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Budi Susetyo
Departemen Statistika, IPB University
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Journal : Limits: Journal of Mathematics and Its Applications

 1 
Perbandingan Metode GWR, MGWR, dan MGWR-SAR pada Data Persentase Penduduk Miskin di Pulau Jawa Andina Fahriya; Budi Susetyo; I Made Sumertajaya
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 2 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i2.3057

Abstract

The primary goal of Sustainable Development Goals (SDGs) is to end poverty everywhere in all its forms. Poverty is defined as the inability to meet basic needs, such as food, clothing, shelter, education, and healthcare. In Indonesia, the poor population has reached 26.36 million people, with half of them residing on Java Island. Extensive research has been conducted on poverty, particularly using a spatial approach. Spatial regression is a statistical method that explicitly incorporates geographical aspects into a model framework. In spatial regression, two main challenges arise: spatial dependence and heterogeneity. These two effects are inherently interconnected and must be considered simultaneously. Mixed Geographically Weighted Regression with Spatial Autoregressive (MGWR-SAR) is a combination of Mixed Geographically Weighted Regression (MGWR) and Spatial Autoregressive (SAR). MGWR-SAR effectively addresses both spatial dependence and spatial heterogeneity simultaneously. This study aims to determine the best method for modeling the percentage of poor population on Java. The variables used included PPM, BPJSPBI, PPKM, PLSMP, PPTB, BPNT, NCPR, and IPM. The kernel function was selected based on the smallest cross-validation (CV) value, which was a Fixed Gaussian with a CV of 603.8268. Based on the GWR model, the global variables identified were PPTB, BPNT, and IPM, whereas the remaining variables were local. The MGWR-SAR method was found to be the best model for predicting the percentage of poor population, with an AIC = 448.9645, RMSE = 1.9075, and  = 75.23%.
Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Amir, Sulfikar Anak Agung Istri Sri Wiadnyani Anang Kurnia Andina Fahriya Anis Sulistiyowati Anisa, Rahma ASEP SAEFUDDIN Aulia Dwi Oktavia Aunuddin Aunuddin Bagus Sartono Bambang H. Trisasongko Bambang Juanda Brian G. Lees Cici Suhaeni Cut N. Ummu Athiyah DAMAYANTI BUCHORI Darfiana Nur Dewi Jasmina Dewi Jasmina, Dewi Dhea Dewanti Dian Kurniasari Dito, Gerry Alfa Dyah R. Panuju Endah Febrianti Erfiani Erfiani Fadjrian Imran Fahriya, Andina Faisal Arkan Farit Mochamad Afendi Fitrianto, Anwar H Karwono Hafidz Muksin Hamid, Assyifa Lala Pratiwi Hari Wijayanto Herlina Herlina Hermawati, Neni Hiola, Yani Prihantini I Made Sumertajaya Inayatul Izzati Diana Yusuf Indahwati Indahwati Indahwati Indahwati, NFN Intan Juliana Panjaitan Iswan Achlan Setiawan Izzati Rahmi HG Jap Ee Jia Jia, Jap Ee Karwono, H Kesuma Millati Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kristuisno Martsuyanto Kapiluka Kriswan, Suliana Kusman Sadik Kusni Rohani Rumahorbo La Ode Abdul Rahman La Ode Abdul Rahman La Ode Abdul Rahman M Nur Aidi M Nur Aidi, M Nur Mahmud A. Raimadoya Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Amirullah Yusuf Albasia Muhammad Nur Aidi Muhammad Sayuti Mustofa Usman Nurfadilah, Khalilah Nurfajrin, Tria Ermina Nurul Qomariasih Pannu, Abdullah Pika Silvianti Pika Silvianti Putri, Mega Ramatika Qalbi, Asyifah Qomariasih, Nurul Rachman, Nurul Aulia Rahma Anisa Rahmawat, NFN Rahmawati, nFN Ratnasari, Andika Putri Rifannisa Bahar Rifki Hamdani Rizki, Akbar Robert, Zahira Rahvenia Safitri, Wa Ode Rahmalia Sanusi, Ratna Nur Mustika Satriyo Wibowo Sembiring, Febryna Sri Ningsih Desi Afriany Sulandra, Ardelia Maharani Sulfikar Amir Suliana Kriswan Supriatin, Febriyani Eka Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Sylvia P. Soetantyo Syukri, Nabila Tina Aris Perhati Tiya Wulandari Ulfa Afilia Shofa Utami Dyah Syafitri Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Warsono Wulan Andriyani Pangestu Yasmin Erika Faridhan Zahira Rahvenia Robert Zainal A Koemadji