Penerapan Algoritma K-Means Berdasarkan Data Tracer Study Di Institut Teknologi Pagar Alam - Sasmita; Inda Anggraini ANGGRAINI; Siti Muntari (BETRIK, 2026)
Tracer studies serve as an essential instrument for higher education institutions to evaluate the alignment between academic curricula and the competencies required in the workforce by systematically tracking alumni data. However, at the Institut Teknologi Pagar Alam, the tracer study process is still conducted manually, resulting in inefficiencies, limited data accuracy, and constraints in performing comprehensive analytical evaluations. This research aims to develop a web-based tracer study system integrated with the K-Means clustering algorithm to automate the grouping of alumni data and enhance the effectiveness of institutional evaluations. The system was developed using the Rapid Application Development (RAD) methodology, which includes requirements planning, prototype design, and system implementation. System validation was carried out through black-box testing using Alpha testing, involving four experts who evaluated the algorithm, database, interface, and system functionality. The implementation results show that the K-Means algorithm successfully categorized alumni into three primary clusters based on employment status and income, namely high-income employed alumni (195 individuals), medium-income employed alumni (45 individuals), and unemployed or low-income alumni (36 individuals). Furthermore, the Alpha testing yielded an average score of 4.673, equivalent to 93.46%, which is classified as Highly Feasible. These findings demonstrate that the system performs effectively in collecting, processing, and analyzing tracer study data. In conclusion, the developed system not only improves the efficiency of data acquisition but also provides analytical insights that support curriculum evaluation, graduate quality mapping, and strategic academic decision-making at the Institut Teknologi Pagar Alam.