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All Journal Jurnal Kajian Pembelajaran Matematika Jurnal Varian Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pengabdian Masyarakat Asia Community Empowerment Jurnal Kajian Matematika dan Aplikasinya
Andi Daniah Pahrany
Universitas Negeri Malang
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Jurnal Varian

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Penerapan Regresi Lasso dan Elastic Net dalam Menganalisis Faktor-Faktor yang Mempengaruhi Tingkat Pengangguran Terbuka di Banten Mustikasari, Anita; Pahrany, Andi Daniah
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/xey50x64

Abstract

This study aims to identify and analyze the variables that affect the open unemployment rate in Banten Province, Indonesia. The analyzed variables include population density, average years of schooling, labor force participation rate, minimum wage, Provincial GRDP, total labor force, and the number of poor people. The method used in this study is multiple linear regression analysis with secondary data from the Central Bureau of Statistics (BPS) for the period 2017–2022. The analysis revealed multicollinearity in the average years of schooling variable, with a Variance Inflation Factor (VIF) >10. To address this issue, Lasso regression and Elastic Net regression were applied. The results of this study show that Lasso regression produces a model with a Mean Squared Error (MSE) of 1.3234857, while Elastic Net regression yields a model with a lower MSE of 0.180683, indicating better predictive performance. The best model for predicting the open unemployment rate in Banten Province is the Elastic Net regression. The variables that significantly affect the open unemployment rate are average years of schooling, labor force participation rate, minimum wage, Provincial GRDP, total labor force, and the number of poor people. The conclusion of this study is that Elastic Net regression is more effective in predicting the open unemployment rate than other methods. The implication of these findings is that the generated model can serve as a basis for formulating more effective labor policies to reduce the unemployment rate in Banten Province.
PENERAPAN ANN DAN GARCH PADA ANALISIS VOLATILITAS PERAMALAN TINGKAT KLAIM BIAYA RAWAT INAP TINGKAT PERTAMA (RITP) BPJS KESEHATAN Pahrany, Andi Daniah; Wakhidah, Melani Nur; Norrulashikin, Siti Mariam Binti
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5422

Abstract

The volatility of First-Level Inpatient Care (RITP) claim costs poses a substantial challenge to BPJS Health’s financial management, underscoring the need for accurate forecasting methods. This study employs Artificial Neural Network and Generalized Autoregressive Conditional Heteroscedasticity models to examine volatility dynamics and assess predictive performance. The results indicate that both models capture nonlinear patterns, heteroskedasticity, and temporal dependencies, with evidence that past fluctuations largely influence current volatility. Forecast accuracy is generally high, as reflected in the small discrepancies between predicted and actual values across most provinces. Nevertheless, the models exhibit limitations in capturing extreme peaks and troughs, where abrupt claim variations are not fully represented. These findings highlight the effectiveness of Artificial Neural Networks and Generalized Autoregressive Conditional Heteroscedasticity in modeling claim volatility, while emphasizing the need for model refinement, such as parameter optimization or integration with complementary approaches, to enhance forecasting reliability. 
Co-Authors Algafari Bakti Manggara Alif Faruqi Febri Yanto Anita Mustikasari Dewanti, Angel Khoirun Nisa Ferdiansyah, Rifaldy Adinandra Hasanah, Irmatul Hasmawati Hasmawati Izzalqurny, Tomy Izzalqurny, Tomy Rizky Kridha Pusawidjayanti Lita Wulandari Aeli Mukhammad Solikhin Nisa’, Fauziah Wardatun Norman Yoshi Haryono Norrulashikin, Siti Mariam Binti Nurdin Nurdin Parwono, Eka Andryanna Ramdhan Fazrianto Suwarman Ramdhan Fazrianto Suwarman Ratna Juwita Ro'uf, Ikhsan Abdul Tatas Ridho Nugroho Wakhidah, Melani Nur