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All Journal Enrichment: Journal of Multidisciplinary Research and Development
Mulya Jefri
Institut Teknologi PLN
Humanities Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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Application of Multiple Linear Regression (MLR) Method in Certification Activities at ITCC ITPLN Hendra Jatnika; Luqman Luqman; Meilia Nur Indah Susanti; Petra Andriyani Mulyo Wibisono; Mulya Jefri
Enrichment: Journal of Multidisciplinary Research and Development Vol. 2 No. 12 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v2i12.314

Abstract

Multiple Linear Regression (MLR) is one of the algorithms in Machine Learning. Machine Learning that estimates the linear coefficient equations involved in one or more independent variables that can predict the value of the variable of interest. Algorithms used to predict the value of a variable based on the value of other variables. Based on 2021 data at the Information Technology Certification Center (TCC), it can be seen that the quality and quantity of Microsoft International Certification graduates is decreasing. In the pre-pandemic MOS certification, the percentage of passes was seventy-two percent (72%), while in the MOS certification during the pandemic the percentage of passes dropped to fifty-two percent (52%). Based on the results of the MLR trial test on the dataset of MOS-Word and MCF-AI certification test participants, a calculation formula is obtained as a benchmark in assessing the MOS-Word and MCF AI certification scores. The study provides specific recommendations to optimize certification training programs by tailoring materials to address critical competencies and participant needs. Additionally, a predictive formula developed in this research can serve as a self-assessment tool for participants to evaluate their readiness for certification tests. These findings underscore the potential of MLR as a robust analytical tool for improving certification processes, enhancing training effectiveness, and ensuring better outcomes for participants. This research contributes to advancements in machine learning applications within education and professional development contexts.
Co-Authors Hendra Jatnika Luqman Luqman Meilia Nur Indah Susanti, Meilia Nur Petra Andriyani Mulyo Wibisono