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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Dan Teknologi (SAINTEKBU)
Arifin, Maulana Muhamad
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Computer Science & IT Control & Systems Engineering Engineering Library & Information Science Mathematics

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Actuarial Modeling of COVID-19 Insurance Kurniawaty, Mila; Arifin, Maulana Muhamad; Kurniawan, Bagus; Sukarno, Sadam Laksamana; Prayoga, Muhammad Teguh
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i3.14999

Abstract

In this article, we provide an actuarial model expected to be able to help financial arrangements to cover losses due to the outbreak of coronavirus disease (COVID-19). We construct the dynamical models of premium and benefit based on generalized SEIR (Susceptible-Exposed-Infected-Recovered). Based on its dynamical model, we formulate the premium and the premium reserves on hospitalization and death benefits of the COVID-19 insurance.
The Classification of Insurance Claim Risk Using the Multilayer Perceptron Method Handamari, Endang Wahyu; Sa'adah, Umu; Arifin, Maulana Muhamad
SAINTEKBU Vol. 17 No. 01 (2025): Vol. 17 (01) January 2025
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v17i01.5178

Abstract

Policyholders purchase insurance policies to protect themselves or their assets from potential financial risks in the future. Insurance guarantees that if an event covered by the policy occurs, the insurance company will provide compensation according to the agreed terms. Insurance companies conduct risk assessments for each policyholder to determine the premium that must be paid, making it essential to classify risk categories accurately. The Multilayer Perceptron (MLP) is one method used for classification problems. It is a machine learning algorithm belonging to the family of artificial neural networks. MLP is a flexible algorithm that can solve various classification problems, including those with complex features and non-linear relationships between input and output variables. The result of this research is the development and implementation of a Multilayer Perceptron method to classify risk categories. The evaluation of the Multilayer Perceptron model for risk classification shows satisfactory performance. Based on the classification report from training and test data, the model does not exhibit overfitting or underfitting.
Improving multilayer perceptron on rainfall data using modified genetics algorithm Marji, Marji; Mahmudi, Wayan Firdaus; Handamari, Endang Wahyu; Santoso, Edy; Arifin, Maulana Muhamad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3994-4005

Abstract

Rainfall prediction is essential for managing water resources, agriculture, and disaster response, particularly in regions affected by climate variability. This study introduces a modified genetic algorithm (MGA) to optimize hyperparameters of a multilayer perceptron (MLP) for rainfall forecasting. The MGA incorporates elitism to retain top-performing solutions and adaptive selection based on model accuracy. The proposed MGA–MLP model was tested on rainfall datasets from Australia and Indonesia (BMKG). Experimental results show that configurations with two hidden layers, rectified linear unit (ReLU) activation and limited-memory Broyden Fletcher Goldfarb Shannon (LBFGS) optimizer, a learning rate of 0.001 and 1000 epochs consistently delivered strong performance. The model achieved accuracies of 86.02% and 79.05%, respectively. These findings indicate that MGA significantly improves MLP performance and provides a reliable, generalizable method for rainfall prediction across diverse climatic conditions.
Co-Authors Bagus Kurniawan, Bagus Edy Santoso Endang Wahyu Handamari Mahmudi, Wayan Firdaus Marji Marji Mila Kurniawaty Prayoga, Muhammad Teguh Sa'adah, Umu Sukarno, Sadam Laksamana