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All Journal Jurnal Masyarakat Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Hartono Cahyadi, Gabriel Ekoputra
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi CRISP-DM Pada Analisis Pembangunan Pendidikan Prasekolah Menurut Kabupaten/Kota di Indonesia Iranti, Putri Chandra; Kurniawan, Dedy; Sanjaya, M Rudi; Rifai, Ahmad; Syahbani, M Husni; Hartono Cahyadi, Gabriel Ekoputra; Sari, Purwita
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5957

Abstract

Preschool education through Kindergarten (TK) plays a crucial role in child development in Indonesia, yet unequal access remains a significant issue. This study evaluates the need for preschool infrastructure development using the K-Means clustering algorithm implemented through RapidMiner. Regional clustering is based on the number of students, number of TK schools, Human Development Index (HDI), poverty rate, population size, and unemployment rate. The CRISP-DM methodology is applied, involving stages of understanding, preparation, modeling, evaluation, and deployment. Data from the Central Bureau of Statistics (BPS) and the Ministry of Education's Dapodik system are utilized, incorporating Z-transformation normalization and data cleansing. The clustering results reveal three main clusters with the lowest Davies-Bouldin Index (DBI) at K=3, scoring 0.205. With a total of 514 districts/cities in Indonesia, the results of the needs of each cluster were obtained, namely Cluster 0 consisting of 402 districts/cities requiring increased participation, Cluster 1 covering 49 districts/cities requiring educational facilities, Cluster 2 covering 63 districts/cities requiring the construction of new schools. This study provides valuable insights into addressing disparities in preschool education access and offers guidance for better resource allocation and policy decisions aimed at improving early childhood education infrastructure.
Aspect-Based Sentiment Analysis on Nickel Mining Activities in Raja Ampat to Support Sustainable Development Goals Karima, Dzakiah Aulia; Indah, Dwi Rosa; Firdaus, Mgs Afriyan; Sanjaya, M Rudi; Hartono Cahyadi, Gabriel Ekoputra
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026 (Ongoing)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.77146

Abstract

Nickel mining in Raja Ampat has triggered significant public reaction, particularly on social media, due to its environmental and social impacts. However, public opinion on this issue has not been systematically analyzed. This study aims to examine public sentiment toward this issue using the Aspect-Based Sentiment Analysis (ABSA) approach with four classification algorithms: Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, and Random Forest, all optimized through the Particle Swarm Optimization (PSO) method. Data was collected from X between June 1 and June 30, 2025, and analyzed based on three main aspects, namely environmental, social, and economic, with a total of 4,025 datasets. The analysis shows that negative sentiment dominates over positive sentiment, with the environmental aspect being the main focus, especially regarding coral reef damage and marine pollution. Among the four models used, the optimized Support Vector Machine algorithm achieved the highest performance with an accuracy of 87.5%. These findings are expected to serve as an evaluation for the government regarding mining permits to formulate policies that support the achievement of SDG 14 (Life Below Water) and SDG 15 (Life on Land).
Co-Authors Ahmad Rifai Dwi Rosa Indah Iranti, Putri Chandra Karima, Dzakiah Aulia M HUSNI SYAHBANI, M HUSNI Mgs Afriyan Firdaus Purwita Sari, Purwita Sanjaya, M Rudi Wiwik Handayani