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Assessing public satisfaction of public service application using supervised machine learning

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

DOI
10.11591/ijai.v13.i2.pp1608-1618
ISSN
2252-8938, 2089-4872
Terbit
2024-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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28 penulis · 62 tautan kepenulisan bersama · 14 lintas organisasi

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  • University of Indonesia
  • Badan Pusat Statistik
  • Ministry of Finance
  • Universitas Teknokrat Indonesia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Mohamad Faisal ZulmyMinistry of Finance1
Monica Vivi KurniawatiAfiliasi tidak diketahui1
Siti Mawar Rini WintangBadan Pusat Statistik1
Joko ParmiyantoBadan Pusat Statistik1
Freddy Richard Putra HuluUniversity of Indonesia1
Azanisa PutriUniversity of Indonesia1
Rifta Dimas SulistiyoUniversity of Indonesia1
Tito Febrian NugrahaUniversity of Indonesia1
Wahyu Setiawan WibowoBadan Pusat Statistik1
Venera GeniaUniversity of Indonesia1
Ahmad FadhilUniversity of Indonesia1
Ryan Randy SuryonoUniversitas Teknokrat Indonesia1
Yohan Adhi StyoutomoUniversity of Indonesia1
Puja Putri AbdullahUniversity of Indonesia1
Rina RahmawatiUniversity of Indonesia1
Fathurahman Ma'ruf HudoarmaUniversity of Indonesia1
Rezki KhairunnasUniversity of Indonesia1
Jeri Apriansyah PaguaUniversity of Indonesia1
Ghina FitriyaUniversity of Indonesia1
Muhammad Yazid Al QaharUniversity of Indonesia1
Ulfah Nur MukharomahUniversity of Indonesia1
Miftahul Agtamas FidyawanUniversity of Indonesia1
R M PutraUniversity of Indonesia1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23322</dc:identifier>
	<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>
	<dc:source>10.11591/ijai.v13.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23322/13963</dc:relation>
	<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>
</oai_dc:dc>