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All Journal Diophantine Journal of Mathematics and Its Applications Limits: Journal of Mathematics and Its Applications
Andini, Agita
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Mathematics

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Analisis Regresi Spasial pada Persentase Penduduk Miskin di Provinsi Bengkulu Tahun 2020 Dikombinasikan dengan Aplikasi Arcmap dan Geoda Putri, Meli Handayani Catur; Immaniah, Rhifa; Andini, Agita; Kamal, Maimuna; Sari, Yunita; Hidayati, Nurul
Diophantine Journal of Mathematics and Its Applications Vol. 2 No. 2 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v2i2.32045

Abstract

Kemiskinan dipandang sebagai ketidakmampuan sisi ekonomi dalam memenuhi kebutuhan dasar makanan dan non makanan yang diukur berdasarkan Garis Kemiskinan. Kemiskinan suatu daerah sangat mungkin dipengaruhi oleh kemiskinan di kabupaten/kota. Maka dari itu, diperlukan suatu pemodelan yaitu model regresi spasial. Tujuan penelitian ini adalah untuk mengidentifikasi faktor-faktor yang berpengaruh terhadap kemiskinan dengan memperhatikan efek spasial di Provinsi Bengkulu pada tahun 2020. Pada analisis ini menggunakan bantuan software GeoDa, Archmap dan R studio. Model regresi spasial yang digunakan dalam penelitian ini adalah Spatial Autoregressive Model (SAR). Model SAR menunjukkan keterkaitan antara suatu kabupaten/kota dengan kabupaten/kota yang berdekatan. Diperoleh nilai AIC sebesar adalah 48.0022 dan nilai tersebut lebih kecil dari regresi klasik. Data yang digunakan diperoleh dari Badan Pusat Statistika (BPS) Provinsi Bengkulu mengenai Persentase Penduduk Miskin.
Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka Andini, Agita; Sunandi, Etis; Novianti, Pepi; Sriliana, Idhia; Agwil, Winalia
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3374

Abstract

Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt the stability of parameter estimates. Therefore, Ridge Regression and Jackknife Ridge Regression methods were used to address this issue. Both methods modify the least squares method by adding a bias constant value. This research uses the Open Unemployment Rate (OUR) data in Sumatra in 2022, and 3 predictor variables exhibit multicollinearity. Based on the analysis comparing the Mean Squared Error (MSE) values, the Jackknife Ridge Regression method yields the smallest MSE value, 0.004. Both methods are effective in addressing multicollinearity and identifying significant predictor variables for OUR in Sumatra Island, namely the Human Development Index (HDI), average years of schooling, number of poor people, Life Expectancy (LE), population density and inactive population
Co-Authors Agwil, Winalia Idhia Sriliana Immaniah, Rhifa Kamal, Maimuna Nurul Hidayati Pepi Novianti Putri, Meli Handayani Catur Sunandi, Etis Yunita Sari