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Bambang Sutejo
Universitas W.R Supratman
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Risk Management Analysis in Tobacco Supply Chain Using the House of Risk Method Rini Oktavera; Kurniawan, Moch. Rosi; Saraswati, Rahayu; Sutejo, Bambang
Journal of Applied Science, Engineering, Technology, and Education Vol. 4 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (511.223 KB) | DOI: 10.35877/454RI.asci844

Abstract

Many risks affect the smooth flow of the supply chain, thereby necessitating numerous efforts to improve supply chain management by preventing and resolving these occurrences. Therefore, the purpose of this research was to determine the sources of possible and priority risks in the tobacco supply chain in Probolinggo Regency and design appropriate priority management strategies. This research investigated the flow of the tobacco supply chain and identified various possible risks using the Failure Modes and Effect Analysis (FMEA) method, which analyzes the impact or severity and chance of occurrence. A risk event was conducted in the plan and source process to determine the type of risk and identify the priority agents that can be reduced by the House of risk (HOR) approach. Subsequently, this research obtained 11 risk events and 20 agents, including 4 priorities for handling, alongside 4 coping strategies designed to address and reduce possible sources of risk in the tobacco supply chain
Benchmarking Machine Learning Models for Corporate Bankruptcy Prediction using Financial Ratios Mulyanto, Sigit; Yonia, Dwika Lovitasari; Arif, Muhammad; Sutejo, Bambang
Jurnal Ekonomi Kreatif dan Manajemen Bisnis Digital Vol 4 No 2 (2025): NOVEMBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jekombital.v4i2.1071

Abstract

Corporate bankruptcy prediction is a critical task in financial risk management, particularly under conditions of economic uncertainty and highly imbalanced datasets. This study presents a comprehensive benchmarking framework that evaluates multiple supervised learning models and a voting ensemble approach for corporate bankruptcy prediction. Using a publicly available dataset comprising 78,682 financial records from US-listed companies on NYSE and NASDAQ (1999-2018), we compare the performance of Random Forest, XGBoost, Gradient Boosting, Support Vector Machine, Decision Tree, and a Voting Classifier. Extensive preprocessing, including outlier removal, normalization, and feature selection, and cost-sensitive learning to mitigate severe class imbalance was conducted to ensure data quality. Model performance was assessed using multiple evaluation metrics such as accuracy, F1-score, and ROC AUC to account for class imbalance. Results demonstrate that the Voting Classifier, integrating Random Forest, XGBoost, and Gradient Boosting via hard voting, achieves superior overall performance with an accuracy of 93.6%, F1-score of 96.5%, and ROC AUC of 82.6%, outperforming individual models. The findings underscore the value of ensemble approaches in improving prediction robustness while addressing class imbalance challenges in financial distress forecasting. This study contributes a reproducible experimental design that can guide future research and practical implementation of learning models in corporate bankruptcy risk assessment.
Co-Authors Achmad Chusaini Agnes Pratiwi Br Damanik Bachrul Amiq Davis Febrinanto Prakasa Dedy Kunhadi Dewi, Pinky Pininta Dina Eldina Marbun Dyah Pelitawati Ellin Fitriano, Andre IGA Sri Deviyanti Kurniawan, Moch. Rosi Luthfitawati, Rosaliana Mochamad Kusaini Moh. Ainul Fais Mohammad Koirul Amin Muhammad Arif Ony Kurniawati Pramitha, Asti Rizkiana Rahayu Saraswati, Rahayu Rini Oktavera Rusli Yusuf Sari Sari Sigit Mulyanto Yonia, Dwika Lovitasari Yuyun Yuniati