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Decision Support System for the Success of Education Program at Secondary School Level using Combination of K-Medoids Clustering and TOPSIS

Journal of Advances in Information Systems and Technology · Vol. 2 No. 1 (2020) · hlm. 33–40

DOI
10.15294/jaist.v2i1.44366
ISSN
2715-999X, 2714-9714
Terbit
2020-04-30
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The success of education programs is one of the concepts of educational equity that aims to educate a nation's life. However, the condition of education in Indonesia is not evenly distributed, and this can be seen from the availability and affordability of education services in each province. This research applies a decision support system to determine two categories: provinces that have not achieved a success rate of education program using K-Medoids Clustering and TOPSIS. The K-Medoids Clustering was used to overcome the outliers in the data. The TOPSIS is used to provide decision making based on the best alternative concept. The best alternative concept in TOPSIS has the closest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The number of clusters formed as many as five clusters. The iteration needed to cluster provinces using K-Medoids Clustering is 817 iterations. The third cluster has the largest variable average value and smallest standard deviation value. So, the third cluster shows the best cluster quality. Determination of provincial members into two categories is partially/fully refers to members of best cluster quality and TOPSIS preference value.

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  • State University of Semarang
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Rekan penulisOrganisasiArtikel bersama
Endang SugihartiState University of Semarang1
Riza ArifudinState University of Semarang1
Kholiq BudimanAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Alfath YanuartoAfiliasi tidak diketahui1
Dinar Diaz SeptianAfiliasi tidak diketahui1
Akhmad MunawarAfiliasi tidak diketahui1
Andika Enggal RamadhanAfiliasi tidak diketahui1
Bhekti KumorowatiAfiliasi tidak diketahui1
Alamsyah AlamsyahState University of Semarang1
Hendi SusantoAfiliasi tidak diketahui1
Martanto Setyo HusodoAfiliasi tidak diketahui1
Muh. SholehAfiliasi tidak diketahui1
Dian Tri WiyantiState University of Semarang1
Dante AlighiriState University of Semarang1
Priyantini WidiyaningrumAfiliasi tidak diketahui1
Agus YuliantoState University of Semarang1

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) https://journal.unnes.ac.id/sju/index.php/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">Decision Support System for the Success of Education Program at Secondary School Level using Combination of K-Medoids Clustering and TOPSIS</dc:title>
	<dc:creator>Widyanti, Devi Tri</dc:creator>
	<dc:creator>Putra, Anggyi Trisnawan</dc:creator>
	<dc:creator>Muslim, Much Azis</dc:creator>
	<dc:description xml:lang="en-US">The success of education programs is one of the concepts of educational equity that aims to educate a nation's life. However, the condition of education in Indonesia is not evenly distributed, and this can be seen from the availability and affordability of education services in each province. This research applies a decision support system to determine two categories: provinces that have not achieved a success rate of education program using K-Medoids Clustering and TOPSIS. The K-Medoids Clustering was used to overcome the outliers in the data. The TOPSIS is used to provide decision making based on the best alternative concept. The best alternative concept in TOPSIS has the closest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The number of clusters formed as many as five clusters. The iteration needed to cluster provinces using K-Medoids Clustering is 817 iterations. The third cluster has the largest variable average value and smallest standard deviation value. So, the third cluster shows the best cluster quality. Determination of provincial members into two categories is partially/fully refers to members of best cluster quality and TOPSIS preference value.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2020-04-30</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/44366</dc:identifier>
	<dc:identifier>10.15294/jaist.v2i1.44366</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 2 No 1 (2020): April; 33-40</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v2i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/44366/18056</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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