IMPLEMENTASI ALGORITMA K-MEANS TERHADAP STATUS TINGKAT DROP OUT MAHASISWA UNIVERSITASESA UNGGUL KAMPUS HARAPAN INDAH

Abstract

Dropout has become a major problem in many universities, significantly impacting students, educational institutions, and the wider community. High dropout rates reflect poor university quality, lowering the reputation and accreditation of the University. The causes of dropout vary, including academic, financial, or other factors. This study aims to develop a more efficient student data processing system to address the dropout problem at Esa Unggul University. Currently, student data management still uses Microsoft Excel, which is not only time-consuming but also prone to input errors. Therefore, this new system uses a data warehouse for data storage, the Python programming language for data processing to identify factors causing dropout, and the K-Means algorithm to identify groups of students with low, medium, and high potential for dropout which are then displayed visually. With a proactive approach such as calling and sending messages to students who are at risk of dropping out, this system is expected to help universities reduce dropout rates and improve the overall quality of education.