Article Per Year (5 Year)
p-Index From 2020 - 2025
0.947
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Pythagoras: Jurnal Matematika dan Pendidikan Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Matematika Jurnal POINTER Jurnal Sains Dan Teknologi (SAINTEKBU) BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Research in Mathematics Trends and Technology Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Endang Wahyu Handamari
Department Of Mathematics, University Of Brawijaya, Malang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
Seleksi Nilai Fuzziness Exponent Optimal pada Algoritma Fuzzy c-Means untuk Mengelompokkan Provinsi di Indonesia Berdasarkan Indikator Pembangunan Ekonomi Sa'adah, Umu; Handamari, Endang Wahyu; Andawaningtyas, Kwardiniya; Setyowati, Nur Fitriana
PYTHAGORAS Jurnal Pendidikan Matematika Vol 17, No 2: December 2022
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v17i2.54897

Abstract

Pada tahun 2015, PBB merancang 17 Tujuan Pembangunan Berkelanjutan (SDGs) untuk mencapai kesejahteraan manusia pada tahun 2030 dengan mengintegrasikan tiga dimensi pembangunan berkelanjutan: ekonomi, sosial, dan lingkungan. Salah satu faktor yang digunakan untuk menilai keberhasilan sebuah wilayah atau pemerintahan dalam mengelola kesejahteraan dan kemakmuran masyarakat adalah tingkat perekonomian. Untuk mewujudkan kondisi tersebut diperlukan strategi dalam pembangunan pada sektor ekonomi. Penelitian ini bertujuan untuk mengelompokkan Provinsi di Indonesia menjadi 3 klaster berdasarkan indikator pembangunan ekonomi menggunakan algoritma fuzzy c-means. Penentuan 3 klaster dimaksudkan untuk klaster provinsi dengan tingkat pembangunan ekonomi rendah, sedang dan tinggi. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diperoleh dari laman resmi Badan Pusat Statistika. Dengan mengetahui karakteristik provinsi berdasarkan indikator pembangunan ekonomi (IPE), maka pengambil keputusan dapat menyusun strategi perencanaan program pembangunan ekonomi berdasarkan skala prioritas pada masing-masing provinsi. Hasil pengelompokan menunjukkan bahwa Provinsi Papua sangat membutuhkan prioritas pembangunan khususnya dalam sektor ekonomi guna peningkatan indeks pembangunan manusia, angka partisipasi sekolah berusia 7 sampai 12 tahun, angka partisipasi sekolah berusia 13 sampai 15 tahun, angka partisipasi sekolah berusia 16 sampai 18 tahun, sumber air minum yang layak, sumber penerangan listrik, dan sanitasi yang layak, karena indikator-indikator tersebut memiliki nilai rendah.
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.
Implementasi Metode Bayesian untuk Menghitung Premi Produk Asuransi Kendaran Bermotor dengan Pendekatan Monte Carlo Markov Chain Situmorang, Boy Nathanael; A’la, Kevina Alal; Arvianti, Aurellia; Yusuf, Feby Indriana; Handamari, Endang Wahyu
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32930

Abstract

Accurate premium determination is a fundamental aspect of risk management in motor vehicle insurance. This study implements the Bayesian method using a Markov Chain Monte Carlo (MCMC) approach to calculate the net premium. The aggregate claim model is constructed from a claim frequency distribution (Poisson) and a claim severity distribution (Generalized Extreme Value (GEV)), with the GEV distribution specifically chosen to model extreme claim risk. The analysis utilizes generated data for the period 2018–2024, with parameters derived from the historical data of PT Asuransi Jasa Indonesia Purwokerto (2013–2017). Parameter estimation, performed via OpenBUGS software, was validated to have achieved good convergence (MC-error   ). Based on the estimated parameters, a premium of IDR 397.502.000 was obtained, calculated using the net premium principle from the expected value of aggregate claims. These results demonstrate that the Bayesian MCMC approach is effective for producing a robust premium estimation, contributing a pricing framework that explicitly accounts for extreme value claims.
VALUE AT RISK ESTIMATION FOR STOCK PORTFOLIO USING THE ARCHIMEDEAN COPULA APPROACH Saifullah, Mohammad Dicky; Sa'adah, Umu; Andawaningtyas, Kwardiniya; Handamari, Endang Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1779-1790

Abstract

Investment is one of the many ways to achieve future profits. One form of investment that is widely made is stocks. The return obtained in investing in stocks is potentially higher than other investment alternatives, but the risks borne are amplified, so it is necessary to analyze these risks that may occur. In this study, the Archimedean copula method is used to estimate the Value at Risk on shares of PT Bank Rakyat Indonesia Tbk (BBRI) and PT Telekomunikasi Indonesia Tbk (TLKM) for the period September 1, 2021, to August 31, 2023. The stock data is used to determine the Archimedean copula model and calculate the estimated value of Value at Risk (VaR) on the stock return portfolio using the Archimedean copula approach. The Archimedean copula models used are the Clayton copula model, Gumbel copula, and Frank copula. Of the three Archimedean copula models, the best model was selected by looking at the largest Maximum Likelihood Estimation (MLE) value. In this study, the log-likelihood value of Clayton copula is 7.958, Gumbel copula is 6.663, and Frank copula is 8.398. Therefore, Frank copula is the best Archimedean copula model with the largest log-likelihood value of 8.398 for the said data. Then the VaR estimation is done with the Frank copula model. The Value at Risk estimation results based on the Frank copula model show maximum loss rates of -0.0277 at the 90% confidence level, -0.0363 at the 95% confidence level, and -0.0516 at the 99% confidence level.
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 Ambarwani, Ajeng Pambuko Arifin, Maulana Muhamad Arvianti, Aurellia A’la, Kevina Alal Edy Santoso Emilia S I F. Laua, Deni Putra Lurenso Fakih, Biastadias Sabirina Feby Indriana Yusuf Hady Rasikhun Imam Nurhadi Purwanto Indah Resti Ayuni Suri Jannah, Liwaul Karim, Corina Kwardiniya Andawaningtyas Mahmudi, Wayan Firdaus Marji Marji Marsudi, Marsudi Mila Kurniawaty Moko, Endri Bitlas Mukti, Donny Hanggoro Ning Setyowati, Citra Ayu Novitasari, Novitasari Nuraini, Safitri Ana Nurcahyani, Lia Pramoedyo Henny, Pramoedyo Rinaldy, Fawzan Rosyidah, Tri Hastuti Nur Sa'adah, Umu Saifullah, Mohammad Dicky Setyowati, Nur Fitriana Shankar, Aldyan Situmorang, Boy Nathanael Yundari, Yundari