Open AccessAnalysis K-Means Clustering to Predicting Student Graduation
Author(s) -
Masna Wati,
Wulandari Rahmah,
Niken Novirasari,
. Haviluddin,
Edy Budiman,
Islamiyah
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1844/1/012028
Subject(s) - graduation (instrument) , cluster analysis , cluster (spacecraft) , field (mathematics) , computer science , variable (mathematics) , principal component analysis , machine learning , mathematics , artificial intelligence , mathematical analysis , geometry , pure mathematics , programming language
The prediction of students’ graduation outcomes has been an important field for higher education institutions because it provides planning for them to develop and expand any strategic programs that can help to improve student academics performance. Data mining techniques can cluster student academics performance in predicting student graduation. The aim of this study is to analysis the performance of data mining techniques for predicting students’ graduation using the K-Means clustering algorithm. The data pre-processing used for data cleaning, and data reducing using Principle Component Analysis to determine any variables that affect the graduation time. This algorithm processes datasets of student academics performance numbering 241 students with 16 variables. Based on the clustering using K-means, the highest accuracy rate is 78.42% in the 3-cluster model and the smallest accuracy rate is 16.60% in the 4-cluster model. The influential variable in predicting student graduation based on the value of the loading factor is the GPA total of the 1st to 6th semester.