Visualization of Lecturer Teaching Evaluation Data Using K-Means Clustering and Tableau Methods - Golda Tomasila; Marchello Gefan Salenussa; Maryo Indra Manjaruni; Ravensca Matatula; Paul Rio Pelupessy; Julius Chrisostomus Aponno (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
In the process, the results of monitoring and evaluating lecturers in each semester are usually only presented in the form of tables and descriptive explanations, but have not yet visualized the data for further analysis. The purpose of this study is to visualize the results of lecturer teaching evaluation using the K-Means Clustering and Tableau algorithms, and is expected to help the faculty and university monitor and evaluate lecturers in each semester in a more objective and informative manner. The results of the study found that the k-means clustering algorithm succeeded in finding the pattern of student clustering on the evaluation of lecturer teaching and based on the visualization of the results of k-means with a tableau it was found that most students gave a positive response to lecturer teaching and only a small number of students gave a poor assessment of lecturer teaching by emphasizing on improving the teaching process, namely consistently carrying out RPS, punctuality and so on