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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Statistika dan Aplikasinya Parameter: Journal of Statistics Premium: Insurance Business Journal Jurnal Pengabdian Masyarakat Bangsa RENATA Jurnal Pengabdian Masyarakat Kita Semua Indonesian Journal of Applied Mathematics Jurnal KALANDRA TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Ulil Albab
Dwi Mahrani
Actuarial Science Study Program, Faculty Of Science, Institut Teknologi Sumatera, Indonesia
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Comparison of Mack Chain-Ladder and Bootstrap Methods for Claim Reserve Estimation under IFRS 17 in Lampung General Insurance Tiara Yulita; Putri Isnaini Cahyaning Baiti; Dila Tirta Julianty; Ayu Sofia; Dwi Mahrani; M. Naufal Athaatmaja
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.38485

Abstract

Claim  reserves are funds set aside by insurance companies to pay for reported claims (RBNS) as well as claims that have not yet been reported (IBNR), and they are crucial because they directly affect the financial health of the company. In 2024, there were customer complaints in Lampung regarding delays in claim payments by general and life insurance companies. Therefore, this study uses claim data from general insurance companies in Lampung for the period 2013–2024. The novelty of this study lies in comparing the Mack Chain Ladder analytical method and the Bootstrap simulation method for estimating claim reserves within the IFRS 17 framework using regional insurance data from Lampung, which has not been widely explored in previous studies. This study aims to estimate claim reserves and estimate the Liability for Incurred Claims (LIC),The objective of this study is to compare claim reserve values using an analytical approach (Mack Chain-Ladder) and a simulation approach (Bootstrap), implemented in accordance with the International Financial Reporting Standard (IFRS) 17. The IFRS 17 components to be calculated include the Liability for Incurred Claims (LIC) , Best Estimate Liability (BEL), and Risk Adjustment (RA) under IFRS 17. . Accurate estimation of claim reserves and the implementation of IFRS 17 play a vital role in ensuring the sustainability of insurance companies. The Mack Chain-Ladder (MCL) method is used to obtain equations for the expected value and variance of future claims as well as the prediction error rate. Meanwhile, the Bootstrap method generates numerous simulated claim datasets that reflect various possible scenarios. The advantage of the simulation approach is its ability to provide a full predictive distribution, which can be used to estimate the risk adjustment under IFRS 17. The empirical results show that the estimated claim reserve using the Mack Chain-Ladder (MCL) method is 234,740,644, while the Bootstrap method with 5.000 simulations produces a reserve range ofIn addition, this study also discusses methods for calculating capital requirements based on Value at Risk and for estimating risk adjustment using risk measures applied to the simulated distribution of claim liabilities over the contract period. 233,158,004-236,320,156. These results provide empirical insights into claim reserve estimation and support the implementation of IFRS 17 in regional insurance companies by calculating the BEL, RA, and LIC values, whose results are based on the claim reserve calculation .
Actuarial Evaluation of Additional Contributions in Early Retirement Programs Using the Spreading Gains and Losses Method Dwi Mahrani; Miftha Ulya Nazima; Ayu Sofia; Tiara Yulita
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28726

Abstract

This study examines the actuarial and funding implications of accelerated retirement in a defined benefit pension scheme by integrating the Projected Unit Credit (PUC) method with the Spreading Gains and Losses approach. While both methods are widely applied in pension valuation, limited empirical evidence evaluates their combined implementation under retirement age acceleration scenarios, particularly in Indonesian public sector schemes. This study addresses that gap using secondary administrative employment data of 87 female civil servants obtained from the Investment and One-Stop Integrated Services Office of Lampung Province (Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Provinsi Lampung), grouped into four entry-age cohorts (22–25 years). The analysis compares normal retirement at age 58 with accelerated retirement at age 50, assuming a 5% annual effective interest rate and 8% biennial salary growth. The results indicate that, at valuation age 45, actuarial liabilities increase by approximately 49.8% under retirement at age 50 relative to age 58. The shorter discounting period and earlier benefit payments outweigh the reduced contribution period, resulting in the emergence of Unfunded Actuarial Liability (UAL). The resulting Past Service Liability (PSL) is amortized over five years, requiring additional contributions ranging from IDR 27.06 million to IDR 82.05 million across entry-age groups. These findings highlight the high sensitivity of pension funding to retirement age assumptions and emphasize the importance of actuarial impact assessments prior to policy implementation. However, the deterministic framework and relatively small sample size limit broader generalization of the results.
Comparative Modeling of Pineapple Production Using Gaussian GLM and Random Forest Regression Radot MH Siahaan; Indah Gumala Andirasdini; Fuji Lestari; Dwi Mahrani; Amalia Listiani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28721

