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All Journal Jurnal Informatika dan Teknik Elektro Terapan Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Building of Informatics, Technology and Science Jurnal Teknik Informatika C.I.T. Medicom MEANS (Media Informasi Analisa dan Sistem) Jurnal Informatika Terpadu JUSTIN (Jurnal Sistem dan Teknologi Informasi) D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Artificial Intelligence and Engineering Applications (JAIEA) J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi dan Manajemen
Dikananda, Arif Rinaldi
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Journal : J-Icon : Jurnal Komputer dan Informatika

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APPLICATION OF K-MEANS ALGORITHM IN KINDERGARTEN SCHOOL LOCATION CLUSTERING OF SCHOOL SELECTION STRATEGY BY PARENTS Syifa, Nurkhasanah Fadhila; Martanto, Martanto; Dikananda, Arif Rinaldi; Rohman, Dede
J-Icon : Jurnal Komputer dan Informatika Vol 13 No 1 (2025): March 2025
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v13i1.20202

Abstract

This research aims to improve the kindergarten school location clustering model to support parents' school selection strategies. The main issue raised is the need to understand parents' preferences more deeply in choosing the right school for their children. To achieve this goal, the K-Means algorithm was applied and analyzed to cluster parents' data based on characteristics such as occupation, education, and residential location. This research utilizes a quantitative method with an exploratory descriptive approach. The results showed that the K-Means algorithm successfully formed two clusters with different characteristics. Cluster_0 includes groups with more centralized or close locations, education levels that tend to be low, and types of jobs that are at the lower middle economic level, while cluster_1 groups with more dispersed or distant locations, higher education levels, and jobs that are at higher economic levels. The quality of the resulting clusterization is considered quite good, with a Davies-Bouldin Index (DBI) value of 0.151. The application of the K-Means algorithm is proven to be effective in identifying groups of parents with different preferences, so it can be a foundation for schools in developing more targeted and tailored service strategies. This research makes an important contribution to the application of clustering techniques to support marketing strategies and decision-making in the early childhood education sector.
Co-Authors -, Martanto - ., Martanto abdullah, nur syarief Ade Rizki Rinaldi Ahmad Faqih Ahmad Rifai Almadina, Muhammad Fitrian Shousyade Ardhanur, Ichlas Artoti, Azzahra Rizky Bahrul Jawahir, Muhammad Fasa, Saefullah fatimah, lilis Fithriyani, Nurul Muna Gifthera Dwilestari Hamam, Moh Hardika Hardika, Hardika hermansyah, Muhammad ikhwan adholf Irfan Ali, Irfan Khoirunisa, Pitria khusnul khotimah Martanto Martanto . Mirna Mirna Muhamad Abdul Fatah Muhammad Hilman Mulyawan Mulyawan, Mulyawan Muzani, Muhamad Nur Hermawan, Ilham Nuraeni Nurcholis, Rifki Odi Nurdiawan Pratama, Fidya Arie Pura, Panji Adi Purnamasari, Ade Irma Putri, Haidah Putri, Niken Zeliana Raditya Danar Dana Ramadan, Muhamad Firly Rifa'i, Ahmad Rifa’I, Ahmad Rohman, Dede Rohmat, Cep Lukman Saeful Anwar, Saeful Subandi, Husein Syahri, Ibnu Nava Syifa, Nurkhasanah Fadhila Wijaya, Nurjana Adi Yunus, Shofian