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All Journal LOGIK@ Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Mathematical and Fundamental Sciences Jurnal Fourier Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan AXIOM : Jurnal Pendidikan dan Matematika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jambura Journal of Biomathematics (JJBM) Jurnal Eurekamatika Innovative: Journal Of Social Science Research International Journal of Applied Mathematics, Sciences, and Technology for National Defense The Indonesian Journal of Computer Science
Nina Fitriyati
UIN Syarif Hidayatullah Jakarta
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Pengaruh Hiperparameter dan Variabel Eksogen pada Prediksi Multi-Langkah Kecepatan Angin menggunakan LightGBM Fitriyati, Nina; Liebenlito, Muhaza; Tsabitah, Salma Hasna
Jurnal Fourier Vol. 15 No. 1 (2026)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2025.151.1-16

Abstract

Penelitian ini menganalisis pengaruh konfigurasi hiperparameter dan variabel eksogen terhadap prediksi kecepatan angin multi-langkah menggunakan algoritma LightGBM dengan strategi Recursive dan Direct. Hasil penelitian menunjukkan bahwa panjang horizon prediksi merupakan salah satu faktor utama yang mempengaruhi tingkat kesalahan prediksi, dimana nilai kesalahan meningkat secara konsisten dari horizon 1 langkah hingga 30 langkah. Penambahan variabel eksogen pada model berupa suhu minimum dan kelembaban relatif terbukti mampu meningkatkan kinerja model pada seluruh horizon, dengan dampak yang lebih besar pada horizon menengah dan panjang. Selain itu, tuning hiperparameter menunjukkan pengaruh yang bergantung pada horizon, dimana tuning memberikan manfaat yang lebih jelas pada prediksi jangka panjang. Secara keseluruhan, hasil penelitian ini menunjukkan bahwa kinerja prediksi kecepatan angin tidak hanya dipengaruhi oleh pemilihan algoritma, tetapi juga dipengaruhi oleh kombinasi strategi prediksi, variabel eksogen, konfigurasi hiperparameter, dan panjang horizon prediksi. Kata Kunci: horizon, konfigurasi hiperparameter, pendekatan Direct, pendekatan Recursive. Abstract This study analyzes the effect of hyperparameter configuration and exogenous variables on multi-step wind speed prediction using the LightGBM algorithm with Recursive and Direct strategies. The results show that the length of the prediction horizon is one of the main factors affecting the level of prediction error, with the error increasing consistently from 1 step to 30 steps. The addition of exogenous variables to the model, such as minimum temperature and relative humidity, has been shown to improve model performance across all horizons, with a greater impact on medium- and long-horizon forecasts. In addition, hyperparameter tuning exhibits a horizon-dependent effect, with greater benefits for long-term predictions. Overall, the results of this study indicate that wind speed prediction performance is influenced not only by the choice of algorithm but also by the combination of prediction strategy, exogenous variables, hyperparameter configuration, and prediction horizon length. Keywords: horizon, kyperparameter configuration, Direct approach, Recursive approach.
ESTIMASI BAYESIAN PADA PARAMETER HUKUM MOTALITA GOMPERTZ MENGGUNAKAN ALGORITMA METROPOLIS-HASTINGS Yulinda Eliskar; Rustam Rustam; Nina Fitriyati; Khaerudin Saleh
AXIOM : Jurnal Pendidikan dan Matematika Vol 12, No 2 (2023)
Publisher : State Islamic University of North Sumatra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30821/axiom.v12i2.18061

