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Yulian Fauzi
Departemen Matematika, FMIPA, Universitas Bengkulu
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences

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Journal : Journal of Mathematics UNP

 1 
ANALISIS KEMISKINAN EKSTREM PROVINSI BENGKULU MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) DENGAN PEMBOBOT ADAPTIVE GAUSSIAN KERNEL DAN ADAPTIVE BI-SQUARE Riki Wahyudi; Yulian Fauzi; Jose Rizal
Journal of Mathematics UNP Vol 8, No 2 (2023): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/unpjomath.v8i2.14914

Abstract

Extreme poverty is a condition of inability to fulfill basic needs, namely the need for food, clean drinking water, proper sanitation, health, shelter, education, and access to information which is not only limited to income, but also access to social services (United Nations, 1996).Geographically Weighted Regression (GWR) model is used in mapping extreme poverty of all level 2 regions in Bengkulu Province using Adaptive Gaussian Kernel and Adaptive Bi-Square weights as well as finding the best GWR model and analyzing the model against extreme poverty mapping of Bengkulu Province. The data used in this study is the March 2022 Susenas data. Of the 18 variables that allegedly affect extreme poverty, only 6 variables support the assumption of spatial heterogeneity in GWR modeling. Based on the selection of the best model, it is known that the GWR model with Adaptive Bisquare Kernel weighting is a suitable model for the percentage of extreme poor people in Bengkulu Province with the smallest AIC value
MODEL DEPENDENSI HARGA-HARGA KOMODITAS EKSPOR UNGGULAN INDONESIA MENGGUNAKAN PENDEKATAN COPULA Riri Sesiati; Jose Rizal; Yulian Fauzi
Journal of Mathematics UNP Vol 8, No 2 (2023): Journal Of Mathematics UNP
Publisher : UNIVERSITAS NEGERI PADANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/unpjomath.v8i2.14860

Abstract

  The export of non-oil and gas leading commodities (palm oil, rubber and cocoa) contributes the largest state revenue. Copula is used to model the dependency between two variables that have different marginal distributions. The data to be used is secondary data from the Commodity Futures Trading Supervisory Agency (BAPPEBTI) starting from January 1, 2018 - February 28, 2020 for the period before the Covid-19 pandemic and March 1, 2020 - November 26, 2021 for the period after the Covid-19 pandemic. The dependency of the two variables to be measured is the price of palm oil, rubber and cocoa before and after the start of the Covid-19 pandemic in Indonesia. The Copula that will be used are Joe, Gumbel, Frank, Gaussian, Clayton, and Student's t. The best Copula model obtained for palm oil and rubber dependencies is the Joe Copula, while palm oil and cocoa and cocoa rubber dependencies are the Clayton Copula.  
Co-Authors Asahar Johar Boko Susilo Boko Susilo Brotopuspito, Kirbani Sri Diah Khairiyah Foni Panca Wardhani Hana Taqqiyah Fachri Hartono, Hartono Istikomah Istikomah Jose Rizal Khairinnisa, Khairinnisa Kongko, Widjo Nugraheni, Enny Nur Afandi Nursalim, Rahmat Putri Jelita, Cici Ratna RANI Riki Wahyudi Riri Sesiati Sumarlan, Ahmad Suwarsono Suwarsono Ulfasari Rafflesia Zulfia Memi Mayasari