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Comparison algorithms with optimization for clustering multi-criteria

IAES International Journal of Artificial Intelligence · Vol. 15 No. 4 (2026) · hlm. 3476–3491

DOI
10.11591/ijai.v15.i4.pp3476-3491
ISSN
2252-8938, 2089-4872
Terbit
2026-08-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In higher education, forecasting student graduation is crucial for early intervention development, policy formulation, and class planning. This categorizes the factors that affect student graduation according to both academic and non-academic traits. To compare various clustering techniques, such as K-means, fuzzy C-means (FCM), K-medoids, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, Gaussian mixture model (GMM), and deep learning (DL), as well as particle swarm optimization (PSO) and genetic algorithm (GA) optimization for K-means and FCM, this article applies the hybrid elbow + silhouette optimization prior to the clustering process. Two clusters were found in the pre-cluster optimization research results. K-means, FCM, GMM, and DL clustering all demonstrated more distinct centroid separation; K-means and GMM were the most visually stable and comprehensible. Inter-cluster separation is the main goal of post-cluster optimization, and the K-means + PSO method is the best option. The cross-dataset validation findings indicate that the clustering model exhibits moderate consistency on the new dataset, with an adjusted Rand index (ARI) of 0.478 and a normalized mutual information (NMI) of 0.433.

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22 penulis · 67 tautan kepenulisan bersama · 28 lintas organisasi

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  • Muhammadiyah University of Makassar
  • STMIK Profesional Makassar
  • Asia e University
  • Universitas Handayani Makassar
  • University of Brawijaya
  • Universitas DIPA Makassar
  • Sekolah Tinggi Agama Islam Negeri Majene
  • Organisasi lain
  • 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
Nasir UsmanSTMIK Profesional Makassar2
Medy Wismu PrihatmonoAfiliasi tidak diketahui2
Nurdiansyah NurdiansyahUniversitas DIPA Makassar2
Musdalifa ThamrinSTMIK Profesional Makassar2
I Dewa Made WidiaUniversity of Brawijaya2
Suardi Hi BaharuddinSTMIK Profesional Makassar2
Muhammmad FaisalAfiliasi tidak diketahui1
Mirfan MirfanUniversitas Handayani Makassar1
Mila JumarlisSekolah Tinggi Agama Islam Negeri Majene1
Irmawati ImawatiPoliteknik Negeri Ujung Pandang1
Mardiah MardiahAfiliasi tidak diketahui1
Hairin AnisaUniversitas Handayani Makassar1
Hoga SaragihBinus University1
Sumiati SumiatiSerang Raya University1
Agung TriayudiNasional University1
Abd Rahman WahidMuhammadiyah University of Makassar1
Desi AnggreaniMuhammadiyah University of Makassar1
Muhyiddin A M HayatMuhammadiyah University of Makassar1
Aedah Binti Abd RahmanAsia e University1

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">Comparison algorithms with optimization for clustering multi-criteria</dc:title>
	<dc:creator>Mulyadi, Ida</dc:creator>
	<dc:creator>Abdul Rahman, Titik Khawa</dc:creator>
	<dc:creator>Faisal, Muhammad</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Elbow; Fuzzy C-means; K-means; Optimization; Silhouette</dc:subject>
	<dc:description xml:lang="en-US">In higher education, forecasting student graduation is crucial for early intervention development, policy formulation, and class planning. This categorizes the factors that affect student graduation according to both academic and non-academic traits. To compare various clustering techniques, such as K-means, fuzzy C-means (FCM), K-medoids, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, Gaussian mixture model (GMM), and deep learning (DL), as well as particle swarm optimization (PSO) and genetic algorithm (GA) optimization for K-means and FCM, this article applies the hybrid elbow + silhouette optimization prior to the clustering process. Two clusters were found in the pre-cluster optimization research results. K-means, FCM, GMM, and DL clustering all demonstrated more distinct centroid separation; K-means and GMM were the most visually stable and comprehensible. Inter-cluster separation is the main goal of post-cluster optimization, and the K-means + PSO method is the best option. The cross-dataset validation findings indicate that the clustering model exhibits moderate consistency on the new dataset, with an adjusted Rand index (ARI) of 0.478 and a normalized mutual information (NMI) of 0.433.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Muhammadiyah Makassar</dc:contributor>
	<dc:date>2026-08-01</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/30202</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i4.pp3476-3491</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 4: August 2026; 3476-3491</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/30202/15313</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Ida Mulyadi, Titik Khawa Abdul Rahman, Muhammad Faisal</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>