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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) BASKARA: Journal of Business and Entrepreneurship Easta Journal of Innovative Community Services Jurnal Multidisiplin West Science Jurnal Adijaya Multidisiplin Indonesian Journal of Advanced Research (IJAR) Journal of Computing Theories and Applications Jurnal Literasi Digital
Yanuar Agung Fadlullah
Universitas Negeri Yogyakarta
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics 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.
Digital-Era Entrepreneurship Readiness: The Influence of Financial Literacy, Digital Creativity, and Technology-Based Learning among University Students Muhammad Kholili Abdul Wahhab; Kiromim Baroroh; Yanuar Agung Fadlullah
BASKARA : Journal of Business and Entrepreneurship Vol. 8 No. 1 (2025): BASKARA: Journal of Business and Entrepreneurship
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54268/baskara.v8i1.27468

Abstract

Despite the fact that tremendous opportunities for online business have been made available as a result of the rapid development of technology, student participation is still extremely low. This study aims to analyze the effect of financial literacy, digital creativity, and technology-based learning on the readiness of students at the Faculty of Economics and Business, Universitas Negeri Yogyakarta, in managing online businesses using a mixed-methods approach with an explanatory sequential design. The quantitative phase involved 348 respondents selected from a population of 2,628 students from the 2021–2023 cohorts through proportionate stratified random sampling and was analyzed using multiple regression with SPSS 27.0. The qualitative phase was conducted to clarify and provide deeper insights into the quantitative results. The findings reveal that financial literacy (t = 5.946, p < 0.05) and digital creativity (t = 7.708, p < 0.05) have a significant and positive influence on student readiness, whereas technology-based learning shows no significant effect (p = 0.212). Together, the three variables contribute 44% to student readiness, while other factors beyond this model influence 56%. The qualitative analysis emphasizes that financial literacy strengthens decision-making, while digital creativity fosters innovation; however, technology-based learning is still perceived as less applicable. These findings underscore the importance of financial literacy and digital creativity in equipping students to manage online businesses, while the effectiveness of technology-based learning requires further evaluation to enhance its impact
Co-Authors Amir Hamzah bin Sofhi @ Subhi Annisa 'Amali Shoolihah Arfin Yogatama Asma’ Khoirunnisa’ Bagus Putra Setiyawa Bagus Putra Setiyawan Bartolomeus Nori Narendra Darmono Dian Nurdiana Gilang Adi Prasetyo Hanifah Mar’atush Shalihah Khakam Ma&#039;ruf Khakam Ma'ruf Khakam Ma’ruf Kiromim Baroroh Muhamad Riyan Maulana Muhammad Kholili Abdul Wahhab Priyanto, Priyanto Putu Sudira Rokhmad Syarifuddin Sahid Ramandhani Sugiri Sugiri Surono Surono Yosef Budiman