Data Mining for Analyzing Causes of Student Registration Delays at UNMARIS Using Decision Tree
DOI:
https://doi.org/10.52958/iftk.v22i2.12725Keywords:
Data Mining , Student Registration , Registration Delay , Decision TreeAbstract
This study aims to analyze the factors that influence student registration delays at Stella Maris University Sumba (UNMARIS) by utilizing data mining methods using the C4.5 Decision Tree algorithm. The problem of registration delays often occurs and has an impact on the academic administration process and the orderliness of the lecture schedule. The C4.5 algorithm was chosen because it has the ability to process categorical and numerical data and produces decision rules that are easy to interpret. The data used is student data that includes attributes such as GPA, payment status, distance from residence, occupation, semester, and type of registration. The analysis process begins with the data pre-processing stage, calculation of entropy and information gain values, decision tree formation, and evaluation of results. The results of the study show that the payment status and GPA attributes have the highest information gain values, making them the dominant factors that influence the timeliness of student registration. The resulting decision tree model provides a good level of accuracy and is able to classify students into fast, medium, or slow registration categories. This study is expected to assist academics in formulating more effective policies.
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