Comparison of Clusterization of Higher Education Institutions in Regions XII Maluku and North Maluku based on the Science and Technology Index (Sinta)

Haris Kolengsusu, Agung K Henaulu, Farida Mony

Abstract


This research aims to analyze the comparison of clustering results of universities based on data sourced from the Science and Technology Index (Sinta). The analysis results will serve as a guide for all higher education institutions in the XII Maluku and North Maluku regions to improve their performance in the three pillars of higher education to achieve better clustering. This study was conducted by extracting Sinta data from the years 2019 to 2021. The research results show that based on the Total All Score, the universities in Maluku are ranked in descending order from the highest to the lowest score. The main cluster is occupied by Pattimura University with a total score of 16.04, followed by State Polytechnic of Ambon with a total score of 13.45. In the middle cluster, we have Indonesian Christian University of Maluku with a total score of 9.19, and in the basic cluster, we have Darussalam Ambon University with a total score of 6.62, Maluku Husada Health Sciences Institute with a total score of 4.79, Doktor Husni Ingratubun Tual University with a total score of 2.5, and Institute of Technology and Business, Ambon School of Computer Science with a total score of 2.37. As for North Maluku, the universities are ranked in descending order from the highest to the lowest score. The main cluster is led by Universitas Kharun with a total score of 14.01. In the middle cluster, we have Muhammadiyah University of North Maluku with a total score of 11.62, and Halmahera University  with a total score of 9.45. In the basic cluster, we have Nuku University with a total score of 5.45, Pasifik Morotai University with a total score of 5.3, Hein Namotemo University with a total score of 4.63, and Bumi Hijrah University Tidore  with a total score of 3.31


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References


Y. Sasmita, M. Muhsi, and M. Walid, “Klasterisasi Perguruan Tinggi Swasta di Madura Berdasarkan Kinerja Sumber Daya Manusia dan Mahasiswa Menggunakan Metode K-Means Clustering,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2157, 2022, doi: 10.30865/mib.v6i4.4431.

A. D. Reskia, “Strategi Pencitraan Public Relations Stie Nobel Indonesia Makassar Dalam Menguatkan Citra Sebagai Sekolah Tinggi Terbaik Di Sulawesi Versi Klasterisasi Perguruan Tinggi Lldikti Wilayah Ix,” Http://Repository.Unhas.Ac.Id/, 2021.

Arwildayanto, Arifin, and A. Suking, “Analisis Deskriptif Daya Saing Perguruan Tinggi,” Ilmu Pendidik. J. Kaji. Teor. dan Prakt. Kependidikan, vol. 5, no. 1, pp. 25–34, 2020.

H. ULM, “Berita ULM (ULM Menuju Klaster Satu),” 2020.

R. P. Primanda, A. Alwi, and D. Mustikasari, “DATA MINING SELEKSI SISWA BERPRESTASI UNTUK MENENTUKAN KELAS UNGGULAN MENGGUNAKAN METODE K-MEANS CLUSTERING (Studi Kasus di MTS Darul Fikri ),” Komputek, vol. 5, no. 1, p. 88, 2021, doi: 10.24269/jkt.v5i1.686.

S. Laohakiat and V. Sa-ing, “An incremental density-based clustering framework using fuzzy local clustering,” Inf. Sci. (Ny)., vol. 547, no. 8, pp. 404–426, 2021, doi: https://doi.org/10.1016/j.ins.2020.08.052.

R. Moriyasu and T. Kobayashi, “Impact of career education on high school students’ occupational choice: Evidence from a cluster-randomized controlled trial,” Japan World Econ., vol. 63, no. July, p. 101146, 2022, doi: 10.1016/j.japwor.2022.101146.

S. W. Hemelt, M. A. Lenard, and C. G. Paeplow, “Building bridges to life after high school: Contemporary career academies and student outcomes,” Econ. Educ. Rev., vol. 68, no. February 2018, pp. 161–178, 2019, doi: 10.1016/j.econedurev.2018.08.005.

