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All Journal Jurnal Ilmiah Desain & Konstruksi Jurnal Bahan Alam Terbarukan Jurnal Jarkom Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik JURNAL REFORMASI ADMINISTRASI Jurnal Ilmiah untuk Mewujudkan Masyarakat Madani Musamus Journal of Civil Engineering Fairness and Justice: Jurnal Ilmiah Ilmu Hukum REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Stock Peternakan Jurnal Otoranpur IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Journal of Computer Science and Research MULTIPLE: Journal of Global and Multidisciplinary
Eko Budianto
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Journal : Journal of Computer Science and Research

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Model Predictive Analysis of Performance in Training and Course Institutions Using Naive Bayes and K-Means Clustering Eko Budianto; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 1 (2025): Jan: Computer Science
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65126/jocosir.v3i1.68

Abstract

The performance of course and training institutions (LKP) is a crucial factor in determining the quality of non-formal education in Indonesia. Performance assessments are currently conducted manually using the National Accreditation Board for Non-Formal Education (BAN-PNF) assessment instrument, which is time-consuming and prone to subjectivity. This research aims to develop a predictive analysis model for the performance of course and training institutions using a combination of the Naive Bayes and K-Means Clustering methods. The K-Means Clustering method is used to group institutions based on similar characteristics across key variables such as trainers, infrastructure, curriculum, management, and graduate outcomes. These clustering results are then used as additional features for the Naive Bayes classification model to predict performance categories (high, medium, or low). Testing of 150 institutions' data showed a predictive accuracy of 89.2%, with three main clusters representing high-, medium-, and low-performing institutions. This model has the potential to become a data-driven tool for governments and institutions to conduct performance evaluations quickly, objectively, and adaptively to changes in training data.
Co-Authors . Zulfan Agus Yulianto Andika, Gilang Pati Asfar, A.M.Irfan Taufan Asfar, Andi Muhamad Iqbal Akbar Barus, Efriansyah Putra Bahari Budi Doloksaribu, Budi Budi Sudrajat Darnis, Rio Putra Dina Andayati Djarot B. Darmadi, Djarot B. Febrianti, Dina Fehr, Tommy Wilmar Firmansyah Firmansyah Guntoro, Eko Joko Harapan Tua R. F. S Ika Devy Pramudiana Imron, Akhmad Ali Jamlean, Mario Christian Marcel Jeanne Dewi Damayanti Khairul Kristanto, Rudi Mega Novianti, Mega Moch. Agus Choiron Muhammad Hasanuddin, Muhammad Muhammad Iqbal Mukhsen, Muh Iqbal Putri, Gina Liwi Rahman, Fadlan Fadlul Rahmat, Ilham Noviari Rizaldi, Fakhri Rr.Yuliana Rachmawati K, Rr.Yuliana Ruswandi Tahrir Salsabilla, Salsabilla Sri Kamariyah Wigandi, Dikdik Permana