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All Journal International Journal of Global Operations Research
Rohman, Aletta Divna Valensia
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Accelerated Pension Fund Calculations Using the Individual Level Premium Method and the Projected Unit Credit Method Case Study: PT. Dirgantara Indonesia Rohman, Aletta Divna Valensia; Mayaningtyas, Chibi Adinda
International Journal of Global Operations Research Vol. 5 No. 3 (2024): International Journal of Global Operations Research (IJGOR), August 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i3.325

Abstract

This paper examines the calculation of accelerated pension funds using two actuarial methods: the Individual Level Premium (ILP) method and the Projected Unit Credit (PUC) method. The case study focuses on PT. Dirgantara Indonesia. We compare the methods' impact on normal contribution amounts, actuarial liabilities, and retirement benefits. The research highlights the advantages and disadvantages of each approach, considering factors like participant age and contribution period. The findings demonstrate that the PUC method generally leads to lower normal contributions but may result in lower final retirement benefits compared to the ILP method. This study provides valuable insights for companies and employees in PT. Dirgantara Indonesia to choose the most suitable method for their accelerated pension plan, considering their financial goals and risk tolerance.
OPTIMIZATION OF PORTFOLIO PERFORMANCE ON THE JAKARTA ISLAM INDEX (JII) STOCK IN DECEMBER 2023 – MAY 204 USING THE MARKOWITZ MODEL Rohman, Aletta Divna Valensia; Fatimah, Siti
International Journal of Global Operations Research Vol. 5 No. 3 (2024): International Journal of Global Operations Research (IJGOR), August 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i3.322

Abstract

This research aims to optimize the performance of a stock investment portfolio on the Jakarta Islamic Index (JII) during the period from December 2023 to May 2024, using the Markowitz model. This model minimizes portfolio risk by considering expected returns and covariance between stocks. Historical data on stock returns in the JII during the research period will be used to calculate expected returns and covariance. Furthermore, the Markowitz model will be used to determine the optimal investment proportion for each stock in the portfolio. The results of this research are expected to provide information to JII stock investors about the optimal combination of stocks to achieve maximum returns with controlled risk.
Analysis Automobile Insurance Fraud Claim Using Decision Tree and Random Forest Method Wicaksono, Ridwan Lazuardy Bimo; Rohman, Aletta Divna Valensia
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.337

Abstract

Insurance fraud, particularly in the automobile sector, poses significant financial risks to insurance companies. This study aims to analyze fraudulent claims in automobile insurance using Decision Tree and Random Forest methods. A dataset consisting of 10,000 entries was utilized, containing variables such as vehicle type, claim amount, and claim status. The Decision Tree method was employed for its interpretability, while Random Forest was used for its superior accuracy. Results indicated that the Random Forest model outperformed the Decision Tree model, achieving an accuracy of 51.37% compared to 50.47%. This research highlights the effectiveness of machine learning techniques in detecting insurance fraud and provides insights for insurers to enhance their fraud detection systems.
Co-Authors Mayaningtyas, Chibi Adinda Siti Fatimah Wicaksono, Ridwan Lazuardy Bimo