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All Journal FORUM STATISTIKA DAN KOMPUTASI Teknosains: Media Informasi Sains dan Teknologi GRADIEN International Journal of Advances in Intelligent Informatics Jurnal Sains Matematika dan Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Berdaya Mandiri Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Pattimura Proceeding : Conference of Science and Technology Prosiding Seminar Nasional Official Statistics Indonesian Journal of Community Empowerment and Service Aceh International Journal of Science and Technology Jurnal Sintak Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Journal of Statistics and Data Science Limits: Journal of Mathematics and Its Applications
Pepi Novianti
Program Studi Statistika, FMIPA Universitas Bengkulu
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Journal : Limits: Journal of Mathematics and Its Applications

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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 Abdul Aziz, Ali Agwil, Winalia Ahmad Ansori Mattjik Andini, Agita Anjani, Retno Tri Baki Swita Crisdianto, Riki Dedi Rosadi Devni Prima Sari Dian Agustina Dian Agustina Dian Agustina Dwi Lorenza, Kenny Dyah Pangesti, Riwi Dyah Setyo Rini Dyah Setyo Rini Dyah Setyorini Eka Angasa Etis Sunandi Fachri Faisal Fadila, Risfa Faeza, Veronnica Noer Fairuzindah, Athaya Fhaeza, Veronnica Noer Ghufira Hayadi, Ilham Herlin Fransiska Hermansah I Made Sumertajaya Idhia Sriliana Jose Rizal Jose Rizal Karuna, Elisabeth Evelin Marta, Rezkyan Nur Afandi Pangesti, Riwi Dyah Rini, Dyah Setyo Riwi Dyah Pangesti Setyo Rini, Dyah Sigit Nugroho Sigit Nugroho Sihombing, Esther Damayanti Suhaimi, Alus Ahmad Sunandi, Etis Susi Wijuniamurti Swita, Baki Ulfasari Rafflesia Ulfasari Rafflesia Ulfasari Rafflesia Ulfasari Rafflesia Wahyuliani, Indah