Students Demography Clustering Based on The ICFL Program Using K-Means Algorithm

• Published in: JOIV : international journal on informatics visualization Online Vol. 7; no. 2; p. 555
• Main Authors: Andreswari, Rachmadita, Fauzi, Rokhman, Izzati, Berlian Maulidya, Widartha, Vandha Pradwiyasma, Pramesti, Dita
• Format: Journal Article
• Language: English
• Published: 20.06.2023
• ISSN: 2549-9610; 2549-9904
• DOI: 10.30630/joiv.7.2.1916

Independent Campus, Freedom to Learn (ICFL) Program is one of the manifestations of student-centered learning. This program can help students reach their full potential by allowing them to pursue their passions and talents. This study aims to see how the segmentation of students participating in the ICFL program is based on demographic data. This research is based on survey responses from students participating in the ICFL program. The method used in this study is input data preparation, pre-processing, data cleansing, and data analysis. The information will be pre-processed before being utilized and evaluated. To help produce better outcomes in data clustering, the K-Means clustering approach is used, which is processed using the Python computer language. The data is clustered using the K-Means clustering approach based on gender characteristics, Grade Point Average (GPA), university entrance selection, ICFL category, and year or semester when participating in ICFL. This study resulted in three clusters with each of its criteria. The dominant gender is found in clusters 2 (100% female) and 3 (100% male). Software Development was the most popular ICFL category among students in cluster 1, accounting for 67%, while Design and Analysis Information Systems was the most popular in clusters 2 and 3. The most dominant ICFL program is found in three clusters. ICFL - Internship program in which at least 40% of participants come from each cluster. The research results are expected to assist stakeholders in evaluating the implementation of the ICFL program.