Data Mining Analysis for KIP Scholarship Eligibility Using Integrated DBSCAN and TOPSIS - Imam Akbar; Chyquitha Danuputri; Rahma; Ita Sarmita Samad (Asosiasi Doktor Sistem Informasi Indonesia, 2026)
This study aims to objectively analyze the feasibility of prospective recipients of the Smart Indonesia Card Scholarship (KIP-K) by integrating the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The research dataset consists of 287 data on prospective scholarship recipients with 11 main attributes that reflect the socio-economic and academic conditions of students. The research process includes data collection, pre-processing, transformation of categorical attributes into numerical values using a linear weighting scheme, cluster analysis using DBSCAN, and candidate ranking using TOPSIS. DBSCAN is used to identify cluster patterns and detect anomalies in the data of potential recipients, while TOPSIS is used to rank candidates based on proximity to the ideal solution. The results of the grouping produced 10 clusters and one noise cluster that showed a variety of socio-economic characteristics of prospective scholarship recipients. The results of the ranking show that some of the candidates with the highest TOPSIS scores come from clusters with higher levels of economic vulnerability. In addition, some of the high-scoring candidates also came from the noise cluster, indicating that even though they did not belong to a particular group, they still met the eligibility criteria based on a multi-criteria evaluation. These findings show that the combination of DBSCAN and TOPSIS has the potential to support the process of analyzing the eligibility of scholarship recipients in a more systematic and data-driven manner.