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All Journal Jurnal Varian Yumary: Jurnal Pengabdian kepada Masyarakat
Iman Setiawan
Universitas Tadulako, Palu, Indonesia
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Social Sciences Other

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Modeling Ridge–GLS Regression for Multicollinearity and Autocorrelation in the Consumer Price Index Radifa Radifa; Nur’eni Nur’eni; Nurul Fiskia Gamayanti; Mohammad Fajri; Iman Setiawan; Fadjryani Fadjryani
Jurnal Varian Vol. 9 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i2.6270

Abstract

The Consumer Price Index (CPI) in Indonesia showed a relatively stable increase from January 2022 to December 2023, but experienced fluctuations from January 2024 to February 2025. This situation highlights the need to analyze the factors influencing CPI movements. However, the data used in this study suffer from autocorrelation and multicollinearity issues. The aim of this study is to model the Consumer Price Index (CPI) in Indonesia and evaluate the effectiveness of Ridge Regression and Generalized Least Squares (GLS) in addressing multicollinearity and autocorrelation issues. The method used in this study combines Ridge Regression and Generalized Least Squares (GLS) with the Cochrane–Orcutt transformation. Ridge Regression is applied using the Hoerl, Kennard, and Baldwin (HKB) bias constant, while GLS is used through the Cochrane–Orcutt approach. This study uses monthly time-series data from January 2022 to February 2025, with independent variables consisting of narrow money supply, broad money supply, interest rates, and the Food Price Index (FPI). The results indicate that all Variance Inflation Factor (VIF) values were below 10 and the Durbin–Watson statistic increased to 1.9044. Additionally, broad money supply, narrow money supply, interest rates, and the Food Price Index were found to influence CPI. These findings indicate that the combined Ridge–GLS approach provides an appropriate model for analyzing Indonesian CPI dynamics in the presence of multicollinearity and autocorrelation.
Improving Research Data Analysis Competence Through Statistical Software Training And Assistance Nurul Fiskia Gamayanti; Hartayuni Sain; Fadjryani Fadjryani; Iman Setiawan; Mohammad Fajri
Yumary: Jurnal Pengabdian kepada Masyarakat Vol 7 No 1 (2026): September
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/yumary.v7.n1.p271-281.2026

Abstract

Purpose: This community service activity aims to improve lecturers’ competence in processing and analyzing research data using STATA statistical software. The activity also seeks to enhance participants’ understanding of statistical analysis methods, ranging from descriptive to inferential analysis, to support the production of more comprehensive and reliable research findings.Research Methodology: The activity was implemented through three stages: preparation, training and mentoring, and evaluation. The preparation stage involved coordinating with the partner institution, identifying participants’ training needs, preparing training materials, and providing the required facilities and STATA software. The training stage consisted of material presentation, demonstrations, and hands-on practice in data management, descriptive statistics, inferential statistics, and interpretation of STATA outputs. The activity was followed by direct mentoring to assist participants in applying the materials. Evaluation was conducted through observation and participant questionnaires.Results: The activity was successfully implemented, and participants demonstrated a good understanding of the materials and were able to operate STATA during the practical sessions.Conclusions: The training and mentoring activities contributed to improving participants’ knowledge and practical skills in processing and analyzing research data using STATA.Limitations: Limited time restricted the depth of practice and mentoring provided to participants.Contributions: This activity provides practical support for lecturers in strengthening their statistical analysis skills and utilizing STATA to support future research activities.
Co-Authors Fadjryani Fadjryani Hartayuni Sain Mohammad Fajri Nurul Fiskia Gamayanti Nur’eni Nur’eni Radifa Radifa