Jurnal Gaussian
Vol 15, No 1 (2026): Jurnal Gaussian

REGRESI ROBUST ESTIMASI-M UNTUK PEMODELAN TINGKAT PENGANGGURAN TERBUKA (TPT) DI PROVINSI JAWA TENGAH

octa choerunnisa (Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro)
Mustafid Mustafid (Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro)
Sudarno Sudarno (Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro)



Article Info

Publish Date
24 Aug 2026

Abstract

Regression analysis can be used to overcome the outlier problem with robust regression. The M-estimation robust regression method is an estimation approach that is the simplest computationally and theoretically. The purpose of using the robust regression method is because there is an outlier problem in the data. This research will create a model on open unemployment rate data in Central Java in 2022 which involves variables including average length of schooling, population, number of poor people, minimum wage, and per capita expenditure. Based on the Ordinary Least Squares (OLS) regression equation, it shows that there is a violations of assumptions and outlier data is detected. So it is necessary to carry out robust regression to overcome the outlier problem. In the test carried out using M-estimation robust regression, the best robust regression model was obtained  = -5,899 + 0,0192 X3+ 0,0000024 X4+0,00036 X5. The M-estimation robust regression model can be concluded that total variation in the open unemployment rate in Central Java in 2022 is explained by the number of poor people, minimum wages, and per capita expenditure by 31.39%, and the MSE value is 2.143382.

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Journal Info

Abbrev

gaussian

Publisher

Subject

Other

Description

Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM ...