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All Journal Media Statistika Kubik Journal of Science and Applicative Technology JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Cendekia : Jurnal Pendidikan Matematika Variance : Journal of Statistics and Its Applications KUBIK: Jurnal Publikasi Ilmiah Matematika Griya Journal of Mathematics Education and Application Binawan Student Journal Enthusiastic : International Journal of Applied Statistics and Data Science Smart Society: Community Service and Empowerment Journal Jurnal Pengabdian Masyarakat Bangsa RENATA Jurnal Pengabdian Masyarakat Kita Semua JPMB Parahita Indonesian Journal of Applied Mathematics Jurnal KALANDRA TeknoKreatif: Jurnal Pengabdian kepada Masyarakat
Yulita, Tiara
Institut Teknologi Sumatera
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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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.
Comparison of Pure Premiums for Motor Vehicle Insurance Using ZTP-Gamma GLM and Tweedie GLM Yushinta Cahya Lestari; Tiara Yulita; 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.v10i1.28453

Abstract

The increasing number of motor vehicles has contributed to higher traffic density and a greater risk of accidents, thereby reinforcing the importance of protection through motor vehicle insurance. Therefore, accurately determining the pure premium is essential to maintain risk balance and ensure the sustainability of insurance companies. This study employs Generalized Linear Models, which are an extension of classical linear regression that allow the response variable to follow non-normal distributions, particularly the Zero-Truncated Poisson, Gamma, and Tweedie distributions. Using motor vehicle insurance claim data from 2022 with 386 observations, this research compares two premium modeling approaches, namely the ZTP–Gamma model for estimating claim frequency and claim severity, and the Tweedie GLM for modeling total claims in the calculation of pure premiums for motor vehicle insurance. The analysis shows that the estimated pure premiums for the ZTP–Gamma GLM range from IDR 2,138,532 to IDR 19,939,391, while the estimates for the Tweedie GLM range from IDR 2,153,665 to IDR 20,936,047. The ZTP–Gamma GLM demonstrates better accuracy, with a MAPE value of 23.65% compared to 25.844% for the Tweedie GLM, resulting in an accuracy difference of 2.194%. These findings indicate that the ZTP–Gamma GLM is more effective in producing accurate pure premium estimates.
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.
ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE KALMAN FILTER DENGAN STATE SPACE MODEL SCALAR PADA PRODUK ASURANSI UMUM Chintya Carissa Manurung; Tiara Yulita; Amalia Listiani
KUBIK Vol 9 No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Department of Mathematics, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Usually, there is a delay in reporting claims from the time of the incident which results in the insurance company having a responsibility or debt. Therefore, insurance companies need to prepare funds to cover these debts, namely with claims reserves. There are two types of claim reserves, namely Incurred But Not Reported (IBNR) and Reported But Not Settled (RBNS). This research focuses on determining the estimation of aggregate claim reserves using the Kalman Filter method with scalar State Space Models (SSMs) which is a model resulting from the development of the Chain Ladder (CL) method. The Kalman Filter method with SSMs is a stochastic method that takes into account the time series model so that it can predict the temporal dynamics of a system more accurately. The results of forecasting claim reserves using the Kalman Filter method with SSMs will be compared with the CL method. Variational Of Coefficient (VOC) is an error predictor to determine the best method. The calculation results using the Kalman Filter method with SSMs produce a smaller VOC value than the CL method, proving that the Kalman Filter method with SSMs is better than CL.
Co-Authors Ade Lailani Adhitya Ronnie Effendie, Adhitya Ronnie Aditya Rahman Agus Irawan Agus Irawan Agus Sofian Eka Hidayat Agus Sofian Eka Hidayat Alfian, Muhammad Nuril Amalia Listiani Andi Fitriawati Aprilia, Inaya Sathrani Ayu Sofia Ayu Sofia Azzanina, Nanda Baiti, Putri Isnaini Cahyaning Berliana Ratam, Aldila Nur Indah Chaterine Theresia Lubis Chintya Carissa Manurung Dani Al Mahkya Dani Al Mahkya Dewi, Karina Sylfia Dharu Cahyoaji Sasongko Dila Tirta Julianty Divira Iga Firdianti Dwi Mahrani Erica Grace Simanjuntak Fitriawati , Andi Fuji Lestari Gilang Primajati Gustina K. S, Annisa Hevita Hardi Suyitno Hidayat, Agus Sofian Eka Inayah, Larasati Nurul Indah Gumala Andirasdini Irfan, Miftahul Irwan, Sri Efrinita Khairunnisa Maharani Lestari, Yushinta Cahya Linda Rassiyanti Listiani, Amalia Luluk Muthoharoh M. Naufal Athaatmaja Madonna, Nora Manurung, Chintya Carissa Marisa Marisa Ma’rufah Hayati Miftahul Irfan Miftha Ulya Nazima Mt, Ma’rufah Hayati Muklas Rivai Nasrullah Nasrullah Nasrullah Nora Madonna Patricia, Mitha Putri Isnaini Cahyaning Baiti Rivai, Muklas Rosni Rosni Rosni, Rosni Siregar, Abian Avisena Sitorus, Kristiani Sugiman Sugiman Yuliana Yuliana Yushinta Cahya Lestari