Assessing public satisfaction of public service application using supervised machine learning

Zharif Mustaqim, Ilham; Melani Puspasari, Hasna; Tri Utami, Avita; Syalevi, Rahmad; Ruldeviyani, Yova

doi:10.11591/ijai.v13.i2.pp1608-1618

Open access Journal SINTA 1

Assessing public satisfaction of public service application using supervised machine learning

IAES International Journal of Artificial Intelligence · Vol. 13 No. 2 (2024) · pp. 1608–1618

DOI: 10.11591/ijai.v13.i2.pp1608-1618
ISSN: 2252-8938, 2089-4872
Published: 2024-06-01
Publisher: Institute of Advanced Engineering and Science
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.

Keywords

Collaboration network of the authors

27 authors · 61 co-authorship links · 13 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

University of Indonesia
Badan Pusat Statistik
Ministry of Finance
Affiliation unknown

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

Show as table

Co-author	Organisation	Joint articles
Mohamad Faisal Zulmy	Ministry of Finance	1
Monica Vivi Kurniawati	Affiliation unknown	1
Siti Mawar Rini Wintang	Badan Pusat Statistik	1
Joko Parmiyanto	Badan Pusat Statistik	1
Freddy Richard Putra Hulu	University of Indonesia	1
Azanisa Putri	University of Indonesia	1
Rifta Dimas Sulistiyo	University of Indonesia	1
Tito Febrian Nugraha	University of Indonesia	1
Wahyu Setiawan Wibowo	Badan Pusat Statistik	1
Venera Genia	University of Indonesia	1
Ahmad Fadhil	University of Indonesia	1
Yohan Adhi Styoutomo	Affiliation unknown	1
Puja Putri Abdullah	University of Indonesia	1
Rina Rahmawati	University of Indonesia	1
Fathurahman Ma'ruf Hudoarma	Affiliation unknown	1
Rezki Khairunnas	Affiliation unknown	1
Jeri Apriansyah Pagua	Affiliation unknown	1
Ghina Fitriya	Affiliation unknown	1
Muhammad Yazid Al Qahar	Affiliation unknown	1
Ulfah Nur Mukharomah	Affiliation unknown	1
Miftahul Agtamas Fidyawan	Affiliation unknown	1
Ramadhoni Putra	Affiliation unknown	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">Assessing public satisfaction of public service application using supervised machine learning</dc:title>
	<dc:creator>Zharif Mustaqim, Ilham</dc:creator>
	<dc:creator>Melani Puspasari, Hasna</dc:creator>
	<dc:creator>Tri Utami, Avita</dc:creator>
	<dc:creator>Syalevi, Rahmad</dc:creator>
	<dc:creator>Ruldeviyani, Yova</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Aplikasi Cek Bansos; N-grams; Public satisfaction; Sentiment analysis; Supervised machine learning; Thematic analysis</dc:subject>
	<dc:description xml:lang="en-US">The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
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	<dc:date>2024-06-01</dc:date>
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	<dc:identifier>10.11591/ijai.v13.i2.pp1608-1618</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 1608-1618</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
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	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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