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All Journal International Journal of Management Science and Information Technology (IJMSIT) INOVTEK Polbeng - Seri Informatika
Maulana, Syaban
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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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.
Tableau-Based Business Intelligence Analysis For the 4P Strategy and Product Sales KPIs in Vending Machines at Minori Group Sulaeman, Lambok Rommy; Kumoro, Chowal Jundy; Maulana, Syaban; Rabbani, Davina Clarissa
International Journal of Management Science and Information Technology Vol. 5 No. 2 (2025): July - December 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v5i2.5979

Abstract

This study aims to analyze the application of Business Intelligence (BI) using Tableau in supporting data-driven decision making in the marketing mix strategy (4Ps) and Key Performance Indicators (KPIs) for vending machine product sales at Minori Group. With the increasing need for efficiency and competition in the South Cikarang industrial area, Minori Group faces challenges in integrating marketing and sales data scattered across various sources. The research method used is a descriptive quantitative approach with the following stages: data collection from vending machine applications, data cleaning using Excel, integration into Tableau for visual analysis, interpretation of visualization results, and preparation of strategic recommendations. The results show that visualization with Tableau is able to provide a comprehensive picture of product performance based on the aspects of Product, Price, Place, and Promotion. The main findings show that inconsistent product placement per slot (BIN) causes sales analysis to be less accurate, while KPI calculations reveal significant variations in performance between products. Tableau has proven to be effective in helping management identify sales patterns, high-performing products, and areas that need to be optimized through promotional strategies and slot rearrangement. The integration of Marketing Mix theory, Business Intelligence, and Technology Acceptance Model (TAM) confirms that Tableau not only serves as a visualization tool, but also as a strategic platform in building a data-driven organizational culture. The implementation of BI through Tableau is expected to increase marketing effectiveness, operational efficiency, and company profitability.
Co-Authors Fatonah, Nenden Siti Gerry Firmansyah Kumoro, Chowal Jundy Rabbani, Davina Clarissa Sulaeman, Lambok Rommy Widodo, Agung Mulyo