K-Means Clustering Method On Academic Advising Management And Early Detection Of Student Dropout (Sequential Explanatory Mixed Method Study At UIN Sunan Ampel Surabaya And IAIN Kediri) - Fauziyah Laili, Ummiy; Tanzeh, Ahmad; Efendi, Nur; Gufron, Moch (CV. Inara, 2024)
In this millennial era, education is a need that must be prioritized. It can be seen from the progress in the field of education in Indonesia. Its development is increasingly showing rapid significance. But the irony is that the rapid development of education is also not free from various kinds of severe and diverse challenges and fierce competition at the regional and national levels. One crucial problem that illustrates the success and failure that often occurs in higher education is dropout. The student dropout rate is often associated with public interest in higher education, public interest often concludes that if the dropout rate is high then the quality of the campus can be said to be low, conversely if the student dropout rate is low then the quality of the campus is high. Research was conducted to find the application of the k-means clustering method in early detection of potential student dropouts using the k-means data meaning algorithm. The approach in this research is mixed methods research explanatory design model, where researchers conduct quantitative research with manova analysis and followed by qualitative research, the data used in quantitative methods are used to map students with the highest dropout potential to the lowest dropout potential using k-means clustering data analysis, while qualitative research uses a data base.The results of research on data analysis of mapping students who are potential dropouts resulting from k-means clustering data analysis for IAIN Kediri obtained 10 clusters with a silhouette coefficient value of 0.790 and 3 clusters with a silhouette coefficient value of 0.770. While at UIN Sunan Ampel Surabaya, 8 clusters were obtained with a silhouette coefficient value of 0.840 and 5 clusters with a silhouette coefficient value of 0.820. Based on the silhouette coefficient value data both at IAIN Kediri and UIN Sunan Ampel which is between 0.7 < SC ≤ 1, it is concluded that the cluster model obtained has a strong structure. The effect of k-means clustering method on academic advising management is explained from the results of MANOVA analysis for student data of UIN Sunan Ampel Surabaya shows there is a significant difference between k-means clustering method on academic advising management at UIN Sunan Ampel Surabaya with the value of F(469.179) = 8, p = 0.000., and there is a significant effect between k-means clustering method on academic advising management at IAIN Kediri with the value of F(244.8227) = 10, p = 0.000.While the effect of the k-means clustering method on early detection of student dropout is explained from the results of Manova analysis for data from UIN Sunan Ampel Surabaya which shows a significant influence between the k-means clustering method on early detection of student dropout at UIN Sunan Ampel Surabaya with a value of F (1,272,286) = 8, p 0. 000 there is a significant influence between the k-means clustering method on early detection of student dropout at IAIN Kediri. the results of qualitative data analysis are presented in 4 major sections, namely regarding the management of academic advising in planning, organizing, implementing and supervising academic advising in dropout prevention efforts at UIN Sunan Ampel Surabaya and IAIN Kediri.