The Implementation of Decision Tree Classification Techniques to Predict the Duration of Students Completing the Thesis at PTIK FKIP UNS

Halim Perdana Kesuma (Universitas Sebelas Maret)
Dwi Maryono (Universitas Sebelas Maret)
Febri Liantoni (Universitas Sebelas Maret)

Journal of Informatics and Vocational Education
Vol 5, No 2 (2022): Journal of Informatics and Vocational Education - July

This paper aims to determine the reasons why students take a long time in compiling their thesis. The slowness of students in compiling will have an impact on their graduation. This is a serious problem faced by educational institutions. Out of 328 students at PTIK FKIP UNS who took thesis credits, only 85 were able to graduate on time. Therefore, this study was conducted to identify the causes. The research data was taken from the alumnus class of 2012 to 2017. The data was processed using RapidMiner software. The technique used was the decision tree classification technique with the C4.5 algorithm, and to optimize the accuracy of the model, the Particle Swarm Optimization (PSO) algorithm was also added. This study got an accuracy rate of 76% and an AUC score of 0.733.