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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics LEMMA: Letters Of Mathematics Education MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Eksakta : Berkala Ilmiah Bidang MIPA Pelita Eksakta Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Applied Data Sciences Journal of Science and Technology MATHunesa: Jurnal Ilmiah Matematika Journal of Mathematics UNP Mathematical Journal of Modelling and Forecasting Journal of Statistics and Data Science
Devni Prima Sari
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Hierarchical Clustering of Education Indicators in Papua Island: A Ward’s Method Approach Prastika, Ifa; Sari, Devni Prima
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9740

Abstract

Education development aims to ensure inclusive, equitable education and increase learning opportunities for all Indonesian citizens. Papua Island is still not an island with a high education level; data on education indicators indicate this in each Regency / City on the island of Papua, with a value below the national average. Identifying districts/cities is needed to improve education, so clustering is carried out using the Ward method. This research aims to group and map regencies/cities on the island of Papua based on education indicators. The results of this study are expected to be a consideration and benchmark for the government in making decisions regarding education in districts/cities on the island of Papua, considering the region's characteristics. This is an applied research with the data type used, namely secondary data on education indicators in Papua Island in 2022. Data sources are obtained from the official website of the Central Bureau of Statistics of each province on the island of Papua. Four education indicators are taken into account in this research, namely the School Participation Rate (SPR), the Gross Enrollment Rate (GER), the Net Enrollment Ratio (NER), and the Average Years of Schooling (AYS), which are then detailed into 10 variables. The cluster analysis process uses Euclidean distance and cluster validation using the Dunn Index. The results showed that 3 clusters formed. Cluster 1 consists of 27 districts/cities; this first group is classified as a high level of education. Cluster 2 consists of 7 districts/cities with a medium level of education, and Cluster 3 has eight districts/cities with a low level of education—cluster results based on the highest Dunn Index validation value of 0.414.
Regional Clustering in Sumatera Based on Walfare Indicators Using Fuzzy C-Means Putri, Rahma Dana; Sari, Devni Prima
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10103

Abstract

Welfare refers to a condition in which individuals have sufficient means to meet both physical and spiritual needs. In Indonesia, welfare is a national goal, yet Sumatra experiences the highest development disparity, contributing to unequal welfare distribution across regions. This study aims to cluster regions in Sumatra based on welfare indicators using the Fuzzy C-Means (FCM) method, analyze cluster characteristics, and provide policy recommendations for decision-makers. FCM is used because it accommodates uncertainty and allows each data point to belong to more than one cluster, making it suitable for welfare analysis. Cluster validity was tested using Partition Coefficient Index (PCI) and Silhouette Coefficient, both indicating that the optimal number of clusters is two. The results show that Cluster 1 consists of 62 regions with relatively higher welfare conditions, while Cluster 2 includes 92 regions with lower welfare characteristics. One notable member of Cluster 2 is Ogan Komering Ulu, with a high membership degree of 0.869. Recommended policies include improving access to clean water and healthcare, enhancing education, strengthening local economies, and delivering targeted social assistance to underdeveloped areas. For Cluster 1, sustainable development efforts should be maintained.
Co-Authors 1004, Anisa Adellia, Clara Febby Adhitya Ronnie Effendie, Adhitya Ronnie Afifah Humayrah Afifah, Wardah Hasna Anggraini, Fira arsilla uswatunnisa Danardono Danardono Dedi Rosadi Deswita, Siska Dina Agustina Dina Agustina, Dina Dina Fakhriyana Emmelia Safani Fadhilah Fitri Febri, Fera Malianis Filfiqri, Hamimatul Gulo, Trimodesman Hardinsyah Gusvira Widuri irwandi irwandi Jayawarsa, A.A. Ketut Jeri Jefrianto Lestari, Adika Risky Media Rosha Medika, Amelia Nurul Miftha Delinda Muhammad Rashif Hilmi Pepi Novianti Prastika, Ifa Pratama, Tasya Putri Puja Ermiati Putri, Elisa Ananda Putri, Muthiara Hazimah Putri, Rahma Dana Putriyana Nursal Rara Sandhy Winanda Riri Indriani Rohadatul Aisy Sadli, Wanda Hamidah Soleha, Annisa Sumita, Nurmaya Tessy Octavia Mukhti Tri Rahmayani Ulfasari Rafflesia Wardana, Yuni Yolandafitri Zulvia