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Machine Learning for Public Sector Performance Prediction: The Role of Communication, Decision-Making, and Strategy Alignment

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 7 No. 2 (2026) · hlm. 1449–1465

DOI
10.37385/jaets.v7i2.9624
ISSN
2715-6079, 2715-6087
Terbit
2026-06-15
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

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.

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Kelana Ashil SiddhawiraPadjadjaran University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Machine Learning for Public Sector Performance Prediction: The Role of Communication, Decision-Making, and Strategy Alignment</dc:title>
	<dc:creator xml:lang="en">Riyadi, Aris</dc:creator>
	<dc:creator xml:lang="en">Kuswarno, Engkus</dc:creator>
	<dc:creator xml:lang="en">Sugiana, Dadang</dc:creator>
	<dc:creator xml:lang="en">Suryana, Asep</dc:creator>
	<dc:subject xml:lang="en">Organizational Performance</dc:subject>
	<dc:subject xml:lang="en">Decision Making</dc:subject>
	<dc:subject xml:lang="en">Communication Quality</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Public Sector</dc:subject>
	<dc:description xml:lang="en">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.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2026-06-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.yrpipku.com/index.php/jaets/article/view/9624</dc:identifier>
	<dc:identifier>10.37385/jaets.v7i2.9624</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS); 1449-1465</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v7i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/9624/6421</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Aris Riyadi, Engkus Kuswarno, Dadang Sugiana, Asep Suryana</dc:rights>
</oai_dc:dc>