Data Mining Analysis of K-means Algorithm and Decision Tree for Early Detection of Students at Risk of Dropping Out - Akbar, Imam; Samad, Ita Sarmita; Rahmat, Rahmat; Rosmiana, Sri (LPPM Institut Teknologi Telkom Purwokerto, 2025)
Dropout occurs in higher education, where students are unable to complete their studies within a specified timeframe. It has become a significant concern in education due to its substantial impact on individuals, institutions, and society. This study aims to develop a model for predicting the early potential for students' dropout using the K-Means Algorithm and decision trees. The research method consists of a Dataset, Data Preprocessing, K-means implementation, labeling student data, and Decision Tree implementation. This study resulted in 4 clusters. The students in Cluster 1 have an excellent average GPA, a substantial number of credits, and are very active. The students in Cluster 2 have a lower average GPA and are less active than in Cluster 1. The students in Cluster 3 show a relatively good average GPA, which is lower than in Clusters 1 and 2. The number of active students indicates that students in this cluster are much less active or at risk of D.O. than those in clusters 1 and 2. Cluster 4 indicates that the average GPA of students is very low, often close to zero, and they are generally inactive in academic activities. Thus, they are significantly at risk of D.O. at Universitas Muhammadiyah Enrekang. This research provides significant results, both in terms of accuracy and data interpretation. The resulting insights enable universities to make more strategic and targeted decisions, thereby reducing the risk of university dropout rates, increasing resource efficiency, and supporting the overall educational success of students. The accuracy of the resulting model is 98.52% which indicates that the model has excellent performance in classifying students at risk of D.O.