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All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Moch Janwar Abdul Azis
Universitas Harkat Negeri
Computer Science & IT

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Analisis Minat Calon Murid dalam Memilih MTs Mambaul Ulum pada SPMB menggunakan Algoritma Naive Bayes Moch Janwar Abdul Azis; R. Bangkit Indarmawan N.; Nugroho Adhi Santoso
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 5 (2026): Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i5.1801

Abstract

This study was motivated by the importance of understanding the factors that influence students' decisions in choosing educational institutions to evaluate and enhance a school's appeal. The research aims to analyze the interest level of students who chose MTs Mambaul Ulum based on location, school quality, facilities, and tuition fees using the Naive Bayes algorithm. A quantitative approach was employed by collecting data through online questionnaires distributed to students enrolled in the 2023–2025 academic years. Using Slovin’s formula with a 10% margin of error, a sample size of 67 respondents was drawn from a population of 200 students. The analytical stages included data transformation into Likert scales, validity and reliability testing, data discretization into low, medium, and high categories, Naive Bayes modeling, and model evaluation via 10-Fold Cross-Validation in RapidMiner. The findings revealed that the high category dominated the variables of location (62.69%), quality (61.19%), facilities (53.73%), and interest (58.21%), whereas tuition fees were mostly in the medium category (49.25%). Evaluation of the Naive Bayes model yielded an average accuracy of 61.67% ± 17.33%, with the highest precision in the low category (85.71%) and a low precision of 31.25% in the medium category. The implication of this study provides insight for MTs Mambaul Ulum regarding student interest characteristics while indicating that although the Naive Bayes algorithm is applicable for interest classification, its performance remains limited, particularly in identifying the medium category.
Co-Authors Nugroho Adhi Santoso R. Bangkit Indarmawan N.