Decision Tree Algorithm for Predicting Alumni Job Competitiveness Through Waiting Time Working

Abstract

The absorption of alumni from universities into the world of work is an essential indicator that universities must pay attention to. One-way universities can pay attention to their alums is through tracer studies, where they can evaluate their curriculum's relevance to what is needed in today's world of work. One aspect that can be seen from the tracer study to assess the competitiveness of alums is the waiting time for alums to get their first job. This is because the sooner alums get jobs, the better the curriculum the university provides to students. This research aims to apply machine learning to predict the waiting time for alums from Telkom University to get their first job and find out what factors influence the waiting time for work. The algorithm used in the research is the Decision Tree with hyperparameter tuning using Grid Search and feature selection application. There are 3 methods of feature selection used for comparison: Spearman's Rank Correlation, Chi-square, and Principal Component Analysis. This research produces the best prediction model in applying Chi-square and hyperparameter tuning with an accuracy of 0.79, recall of 0.79, precision of 0.80, and F1-Score 0.75. Several features, such as the number of companies registered, how to find and get work, internship and practicum experience, ethical competency, discussion, and IT skills, have the biggest effects on the model.