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All Journal Journal of Applied Science, Engineering, Technology, and Education Quantitative Economics and Management Studies
Triutomo, Agung
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Other

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Cross-Sectoral Portfolio Optimization in Emerging Markets: Value at Risk Assessment of Indonesian Consumer and Financial Stocks Ahmar, Ansari Saleh; Wahyuni, Wahyuni; Triutomo, Agung; Rahman, Abdul; Tabash, Mosab
Quantitative Economics and Management Studies Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3861

Abstract

This study examines the comparative risk profiles of single-asset investments versus portfolio strategies using two prominent Indonesian companies: PT. Mayora Indah and PT. Sinar Mas Multiartha. Employing a quantitative approach with Monte Carlo simulation and Value at Risk (VaR) methodology, the research analyzed daily stock returns over a one-year period (January-December 2023). Results reveal that despite similar historical volatility levels between the individual stocks (standard deviations of 2.65% and 2.88%), their correlation coefficient was notably low (0.13), creating significant diversification opportunities. Monte Carlo simulations generated 1,000 potential return scenarios for robust risk assessment, finding that at the 95% confidence level, maximum expected losses on a Rp 100 million investment were Rp 4.78 million for PT. Mayora Indah and Rp 4.58 million for PT. Sinar Mas Multiartha individually. However, a portfolio combining both stocks (60% PT. Mayora Indah, 40% PT. Sinar Mas Multiartha) reduced this potential loss to Rp 2.90 million—representing approximately 37% risk reduction compared to either single-asset investment. This substantial risk mitigation was consistent across all confidence levels (99%, 95%, and 90%). The portfolio also demonstrated improved return characteristics in simulation (0.39% expected return) compared to historical data (0.09%), while maintaining similar risk levels. These findings provide empirical support for the practical value of diversification strategies in the Indonesian equity market, highlighting how even limited diversification across two stocks from different economic sectors can yield substantial improvements in risk-adjusted investment outcomes.
Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification Rais, Zulkifli; Fahmuddin S, Muhammad; Saida, Saida; Triutomo, Agung
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4191

Abstract

The increasing number of hypertension patients and the threat of serious complications make hypertension one of the leading causes of death worldwide. Early prevention is currently considered one of the best solutions. Early prevention through early detection can be achieved by utilizing machine learning technology. XGBoost is a machine learning algorithm based on gradient boosting machines. XGBoost applies regularization techniques to reduce overfitting and has faster execution speed as well as better performance. The objective of this research is to classify hypertension levels using the XGBoost method and leveraging hyperparameter tuning for optimization. In this study, the hyperparameter optimization technique used is gridsearchCV. The evaluation results of the XGBoost classification method using the best combination of parameters show good performance, where the XGBoost model achieves an accuracy of 93.3%, Precision of 97%, Recall of 92%, F1-Score of 93%, and AUC value of 0.935. This implies that the classification of hypertension levels in patients at Pelamonia Makassar Hospital can be well or accurately classified using the XGBoost method.
Co-Authors Abdul Rahman Ahmar, Ansari Saleh Fahmuddin S, Muhammad Rais, Zulkifli Saida, Saida Tabash, Mosab wahyuni wahyuni