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All Journal Jurnal Pendidikan Vokasi Cakrawala Pendidikan JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Journal of Education and Learning (EduLearn) ELINVO (Electronics, Informatics, and Vocational Education) Jurnal Pendidikan (Teori dan Praktik) Martabe : Jurnal Pengabdian Kepada Masyarakat ILKOMNIKA: Journal of Computer Science and Applied Informatics Journal of Computing Theories and Applications
Priyanto, Priyanto
Universitas Negeri Yogyakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Social Sciences Other

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Explainable Machine Learning for Predicting Indonesian Vocational School Accreditation: Geographic Validation, Probability Calibration, and Subgroup Auditing Muhamad Riyan Maulana; Putu Sudira; Priyanto Priyanto; Yanuar Agung Fadlullah; Dian Nurdiana; Amir Hamzah bin Sofhi @ Subhi
Journal of Computing Theories and Applications Vol. 4 No. 2 (2026): JCTA 4(2) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17721

Abstract

Vocational school accreditation is a high-stakes institutional classification, yet the geographic transferability and probability reliability of models based on routinely collected administrative data remain underexplored. This study developed a reproducible, leakage-controlled machine-learning framework for classifying the recorded accreditation classes C, B, and A among Indonesian vocational schools. Its main contribution is an integrated evaluation design that combines province-disjoint validation, probability calibration, cluster-aware uncertainty, and subgroup auditing to test reliability beyond conventional random-validation performance. A cross-sectional dataset containing 149,376 program-level records was deterministically aggregated into 14,391 schools, including 14,134 with valid labels. Eight provinces comprising 1,536 schools were locked before feature selection, model comparison, tuning, and calibration; 12,598 schools from 31 provinces supported development. The selected CatBoost model used 35 structural and vocational features with grouped out-of-fold temperature scaling. On the locked holdout, the calibrated model achieved balanced accuracy of 0.586, Macro F1 of 0.533, quadratic weighted kappa of 0.499, Macro ROC-AUC of 0.757, and expected calibration error of 0.049. School size, teacher resources, vocational staffing, program diversity, and status were most in-fluential. Class B had the lowest recall, and performance varied across school groups and provinces. The framework supports calibrated, auditable preliminary screening, but not automated accreditation or replacement of professional assessors.
Co-Authors Ahmad Chafid Alwi Akbar Permana Putra Amir Hamzah bin Sofhi @ Subhi Andy Ferry Rahman Apri Nuryanto Arif Ainur Rafiq Bunga Putri Maulia Dian Nurdiana Dwi Rahdiyanta Herman Dwi Surjono Herminarto Sofyan Ibnu Hartopo Irma Nuur Rochmah Kadek Cahya Dewi Linda Fitri Pertiwi Mahdiyah Mahdiyah Masduki Zakaria Masduki Zakarijah Mochamad Bruri Triyono Moh Alif Hidayat Sofyan Muhamad Riyan Maulana Muhammad Indra Muhammad Irfan Luthfi Muhammad Trio Maulana Putra Muslikhin Nalurita Absari Pardjono, P. Ponco Walipranoto Priyo Nugroho Adi Putu Indah Ciptayani Putu Sudira Ratna Wardani Saputro, Eko Prasetyo Nugroho Sudji Munadi Tika Widari Tinesa Fara Prihandini Yanuar Agung Fadlullah Yoga Sahria