<![CDATA[This study aims to analyze student satisfaction patterns regarding lecturer teaching performance by integrating K-Means Clustering and Random Forest algorithms. The research data includes 5,260 observations analyzed based on service quality dimensions and academic attributes. The results using the Elbow Method and Silhouette Score established three optimal clusters representing Very Satisfied (score 4.84), Moderately Satisfied (score 4.04), and Dissatisfied (score 3.04) segments. Furthermore, the Random Forest algorithm demonstrated an accuracy of 47% on 1,576 test data and successfully identified that Semester Credit Load (SKS) is the most dominant determinant influencing satisfaction, with an importance value of 47.46%. A unique finding shows that students with the highest academic load (average 20.81 SKS) are actually in the Very Satisfied segment. This study concludes that more intensive student academic engagement correlates positively with appreciation for lecturer teaching quality. These results provide strategic guidance for university management to improve services based on student academic profiles.]]>
Copyrights © 2026