E. B. Gosno, I. Arieshanti, and R. Soelaiman, “Implementasi KD-Tree K-Means Clustering,” J. Tek. Pomits, vol. 2, no. 2, pp. A432–A437, 2013.

S. Susilawati, S. Sudrajat, Y. Nugraheni, A. A. Rachmat, and H. N. Chamidy, “Peran Penting Tracer Study sebagai Salah Satu Penilaian dalam Peningkatan Klasterisasi Politeknik Negeri Bandung,” Proceeding Indones. Carr. Cent. Netw. Summit 2019, vol. 1, no. 1, pp. 201–205, 2019, [Online]. Available: http://e-journals.unmul.ac.id/index.php/ICCN/article/view/3126

A. Nurdin, L. Hadjaratie, and R. Dai, “Sistem informasi manajemen pemeringkatan kemahasiswaan fakultas teknik universitas negeri gorontalo,” vol. 1, no. 2, pp. 156–162, 2021.

R. Watrianthos, R. Handayani, W. Simatupang, D. Irfan, and M. Muskhir, “Penerapan Metode PROMETHEE-GAIA Dalam Pemeringkatan Perguruan Tinggi di Indonesia,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 138, 2022, doi: 10.30865/mib.v6i1.3419.

M. Rozikin, M. Kurniawati, and K. Aliyyah, “Strategi Peningkatan Peringkat Perguruan Tinggi Versi Kemristekdikti (Studi pada Universitas Brawijaya),” Kelola J. Manaj. Pendidik., vol. 7, no. 1, pp. 86–97, 2020, doi: 10.24246/j.jk.2020.v7.i1.p86-97.

Kemendikbudristek, “Pengumuman Klasterisasi Perguruan Tinggi,” 2023. [Online]. Available: https://lldikti15.kemdikbud.go.id/pengumuman/pengumuman-klasterisasi-perguruan-tinggi/

D. Handini, “Klasterisasi Perguruan Tinggi Berbasis Kinerja Penelitan dan Pengabdian kepada Masyarakat, DRTPM Ditjen Diktiristek Gelar Sosialisasi Science and Technology Index (SINTA),” Kabar Dikti, 2022. https://www.dikti.kemdikbud.go.id/kabar-dikti/kabar/klasterisasi-perguruan-tinggi-berbasis-kinerja-penelitan-dan-pengabdian-kepada-masyarakat-drtpm-ditjen-diktiristek-gelar-sosialisasi-science-and-technology-index-sinta/

H. H. Lathabai, “ψ-index: A new overall productivity index for actors of science and technology,” J. Informetr., vol. 14, no. 4, 2020, doi: https://doi.org/10.1016/j.joi.2020.101096.

H. Guruler, A. Istanbullu, and M. Karahasan, “A new student performance analysing system using knowledge discovery in higher educational databases,” Comput. Educ., vol. 55, no. 1, pp. 247–254, 2010, doi: 10.1016/j.compedu.2010.01.010.

V. Kurnia Bakti and A. Rakhman, “Klasterisasi Dokumen Penelitian Perguruan Tinggi Menggunakan K-Means Clustering, Sebagai Analisa Penerapan Sistem Temu Kembali,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 10, no. 3, pp. 167–169, 2021, doi: 10.30591/smartcomp.v10i3.2941.

J. Saha and J. Mukherjee, “CNAK: Cluster number assisted K-means,” Pattern Recognit., vol. 110, 2021, doi: https://doi.org/10.1016/j.patcog.2020.107625.

P. W. Cahyo, “Klasterisasi Tipe Pembelajar Sebagai Parameter Evaluasi Kualitas Pendidikan Di Perguruan Tinggi,” Teknomatika, vol. 11, no. 1, pp. 49–55, 2018, [Online]. Available: https://ejournal.unjaya.ac.id/index.php/teknomatika/article/view/271




DOI: http://dx.doi.org/10.24014/sitekin.v21i1.24826

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