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All Journal Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan
Amal, Muhammad Ikhlashul
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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OPTIMIZING DEFINED BENEFIT PENSION PLAN FUNDING: COMBINING ENTRY AGE NORMAL METHOD AND SINGLE SALARY APPROACH Ekasasmita, Wahyuni; Rahmi, Nur; Tunnisa, Khaera; Amal, Muhammad Ikhlashul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1553-1564

Abstract

The sustainability of defined benefit pension plans relies heavily on effective funding strategies. This study aims to develop an optimized funding strategy for Defined Benefit Pension Plans by integrating the Entry Age Normal (EAN) method with the Single Salary Approach (SSA). The Entry Age Normal method provides a systematic way to distribute the cost of pension benefits over the career of employees, ensuring long-term stability. Meanwhile, the Single Salary Approach simplifies salary projections, making it easier to manage fund contributions while accounting for future wage inflation. To evaluate the effectiveness of this integrated approach, we conducted a case study using salary and pension fund data collected from internal records at Institut Teknologi Bacharuddin Jusuf Habibie (ITH), a higher education institution. Through a series of simulations and sensitivity analyses, we demonstrate that integrating these methods not only minimizes funding volatility but also improves the accuracy of pension liabilities estimation. For instance, at age 25.83, the actuarial liability is Rp 38,929,501, reflecting a relatively low liability at a younger age. As employees approach retirement, the liability increases significantly. At age 47.17, the liability reaches Rp 191,823,284, demonstrating the impact of salary growth and length of service on future benefits. Additionally, for the same age of 25.83, the actuarial liability under SSA-EAN is Rp 37,980,001, which is slightly lower than the EAN estimate. Pension benefits projected under SSA-EAN are also slightly lower than those under EAN, indicating potential cost savings. The findings provide a viable framework for pension plan administrators seeking to achieve both financial sustainability and predictability in managing pension obligations. By integrating SSA with EAN, this study offers a practical solution that addresses key challenges in the actuarial valuation of defined benefit plans, ensuring more stable and predictable pension funding.
Modeling tuberculosis in children under five using poisson and negative binomial regression Fajri, Ahmad; Rahmi, Nur; Maharani, Putri Ayu; Amal, Muhammad Ikhlashul
Desimal: Jurnal Matematika Vol. 7 No. 2 (2024): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v7i2.23464

Abstract

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Indonesia is the country with the second highest number of tuberculosis cases after India. The Ministry of Health stated that there has been a significant increase in cases of tuberculosis among children in Indonesia where the increase in cases of tuberculosis among children has reached more than 200 percent. The number of TB cases can be reduced if the factors that affect the number of TB patients are known. Therefore, efforts should be made to model the number of cases of Tuberculosis among children under five years of age to provide useful information to prevent and control Tuberculosis. The relationship between these factors and the number of people with Tuberculosis can be determined using Poisson regression analysis because the number of cases of Tuberculosis is calculated data. Tuberculosis data contain overdispersion, so another approach is used to overcome it, which is by using a negative binomial regression model. The best model obtained based on the AIC value is the Negative Binomial regression model with an AIC value of 184.095. For further research, it is suggested to test the spatial effect and modeling using the Negative Binomial geographic weighted regression method to find out whether the characteristics of one region and the other influence the geographic location on the model.
Co-Authors Ahmad Fajri Khaera Tunnisa, Khaera Maharani, Putri Ayu Raden Mohamad Herdian Bhakti Wahyuni Ekasasmita