Optimalisasi Akreditasi Perguruan Tinggi dengan Orkestrasi Business Intelligence Berbasis K-Means dan OLAP - Sakinah, Putri; Eko Syaputra, Aldo; Rahman, Zumardi; Fajri, Muhammad; Fatwa Rachmansyah, Haikal (Pustaka Galeri Mandiri, 2025)
University accreditation is a key indicator in assessing the quality and competitiveness of higher education institutions. A high accreditation score reflects strong academic standards, institutional competitiveness, and trust from students, industry, and government. However, the accreditation process is highly complex as it involves multiple aspects, such as curriculum, student performance, faculty competence, as well as the quality of research and community service. Universities often face challenges in managing, analyzing, and presenting academic data systematically, which results in suboptimal strategic decision-making for improving accreditation. The limited use of Business Intelligence (BI) in academic data analysis reduces the effectiveness of identifying the main factors influencing institutional performance. This study aims to develop an academic analysis model based on BI by integrating K-Means Clustering to group academic performance according to specific patterns, Silhouette Score to evaluate clustering quality, and Online Analytical Processing (OLAP) to present academic data in an interactive multidimensional form. The research data were obtained from the Institute for Quality Assurance and Educational Development (LPPPM), covering faculty, student, research, and community service data, as well as other accreditation indicators. The research method includes data collection and preprocessing, application of K-Means for clustering, evaluation using Silhouette Score, and the development of an OLAP dashboard for exploring academic data across relevant accreditation dimensions. The results of the study show that the K-Means method with k = 2 produces the most optimal grouping of study program academic performance based on the highest Silhouette Score value, which is then successfully visualized multidimensionally through OLAP to clarify the distribution, patterns, and differences in characteristics between clusters in an interactive dashboard.