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All Journal Parameter: Jurnal Matematika, Statistika dan Terapannya Journal of Mathematics, Computation and Statistics (JMATHCOS)
Limba, Syella Zignora
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Mathematics

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Forecasting the Inflation Rate Using Long Short-Term Memory Model Based on Consumer Price Index Limba, Syella Zignora; Hapsari, Nimas Ayu; Anggraini, Yenni; Notodiputro, Khairil Anwar; Maulifah, Laily Nissa Atul
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp537-550

Abstract

Human life is constantly exposed to risks such as illness, accidents, and death, which create financial uncertainties for individuals and families. Life insurance serves as an essential financial instrument to mitigate these risks by transferring potential liabilities to insurance companies. This study analyzes premium reserves for whole life and term life insurance using the New Jersey Prospective Method, applying a 6% interest rate and the 2023 Indonesian Mortality Table (TMPI) as the basis of calculation. Actuarial commutation functions are employed to compute annuity values, single net premiums, annual net premiums, and reserve allocations across different ages. The results indicate that reserve values increase with age, reflecting higher mortality risks, with whole life insurance showing a sharper escalation compared to term life insurance. The New Jersey Prospective Method demonstrates accuracy and consistency in reserve estimation, particularly by setting zero reserves in the first policy year, thereby supporting initial liquidity. These findings highlight the method’s effectiveness in maintaining financial stability and readiness of insurance companies to meet future claims and long-term obligations to policyholders.
Evaluation of Tree-Based Models for Predicting Social Assistance Recipient Status Based on National Socio-Economic Survey (SUSENAS) 2024 Hiola, Yani Prihantini; Zulhijrah; Putra, I Gusti Ngurah Sentana; Limba, Syella Zignora; Sartono, Bagus; Firdawanti, Aulia Rizki; Susetyo, Budi; Dito, Gerry Alfa
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xyyv0f37

Abstract

Abstract. Poverty is a major socioeconomic challenge in Indonesia that affects the effectiveness of social protection programs. In response to this challenge, the government has created social assistance programs to improve the welfare of the people. However, the distribution of social assistance is often considered to be inaccurate, resulting in households that are deemed eligible for social assistance not being identified as recipients. One solution to improve the accuracy of distribution is the application of machine learning in the context of classification. Several tree-based models, such as LightGBM, Random Forest, and XGBoost, were selected because of their superior capabilities compared to classical models such as logistic regression, especially in handling complex data and fulfilling model assumptions. This study compares the performance of these three models in predicting social assistance recipient status using data from the 2024 West Java Provincial National Socioeconomic Survey (SUSENAS). Model evaluation was conducted on several data pre-processing scenarios involving outlier handling, class balancing, and feature engineering. The results show that LightGBM consistently outperforms the other models on six metrics, namely Accuracy, Balanced Accuracy, F1-Score, ROC-AUC, PR-AUC, and Brier Score, out of a total of eight evaluation metrics used. SHAP analysis identifies Social Assistance History and Asset Score as the most influential features for model prediction. Friedman and Nemenyi nonparametric tests confirmed significant performance differences between LightGBM and other models based on the F1-Score, PR-AUC, and Brier Score metrics. These findings indicate that tree-based models, particularly LightGBM, can support the development of a more targeted and data-driven social assistance targeting system. Keywords: Social Assistance; Tree-Based; SHAP; SUSENAS; Hybrid Bayesian Optimization
Co-Authors Anggraini, Yenni Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Dito, Gerry Alfa Hapsari, Nimas Ayu Hiola, Yani Prihantini Maulifah, Laily Nissa Atul Putra, I Gusti Ngurah Sentana Zulhijrah