OPTIMASI JUMLAH CLUSTER PADA K-MEANS CLUSTERING MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK PENGELOMPOKAN UKT MAHASISWA - Ira Fazira; Zahratul Fitri; Risawandi (LPPM Universitas Abdurrab, 2025)
The determination of the Single Tuition Fee (UKT) group in higher education faces challenges in terms of distribution fairness due to the inappropriate grouping of students' socio-economic conditions. The K-Means algorithm, while effective in handling large-scale data at good computational speeds, has a drawback in determining the optimal number of clusters automatically. This study aims to implement the integration of Particle Swarm Optimization (PSO) with K-Means Clustering in the grouping of student UKT data and evaluate the improvement of the quality of clustering produced compared to conventional methods. The study uses a dataset of 437 new students of the Faculty of Engineering in 2024 from Malikussaleh University with 8 attributes that describe family socioeconomic conditions. The research stages include pre-processing of data, determination of the optimal number of clusters using PSO, implementation of K-Means clustering with optimal K, model evaluation using Silhouette Coefficient and Davies-Bouldin Index, and model comparison using the elbow method. The results of the study showed that PSO succeeded in determining the optimal number of clusters as many as 3 clusters. The implementation of K-Means with K=3 resulted in the distribution of clusters: cluster 0 (40 students/9.2%), cluster 1 (93 students/21.3%), and cluster 2 (304 students/69.6%). Clustering quality evaluation resulted in a Silhouette Coefficient of 0.278062 and a Davies-Bouldin Index of 1.430505 indicating adequate cluster formation with fairly good internal cohesion and reasonable separation between clusters. Comparison with the conventional K-Means method using the Elbow Method shows the advantage of PSO-K-Means with a higher Silhouette Coefficient (0.278062 vs 0.250300) and a competitive Davies-Bouldin Index (1.430505 vs 1.315400). This research proves that the combination of PSO and K-Means can provide a more optimal solution in the grouping of student UKT to support a fairer determination of tuition fees based on family economic ability.