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All Journal EduMatika: Jurnal MIPA Economic: Journal Economic and Business
Risca Octaviyani Hutapea
Universitas Negeri Medan
Chemistry Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Physics

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Integrating garch, markowitz mean–variance, and lstm for risk, volatility, and portfolio analysis of four major Indonesian banking stocks 2015 – 2024 Risca Octaviyani Hutapea; Gizka Triyunita Sinaga; Ardicha Appu Sianturi
Economic: Journal Economic and Business Vol. 5 No. 1 (2026): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v5i1.1401

Abstract

This study examines the risk structure, volatility behavior, and optimal portfolio construction of four major banking stocks in Indonesia BBCA, BBRI, BMRI, and BBNI during the 2015–2024 period through the integration of the GARCH(1,1) model, Markowitz Mean–Variance optimization, and Long Short-Term Memory (LSTM)-based forecasting. Daily closing price data is transformed into log returns and tested for stationarity before further analysis. The GARCH estimation results indicate persistent high volatility across all stocks (?+? close to 1), with BBNI and BBRI the most responsive to market shocks, while BBCA remains the most stable stock. Markowitz optimization produces a Minimum Variance portfolio dominated by BBCA, while the Maximum Sharpe portfolio allocates funds entirely to BBCA due to its superior return efficiency. The LSTM is able to represent price trends well, as evidenced by low prediction error values for BBRI and BBNI and accuracy between 58–61 percent. The integration provides a comprehensive analytical framework for understanding changing market risk dynamics and supporting adaptive investment decision-making in the Indonesian banking sector. These findings confirm that the hybrid approach can improve risk mapping while maximizing portfolio performance through a combination of historical information, dynamic volatility, and price trend predictions.
Integration of survival analysis in predicting customer churn risk to optimize life insurance redemption value formulation Fachriz Effendy K; Risca Octaviyani Hutapea; Ardicha Appu Sianturi
Economic: Journal Economic and Business Vol. 5 No. 2 (2026): ECONOMIC: Journal Economic and Business
Publisher : Lembaga Riset Mutiara Akbar (LARISMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/ejeb.v5i2.1580

Abstract

The risk of voluntary policy surrender is a major threat to the liquidity and stability of life insurance companies' premium reserves. Conventional actuarial valuations, which assume a static depreciation rate, often fail to accurately mitigate this risk. This study aims to integrate a predictive analytics approach into actuarial mathematics to create a dynamic Surrender Value formula. Using 10,000 historical observations of financial customers as a proxy, the analysis was conducted using the Kaplan-Meier estimator and Cox Proportional Hazard (Cox PH) regression. The Kaplan-Meier estimation results show that the probability of policy survival experiences an exponential decay from 95.8% in the first year to 80.2% in the fifth year. Cox PH modeling confirms that entry age (hazard ratio = 1.048) and female gender (hazard ratio = 1.535) significantly increase the surrender risk, while active customer interaction (hazard ratio = 0.589) acts as a protective factor. The resulting cumulative individual hazard probabilities are then integrated as weighting constants into the surrender charge formula. This integration produces penalty recommendations that adapt to each policyholder's risk profile, providing more proportional and equitable liquidity protection for insurance company operations.
Analysis of Book Production Quality Using Ewma Control Chart: Comparison of Alpha Parameters, Trend Prediction, and Production Correlation Amelia Putri; Aulia Hani; Anatasia Faradhilla; Risca Octaviyani Hutapea
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1106

Abstract

In the printing industry, maintaining product quality is a critical factor for ensuring customer satisfaction and competitive advantage. This study applies the Exponentially Weighted Moving Average (EWMA) control chart to monitor and control the quality of book production at CV Renjana Offset. Using secondary data from November 2023 to July 2024, the analysis focuses on the percentage of rejected products as a key indicator of quality performance. The EWMA method, with a smoothing constant of ? = 0.3, effectively highlights small shifts in the production process. Results show that all EWMA values lie within the control limits (UCL = 22.54%, LCL = 0.00%), indicating the process is statistically under control. Furthermore, comparison of different alpha values demonstrates the trade-off between sensitivity and stability. A moderate negative correlation (r = -0.505) between production volume and reject percentage suggests increased efficiency at higher production scales. The predicted reject percentage for August 2024 is 4.74%, indicating process stability. Overall, EWMA proves to be a valuable tool in continuous quality monitoring and data-driven decision-making in book production.
Analysis of the Most Dominant Causing Factors of Divorce in 34 Provinces in Indonesia Using the XGBoost Algorithm Seila Amalia; Riski Melanton Banjarnahor; Risca Octaviyani Hutapea; Gabriel Fernando Sitorus; Gracia Domini; Arnita Arnita
EduMatika: Jurnal MIPA Vol. 5 No. 2 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i2.1135

Abstract

Divorce in Indonesia shows a significant increasing trend and has a broad social impact. This study aims to identify the most dominant causes of divorce in 34 provinces in Indonesia using the Extreme Gradient Boosting (XGBoost) machine learning algorithm. Secondary quantitative data from the Central Bureau of Statistics in 2024 were analyzed by pre-processing, data sharing, model training, and performance evaluation. The results showed that constant disputes and quarrels were the main causes of divorce, followed by substance abuse and forced marriage. The developed XGBoost model achieved 75% accuracy in classifying the level of divorce risk. These findings provide new insights into understanding the social factors that influence divorce and can be the basis for designing more effective prevention strategies.
Modeling inflation in indonesia using the adf stationarity test, garch, and random forest regression based on monthly consumer price index data from 2014 to 2024 Ardicha Appu Sianturi; Risca Octaviyani Hutapea; Gizka Triyunita Sinaga; Enjelita Simangunsong
EduMatika: Jurnal MIPA Vol. 5 No. 3 (2025): EduMatika: Jurnal MIPA
Publisher : Lembaga Riset Mutiara Akbar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56495/emju.v5i3.1426

Abstract

Inflation is one of the key macroeconomic indicators that reflects price stability and overall economic conditions of a country. This study aims to examine the characteristics, volatility, and forecasting of monthly inflation in Indonesia over the period 2014–2024 by employing descriptive statistical analysis, time series approaches, and machine learning methods. The descriptive results indicate that Indonesia’s inflation has an average rate of 1.71% with a standard deviation of 1.32, a minimum value of 0.04%, and a maximum value of 8.36%. The inflation distribution is asymmetric, as evidenced by a positive skewness of 1.88 and a kurtosis value of 5.15, suggesting the presence of extreme inflation spikes during certain observation periods. Time series analysis reveals that the inflation data do not fully satisfy the stationarity assumption at the level form, thus requiring differencing prior to further modeling. Volatility estimation using the GARCH model indicates the existence of inflation volatility persistence. Furthermore, forecasting results using the Random Forest method demonstrate that the model is able to closely track actual inflation patterns under normal conditions, although predictive accuracy declines during periods of heightened volatility. Overall, this integrated approach provides a more comprehensive understanding of the dynamics of inflation in Indonesia.
Co-Authors Amelia Putri Anatasia Faradhilla Ardicha Appu Sianturi Arnita Arnita Aulia Hani Enjelita Simangunsong Fachriz Effendy K Gabriel Fernando Sitorus Gizka Triyunita Sinaga Gracia Domini Riski Melanton Banjarnahor Seila Amalia