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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat JTAM (Jurnal Teori dan Aplikasi Matematika) Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jambura Journal of Mathematics Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications Mandalika Mathematics and Educations Journal Jurnal Statistika dan Aplikasinya Indonesian Journal of Applied Research (IJAR) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Diferensial Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Griya Journal of Mathematics Education and Application Journal of Science and Technology Journal of Mathematics: Theory and Applications Pattimura International Journal of Mathematics (PIJMath) Jurnal Riset Mahasiswa Matematika Jurnal Multidisiplin Sahombu Indonesian Journal of Computational and Applied Mathematics
La Ode Nashar
Prodi Statistika, Fakultas Matematika Dan IPA, Universitas Negeri Gorontalo
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Calculation of Annual Premiums and Premium Reserves for Endowment Joint Life Insurance Based on Stochastic Interest Rates Using the Monte Carlo Method Bela Cintiya Samwan; Agusyarif Rezka Nuha; Armayani Arsal; Emli Rahmi; La Ode Nashar
Jurnal Multidisiplin Sahombu Vol. 6 No. 01 (2026): Jurnal Multidisiplin Sahombu, January 2026
Publisher : Sean Institute

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Abstract

This study examines the determination of annual premiums and premium reserves for an endowment joint life insurance product by incorporating interest rate uncertainty through the Cox-Ingersoll-Ross (CIR) stochastic model and Monte Carlo simulation. The Indonesian Mortality Table 2023 is used to compute joint survival probabilities for the three insured individuals, while the CIR parameters are estimated from historical interest rate data for the period 2020-2024. The present value of benefits and annuities is calculated along each simulated path, enabling the premium and premium reserves to be evaluated prospectively based on fluctuating interest rate dynamics. The results show that the magnitude of premiums and reserves is influenced by the initial ages of the insured, the mortality structure, the sum assured, and the variability of the simulated interest rates. At the beginning of the contract, all scenarios produce negative reserves because accumulated premiums are still insufficient to cover the expected present value of benefits. However, the reserves increase steadily over time and turn positive toward the end of the insurance term. These findings indicate that the Monte Carlo approach based on the CIR model provides a more adaptive and realistic representation of premium and reserve behavior compared with deterministic methods, thereby supporting more accurate financial risk assessment for insurance companies.
Analisis Peramalan Harga Saham PT Unilever Indonesia Menggunakan Pemodelan LSTM dengan Optimasi PSO Burhan, Dewinto; Hasan, Isran K.; Nashar, La Ode
Jurnal Riset Mahasiswa Matematika Vol 5, No 3 (2026): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v5i3.40090

Abstract

Pergerakan harga saham cenderung tidak stabil dan sulit diprediksi karena memiliki pola yang kompleks serta berubah-ubah dari waktu ke waktu. Untuk mengatasi hal tersebut, penelitian ini menggunakan model Long Short-Term Memory (LSTM) dalam melakukan peramalan harga saham. Agar model yang dihasilkan memiliki kinerja yang optimal, dilakukan optimasi hyperparameter menggunakan metode Particle Swarm Optimization (PSO). Optimasi dilakukan pada tiga parameter utama LSTM yaitu LSTM units, dropout rate, dense units. Dari proses optimasi diperoleh enam konfigurasi model terbaik. Hasil pengujian menunjukkan model ke-5 memberikan hasil optimasi paling baik dengan nilai RMSE 120,3320 dan MAPE sebesar 3,53% dengan menggunakan kombinasi hyperparameter LSTM units = 137, dropout rate = 0,498, dan dense units = 32. Hasil ini menunjukkan bahwa optimasi hyperparameter memberikan peningkatan akurasi peramalan dibandingkan LSTM tanpa optimasi. Dengan demikian, kombinasi model LSTM dengan optimasi PSO mampu menghasilkan peramalan harga saham yang lebih akurat dan stabil. Pendekatan ini dapat digunakan sebagai alternatif dalam analisis pergerakan harga saham dan mendukung pengambilan keputusan investasi.
Co-Authors Agung, Andi Agusyarif Rezka Nuha Ahmad, Rindawati Akor, Umar D. Armayani Arsal Asriadi Asriadi Asriadi Asriadi Bela Cintiya Samwan Bertu Rianto Takaendengan Brahim, Annisa Maharani Burhan, Dewinto Chasanah, Sri Istiyarti Uswatun Dewi Rahmawaty Isa Dina Fakhriyana Djihad Wungguli Fadilah Istiqomah Pammus Gani, Friansyah Hasan S. Panigoro Hasan, Riyanto Hasibuan, Lilis Harianti Herlina Jusuf Ismail Djakaria Isran K Hasan Iyohu, Lisa Rianti K. Hasan, Isran Karina Anselia Mamonto LA ODE SABRAN Ladjali, Sri Indriani Lailany Yahya Latif, Sintia Abdul LIHAWA, SRIRAPI H Lindrawati Abdjul Loleh, Linda Purnama Sari Mahmud, Sri Lestari Marukai, Nur Amalia Muhammad Rezky F. Payu Nadiyyah, Ana NISKY IMANSYAH YAHYA Novianita Achmad Nurafni, Firda Nurwan NURWAN NURWAN Nurwan Nurwan Nurwan Nurwan Nurwan Nurwan Rahman, Gusti Arviana Rahmi, Emli Rasyid, Kamelia Resmawan Resmawan Rianjaya, Ilham Dangu Sabran, La Ode Salmun K. Nasib Sartika Sari Dewi Siti Nurmardia Abdussamad Sugito Mahendra Imran Suleman, Alis Syarif Abdullah Tedy Machmud Ulopo, Asrul S Valentika, Nina Widyastutifajri Nuha Zian Bula