15.49%. This study helps companies in making strategic decisions and improving operational efficiency. Data Mining is an important tool in predicting sales trends and improving prediction accuracy.
Co-Authors Bouk, Anggela M Genohon, Yustino Prami Hakiki, Sudrajad Junda, Alby Afifuddin Kiyosaki, Robert Baz Koten, Antonius Suban Masdiyanto, Andreas Peldon, Tshering Rini Agustina Rozi, Fatchulloh Reza Ar Salisu, Imam Auwal Trisna, Maria Rosalina