Abstract

Tingkat mortalitas merupakan salah satu hal penting untuk menentukan nilai premi pada suatu produk asuransi. Pada umumnya, perusahaan asuransi menggunakan tabel mortalitas deterministik yang dibangun dari data kematian masa lalu. Namun pada kenyataannya, tingkat mortalitas dipengaruhi oleh faktor-faktor ketidakpastian yang menyebabkan tingkat mortalitas tersebut berubah secara stokastik. Pada penelitian ini, akan dikaji pengaruh mortalitas stokastik dalam mengestimasi parameter hukum mortalitas Gompertz menggunakan pendekatan analisis Bayesian sehingga parameter-parameter pada hukum mortalitas Gompertz tidak lagi berbentuk konstanta, namun memiliki distribusi. Estimasi Bayesian dilakukan dengan asumsi distribusi prior adalah normal. Pelibatan unsur stokasik dilakukan dengan menambahkan gangguan mortalitas yang dinyatakan dalam persentase dari force of mortality dengan rentang . Simulasi numerik dilakukan menggunakan Markov Chain Monte Carlo (MCMC) dengan Algoritma Mettopolis-Hastings. Hasil simulasi menunjukkan bahwa dengan l0 = 100000 dan l111 = 0, diperoleh nilai m* berdistribusi normal dengan mean 0.001665164 dan variansi 9,525 × 10-9 dan C* berdistribusi normal dengan mean 1.081264461 dan variansi 6,312134 × 10-7. Hasil ini dapat digunakan sebagai kerangka kerja yang lebih akurat untuk menganalisis keandalan, ketahanan, dan pembiayaan dalam dunia aktuaria, serta memberikan dasar yang lebih baik untuk pengelolaan risiko perusahaan. AbstractThe mortality rate is a crucial factor in determining the premium value for an insurance product. Typically, insurance companies use deterministic mortality tables that are built from past death data. However, in reality, the mortality rate is influenced by various uncertainty factors that cause it to change stochastically. In this research, we will study the influence of stochastic mortality in estimating the parameters of the Gompertz mortality law using a Bayesian analysis approach. This will enable us to model the parameters in the Gompertz mortality law as a distribution rather than a constant value. Bayesian estimation is carried out assuming the prior distribution is normal. The involvement of stochastic elements is carried out by adding mortality disturbance ∈ which is expressed as a percentage of the force of mortality with a range of . Numerical simulations were carried out using Markov Chain Monte Carlo (MCMC) with the Mettopolis-Hastings Algorithm. The simulation results show that with l0 = 100000 and l111 = 0, the m* value is normally distributed with a mean of 0.001665164 dan and a variance of 9,525 × 10-9 and C* is normally distributed with a mean of 1.081264461 and a variance of 6,312134 × 10-7. These results can be used as a more accurate framework for analyzing reliability, resilience and financing in the actuarial world, as well as providing a better basis for enterprise risk management.
Lee-Carter–ARIMA hybrid approach and machine learning for mortality rate forecasting in the United States: Implications for national defense and population risk assessment Vita Nuarini; Mahmudi; Nina Fitriyati; Madona Yunita Wijaya; Irma Fauziah
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol. 4 No. 2 (2026): International Journal of Applied Mathematics, Sciences, and Technology for Nati
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v4i2.1146

Abstract

Background: The Accuracy of mortality rate forecasting plays an important role in various decision-making processes in the life insurance sector, including determining premium amounts. In addition, it also contributes to assessing the readiness of human resources to support national defense, as well as to conducting risk assessments aimed at maintaining demographic stability. The United States mortality data was selected as the study object due to the availability of comprehensive and high-quality. Aims: This study explores five hybrid approaches that combine stochastic models and machine learning, along with one non-hybrid approach to assess their potential to improve forecasting accuracy. Method: In this study, the Lee-Carter–ARIMA, Lee-Carter–Random Forest, Lee-Carter–ANN, Lee-Carter–ARIMA–Random Forest, Lee-Carter–ARIMA–ANN, and ANN models were evaluated. These models were applied to mortality rate data from nine divisions in the United States (US), stratified by gender, using training data from 1966 to 2005 and test data from 2006 to 2015. The best model is determined based on the smallest Mean Absolute Percentage Error (MAPE) value while also considering the interpretability of the model. Result: The study's results show that, across the number of divisions, the Lee-Carter–ARIMA–Random Forest model produces the smallest MAPE values most often. However, in terms of average MAPE, the Lee-Carter–ARIMA–ANN model performs better, with MAPEs of 9.66% for females and 9.28% for males. Furthermore, neither of these models yields a substantial improvement in predictive accuracy compared with the Lee-Carter–ARIMA model. Conclusion: Considering the relatively small decrease in MAPE and the difficulty of interpreting machine learning models due to their black box nature, the Lee-Carter–ARIMA model demonstrates the best overall performance relative to the other models. Nevertheless, the Lee-Carter–ARIMA–Random Forest and Lee-Carter–ARIMA–ANN models show potential as alternative approaches that merit further investigation and may contribute to national defense planning and support the maintenance of demographic stability.
Co-Authors . Mahmudi Aceng Komarudin Mutaqin Adjie, Dharma Syadhi Putra Agus Yodi Gunawan Alkadrie, Syarifah Syila Amelia Emaras, Tamara Anas Qolbu, Adinda Andrew Fiade Anggita Septiani Azzahra Benita Bisyri, M. Alvi Devi Ila Octaviyani Dhea Urfina Zulkifli Dino Agustin Putra Fachry Abda El Rahman Faizah, Salma Abidah Fathu Rahman Gina Isma Kusuma Handarbeni, Zalfa Talitha Haris Hamzah Inna, Suma Irma Fauziah Iskandar Fahmi KHAERUDIN SALEH Khusnul Khotimah Mahmudi Mahmudi Mahmudi Mahmudi Mahmudi Maysun Maysun Maysun, Maysun Muhammad Manaqib, Muhammad Muhaza Liebenlito Nadilia, Nindita Nindita Nadilia Nur Inayah Nurmaleni Nurmaleni NURTITI SUNUSI Rahma, Chelsea Fatihah Rahman, Fathu Rini Marwati Rustam S. Sutawanir Sari, Flori Ratna Sri Maliska Sutanto, Taufik Edy Sutawanir Darwis Taufik Edy Sutanto Tsabitah, Salma Hasna Vita Nuarini Wahri Irawan Wijaya, Madona Yunita Wulandari, Fitria Sisca Yanne Irene Yulinda Eliskar Yupinto, Ikhwal Zharifah, Zharifah Zulkifli, Dhea Urfani