Abstract

This study aims to conduct a comparative modelling of pineapple production at PT Great Giant Pineapple (GGP) using Gaussian GLM as parametric statistical approach and Random Forest Regression method as machine learning based on monthly data from 2014 to 2022. Multicollinearity testing and distribution fitting were conducted to validate the Gaussian assumption. For the Random Forest Regression, hyperparameters were optimized by tuning the number of trees (ntree) and the number of predictors at each split (mtry) with model stability evaluated using Out-of-Bag (OOB) error. The Gaussian GLM achieved a MAPE of 8.41% (R² = 0.106) for the GP3 clone and 11.27% (R² = 0.149) for the F180 clone. Random Forest Regression produced a testing MAPE of 9.28% (R² = 0.144) for GP3 and 12.11% (R² = 0.105) for F180. While both models achieved low prediction error based on MAPE, they differed in identifying influential variables and showed limited explanatory power as indicated by low R² values. The Gaussian GLM identifies air pressure as significant for both clones and rainfall for F180 clone, while Random Forest consistently identifies rainfall as the most influential predictor. These findings confirm the complementary strengths of parametric and machine learning approaches in supporting climate-based production planning and risk mitigation.
Segment Risks Based on Age, Wages, Working Hours, and Claim Costs using K-Medoids Clustering Agus Irawan; Dwi Mahrani; Aldila Nur Indah Berliana Ratam; Marisa Marisa
ULIL ALBAB : Jurnal Ilmiah Multidisiplin Vol. 5 No. 7: Juni 2026
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/jim.v5i7.17771

Abstract

Occupational risk plays a crucial role in employment insurance management due to its direct impact on claim frequency and the sustainability of insurance systems. However, workers’ risk levels are heterogeneous and influenced by factors such as age, wages, working hours, and claim costs. This study aims to determine the optimal number of clusters and analyze worker risk segmentation using the K-Medoids Clustering method. Secondary data, obtained from Kaggle and consisting of 54,000 observations, were used. From this dataset, 300 samples were selected using the Slovin formula. The analyzed variables included age, weekly wages, hours worked per week, initial claim cost, and final claim cost. Prior to clustering, the data were standardized using a robust scaler and tested for multicollinearity. The optimal number of clusters was determined using the Silhouette Coefficient method. The results indicated that the optimal clustering structure consisted of three clusters, with a Silhouette Coefficient value of 0.703. These clusters represented low-risk, medium-risk, and high-risk worker groups. The findings offer valuable insights for insurers to enhance risk segmentation, claims management, and more targeted premium policy formulation.
Pemanfaatan Looker Studio untuk Mengembangkan Kompetensi Analisis dan Visualisasi Data Siswa SMKS Nurul Huda Pringsewu Rosni Rosni; Nora Madonna; Andi Fitriawati; Dani Al Mahkya; Agus Irawan; Erica Grace Simanjuntak; Ma’rufah Hayati; Nasrullah Nasrullah; Miftahul Irfan; Dwi Mahrani; Ayu Sofia; Tiara Yulita; Muklas Rivai
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 1 (2026): Maret
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v4i1.4194

Abstract

Kemampuan dalam memanfaatkan teknologi, khususnya dalam eksplorasi dan visualisasi data, menjadi tantangan signifikan dalam dunia pendidikan di era digital saat ini. SMKS Nurul Huda Pringsewu sebagai mitra kegiatan pengabdian menunjukkan adanya keterbatasan kompetensi siswa dalam mengolah data dan menyajikannya dalam bentuk visual yang informatif. Menanggapi permasalahan tersebut, tim Pengabdian kepada Masyarakat (PkM) melaksanakan pelatihan menggunakan aplikasi Looker Studio untuk eksplorasi data dan pembuatan dashboard statistik. Looker Studio merupakan platform berbasis web yang memudahkan pengguna dalam mengolah data numerik dan menyajikannya secara visual, sehingga proses pembacaan data tidak lagi dilakukan secara manual. Pelatihan ini bertujuan untuk meningkatkan kemampuan siswa dalam mengolah dan memvisualisasikan data menggunakan teknologi terkini. Hasil dari kegiatan ini menunjukkan peningkatan kompetensi siswa dalam mengeksplorasi data statistik serta peningkatan kualitas pembuatan dashboard yang lebih informatif dan menarik. Kegiatan ini diharapkan dapat menjadi langkah awal dalam membekali siswa dengan keterampilan digital yang relevan dengan kebutuhan industri saat ini.
Co-Authors Agus Irawan Agus Irawan Agus Irawan Aldila Nur Indah Berliana Ratam Alfarizi , Mhd Rayhan Jovely Alfian, Muhammad Nuril Andi Fitriawati Andi Fitriawati Ayu Sofia Ayu Sofia Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Dani Al Mahkya Dani Al Mahkya Dewi, Karina Sylfia Dila Tirta Julianty Dila Tirta Julianty Edward Al Faruq Purba Erica Grace Simanjuntak Fitriawati , Andi Fuji Lestari Fuji Lestari Gustina K. S, Annisa Hevita Indah Gumala Andirasdini Irfan, Miftahul Irwan, Sri Efrinita Lestari, Yushinta Cahya Listiani, Amalia Listriani, Amalia M. Naufal Athaatmaja Madonna, Nora Marisa Marisa Marisa Marisa Ma’rufah Hayati Miftahul Irfan Miftha Ulya Nazima Mt, Ma’rufah Hayati Muklas Rivai Nasrullah Nasrullah Nasrullah Nora Madonna Pribadi, Aswan Anggun Putri Isnaini Cahyaning Baiti Radot MH Siahaan Rifky Fauzi Rivai, Muklas Rosni Rosni Rosni, Rosni Siahaan, Marco Marolop Simanjuntak, Erica Grace Siregar, Abian Avisena Syafitri, Nabilah Theo Markus Tiara Shofi Edriani Tiara Yulita Wulandari , Winda Yulita, Tiara