Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Journal of Applied Engineering and Technological Science (JAETS)
Aris Riyadi
Padjadjaran University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Machine Learning for Public Sector Performance Prediction: The Role of Communication, Decision-Making, and Strategy Alignment Aris Riyadi; Engkus Kuswarno; Dadang Sugiana; Asep Suryana
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.9624

Abstract

This study explores the relationship between communication quality, participatory decision-making, and organizational performance in the public sector using machine learning techniques. Data were collected from government agencies, employing stratified random sampling to survey civil servants at various levels of tenure. A decision-tree classification model was used to identify key predictors of organizational performance, with the model achieving 71% accuracy and a weighted F1-score of 0.71. The results highlight that interpersonal communication quality, employee involvement in decision-making, and strategic alignment were the most significant factors influencing performance. This study demonstrates the value of machine learning in capturing complex, nonlinear relationships in organizational data and provides practical insights for enhancing communication systems and decision-making structures in public institutions. The findings offer a framework for improving public sector performance by promoting participatory governance and aligning strategic priorities.
Co-Authors Asep Suryana Dadang Sugiana Engkus Kuswarno