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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Matematika: MANTIK Jurnal Matematika Sains dan Teknologi BAREKENG: Jurnal Ilmu Matematika dan Terapan JCES (Journal of Character Education Society) JTP - Jurnal Teknologi Pendidikan Jurnal Varian JAMBURA JOURNAL OF PROBABILITY AND STATISTICS International Journal of Global Operations Research Parameter: Journal of Statistics VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal Statistika dan Sains Data Jurnal Sains dan Kesehatan
Hasanah, Siti Hadijah
Universitas Terbuka, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Journal : Jurnal Varian

 1 
Analysis of User Satisfication with Graduates in Statistical Study Program Universitas Terbuka Siti Hadijah Hasanah; Dewi Juliah Ratnaningsih
Jurnal Varian Vol 5 No 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v5i1.1331

Abstract

Revolution 4.0 requires the Universitas Terbuka Statistics study program to change the educational curriculum that aims to produce quality graduate competencies. Therefore, to collect informationand evaluate the competence of graduates, it is necessary to conduct tracer study research on each graduate. This study aims to measure user satisfaction with graduate competencies using Gap analysis, Importance-Performance Analysis (IPA), Customer Satisfaction Index (CSI), and a multi-attribute Fishbein model. Based on the value of Gap and Science, the main priority that must be improved by graduates to meet user expectations is the ability to solve problems, generate ideas, and be able to present the results of these ideas in the form of reports/journals. The value of the level of suitability between user satisfaction and the importance of the ability of graduates is very good at 92.87% and a CSI value of 78.25%, which means that overall user satisfaction with graduates is good, besides thatbased on the results of the multi-attribute Fishbein model, an Ao value of 158.20 which means that graduate users have a positive attitude towards the abilities of UT Statistics program graduates.
Comparing SOM, DBSCAN, and K-Affinity Propagation in Labor Economic Patterns Nurmayanti, Wiwit Pura; Yuniarti, Desi; Siringoringo, Meiliyani; Purnamasari, Ika; Putri, Desi Febriani; Hasanah, Siti Hadijah
Jurnal Varian Vol. 9 No. 1 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v9i1.5933

Abstract

The objective of this research is to identify the most effective clustering method for grouping Indonesian provinces by labor–economic indicators to support more precise, data-driven policy formulation. Regional disparities in Indonesia’s economic growth, driven by unequal labor characteristics, remain a significant obstacle to achieving inclusive development. An analytical approach capable of grouping provinces by labor and economic indicators is therefore essential. This study applies a comparative clustering analysis using three unsupervised algorithms: Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and K-Affinity Propagation (K-AP). The dataset consists of five key indicators, namely economic growth, total population, labor force, employment rate, and average wage level obtained from Statistics Indonesia (BPS) for the year 2024. The clustering performance is evaluated using internal validation criteria based on the ratio of within-cluster variation (Sw) to between-cluster variation (Sb), where a smaller ratio indicates more compact, well-separated clusters. The results show that each method produces different clustering structures. SOM and DBSCAN generate three clusters with varying provincial distributions, whereas K-AP produces five clusters with more balanced, representative groupings. The evaluation results indicate ratios of 3.1906 for SOM, 0.2000 for DBSCAN, and 0.1779 for K-AP, indicating that K-AP provides the most optimal clustering performance. These findings confirm that K-Affinity Propagation is the most effective and stable method for classifying Indonesian provinces by labor and economic characteristics. The outcomes of this study provide empirical insights and analytical references for labor-driven economic policy formulation and data-driven regional development planning in Indonesia.
Co-Authors Abdul Rahim Arif, Zainul Chairunnisa, Madiha Dzakiyyah Dewi Juliah Ratnaningsih Eka Julianti Epin Nur Cahya Erlyne Nadhilah Widyaningrum Hendrayani, Hendrayani Ika Purnamasari Isnurani Kertanah, Kertanah Komuna, Avelyn Pingkan M. Fathurahman Maulana, Muhamad Riyan Mujahidin, Ilham Nina Valentika Ningsih, Eva Lestari Nurdiana, Dian Nurmayanti, Wiwit Pura Pangruruk , Thesya Atarezcha Pangruruk Putri, Desi Febriani Rahman, Dela Juliarsih Rif'an, Muhammad Siringoringo, Meiliyani Sri Enny Triwidiastuti Sri Maulidia Permatasari Yuniarti, Desi