The AHP-TOPSIS Integration of a Decision Support System to Analyze and Enhance Student Engagement Based on Cus-tomer Relationship Management Approach - Trias Pungkur Kusumaningrum; Andjar Hernanto; Danik Riawati (Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia, 2026)
Student engagement has become an important issue in higher education because it is closely related to academic achievement, learning effectiveness, and student retention. In digital learning environments, institutions require a systematic and data-driven approach to evaluate student engagement and support strategic academic decision-making. However, most previous studies still focus on descriptive and predictive analysis without providing structured decision support mechanisms integrated with relationship management strategies. Therefore, this study aims to develop an integrated Decision Support System (DSS) by combining the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods within a Customer Relationship Management (CRM) approach to analyze and enhance student engagement. The study was conducted at Politeknik AKBARA Surakarta involving 47 students as respondents. The evaluation criteria consisted of Behavioral Engagement, Emotional Engagement, Cognitive Engagement, LMS Interaction, and Academic Participation. The AHP method was applied to determine the priority weights of the criteria, while TOPSIS was used to rank and classify student engagement levels. The results showed that the proposed framework successfully classified students into high, medium, and low engagement categories objectively and systematically. The CRM approach further translated these classifications into strategic academic actions such as retention programs, engagement improvement, and intensive intervention strategies. The integration of AHP-TOPSIS and CRM provides a comprehensive framework for transforming student engagement data into actionable academic decision support, enabling institutions to implement more personalized, adaptive, and data-driven student engagement management.