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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
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 3 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i3.1443

Abstract

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. Keywords: Business Intelligence; Higher Education Accreditation; K-Means Clustering; Silhouette Score; OLAP.
Co-Authors Anggi Hadi Wijaya Acihmah Sidauruk Adi Arga Arifnur Afdhal Dinilhak Afrianto, Nurdi Ahmad Fathoni Al Ghifari, Ryan Al-Obaidi, Abdulkareem Sh. Mahdi Aldo Eko Syaputra Almaly, Muhammad Andini, Dhiya Safira Arbi Yasin, Arbi Asep Bayu Dani Nandiyanto Asti, Ajeng Dwi Astuti, Asita Puji Ayun, Aisyah Qurrata Azizah, Tiara Budi Permana Putra Budy Satria Candra Surya Dewi, Mauseni Wantika Dhiani Dyahjatmayanti Dian Usdiyana Diningsih, Suci Marta Dwi Asti, Ajeng Ernawati Sinaga Fahreri, Humayra Fatwa Rachmansyah, Haikal Fauzi, Fajar Miraz Fiandini, Meli Fitri Wahyuni Hamandia, Muhammad Hariani Manurung, Kiki Hayati, Nova Hendra, Yomei Herliani, Aprida Hertimi, Sri Khairi, Luthfil Khairuno, Rehan Lia Mirani Liga Mayola Mahyuni, Siska Rita Maidelwita, Yani Manurung, Kiki Hariani Marjanah, Marjanah Maulana, Fajar Muhammad Fajri Muhammad Thoriq Murni, Aprilia Ningsih, Lidya Nurfiah Nurfiah, Nurfiah Nurhapsari, Tiara Putri, Sherly Ananda Ragadhita, Risti Rahman, Arifan Rahman, Zumardi Rahmi Eka Putri Rahmi Eka Putri, Rahmi Eka Rahmi, Siti Fadhilah Rina Maryanti Rini Rahmayanti Risnawati Risnawati Santiuly Girsang, Gabriela Chelvina Siti Rodhiyah, Siti Sri Anggraeni Suhaimi Suhaimi Syauqi, Muhammad Dawi Thoriq, Muhammad Wahyudi Wahyudi Wahyudi Yofhanda Septi Eirlangga Za'ba, Nasir Zamora, Sasya