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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Sistem dan Informatika Jurnal Eksplora Informatika Jurnal Abdi Insani JUKI : Jurnal Komputer dan Informatika Jurnal Locus Penelitian dan Pengabdian Jurnal Indonesia Sosial Sains Eduvest - Journal of Universal Studies Kohesi: Jurnal Sains dan Teknologi Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fatonah, Nenden Siti
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Journal : INOVTEK Polbeng - Seri Informatika

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Predicting Technical Intern Training Program Trainee Success: A Comparative Machine Learning Analysis For Risk Mitigation Maulana, Syaban; Fatonah, Nenden Siti; Firmansyah, Gerry; Widodo, Agung Mulyo
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/1r93bf26

Abstract

Japan's demographic crisis has increased demand for the Technical Intern Training Program (TITP). However, for Sending Organizations (SOs) in Indonesia, this process carries high financial risk due to an upfront talent funding scheme, where significant costs (up to IDR 35,000,000) are paid in advance. Trainee failure (dropouts or runaways) leads to substantial bad debt. This research aims to develop and validate a robust machine learning model for risk mitigation. We compare XGBoost and Random Forest on a dataset of 784 historical trainee records, characterized by extreme class imbalance (75.5% majority class). To address prior methodological weaknesses and prevent data leakage, we implement a 10-fold stratified cross-validation pipeline incorporating StandardScaler and SMOTE. The results show XGBoost (mean macro F1-Score: 0.5470 ± 0.15) significantly outperforms Random Forest (mean macro F1: 0.5098 ± 0.15), which is confirmed as statistically significant (p=0.0384) by a paired t-test. Furthermore, SMOTE is validated as a superior imbalance strategy compared to class_weight (p=0.0076). SHAP analysis identified 'contract duration' and lifestyle factors (e.g., 'alcohol consumption') as key predictors. The final model effectively predicts 'Runaway' cases (F1=0.533) but struggles with 'Training Dropouts' (F1=0.170), indicating a key limitation and a need for temporal features in future work.
Co-Authors Akbar, Habibullah Asianto, Andriyanti Aulia, Anisa BUDI TJAHJONO Dandy Pramana Hostiadi Diah Aryani, Diah Dodo, La Dwi Pamungkas, Eric Eva Hariyanti Fajar Astuti Hermawati Farhana, Sajida Fathan Ali, Dudy Firmansyah, Gerry Gajah, Sulastri Haryoto, Iin Sahuri KHOIRUL ANAM Kusuma, Galih Adhi Mangambali, Hermawan Ali Maulana, Syaban Mohammad Yazdi Pusadan Noviandi Noviandi, Noviandi Pamungkas, Ryan Tri Putri, Rani Aprillya Rahmawati, Wina Rosya, Ernalinda Sari, Yunita Maulidia Setiaji, Willian Rossa Tangkawarow, Irene Widodo, Agung Mulyo Widodo, Agung Mulyono Yusvita, Ferdania