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All Journal Journal of Embedded Systems, Security and Intelligent Systems Journal of Vocational, Informatics and Computer Education Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal of Digital Technology and Computer Science Innovation and Applied Education Journal
Aprilianti Nirmala S
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Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Social Sciences Other

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Journal : Journal of Digital Technology and Computer Science

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KNN Vs Naive Bayes: An Innovative Comparison in Predictive AI Learning With Association Data Support Jannah, Devi Miftahul; Aprilianti Nirmala S
Journal of Digital Technology and Computer Science Vol. 3 No. 1 (2025): November 2025
Publisher : Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/digitech.v3i1.20251

Abstract

This study analyzes how Naive Bayes and K-Nearest Neighbor (KNN) predict learning outcomes based on artificial intelligence (AI). The main focus of this study is the difficulty of algorithms in handling complex learning data and the contribution of Association Rule Mining (ARM) attribute features in improving prediction accuracy. The methods applied include two classification algorithms (KNN and Naive Bayes) in an exploratory-comparative quantitative research design, as well as the application of ARM to uncover hidden patterns among variables using the apriori algorithm. Data for 368 students with prior experience in artificial intelligence technology was collected through an online survey. Although KNN outperforms in recall, the study results show that Naive Bayes has higher precision. By detecting hidden correlation patterns that cannot be identified by conventional classification methods, ARM improves classification results. The discussion emphasizes that the selection of the best algorithm depends on the application's objectives, namely whether the priority is on classification accuracy or the range of relevant results. Based on these findings, a hybrid technique combining KNN, Naive Bayes, and ARM is highly recommended for creating a more efficient and accurate prediction system to support AI-based education.
Co-Authors Ahmad Luthfi Amelia Syamsuddin Andi Dio Nurul Awalia Della Fadhilatunisa Devi Miftahul Jannah Elma Nurjannah Fitri Amaliyah Batubara Ibrahim Al khalil Jannah, Devi Miftahul Kurnia Wahyu Prima La Ode Lisbar M. Miftach Fakhri Muarsiyah Waddah Muh Rezky Awal Mushaf Nur Annafiah Nur Yahya Akhmad Nurrahmah Agusnaya Nurzabrina Anugrani Pramudya Asoka Syukur Sitti Hajerah Hasyim Wahyu Amal Imran