Laras Mayangda Sari, -
(2024)
IMPLEMENTATION OF CLASSIFICATION TECHNIQUES FOR
FINDING THE RELEVANCE OF FIELDS AND SUPERVISORS
IN FINAL PROJECTS.
International Conference on Control & Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence (CERIA 2024)..
(Submitted)
Abstract
This study aims to analyze the alignment
between research fields and supervisors in the final project
documents of Information Systems students. This descriptive quantitative research with survey method. The sample used of student final project documents. The sample was selected using the purposive sampling technique, focusing on Information Systems students' final projects from 2021-2023. A validation sheet was used as the research instrument to assess the alignment between fields and supervisors. The research procedure began with data labeling of fields of expertise and supervisors, conducted by two expert lecturers. The data was then analyzed using classification techniques supported by Naïve Bayes Classifier, Decision Tree, Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbor (KNN) algorithms to determine the accuracy of the alignment between research fields and supervisors. The algorithm results showed that the alignment between research fields in the final projects and supervisors' expertise ranged from 50-70% accuracy. The algorithm performance review indicated that the Random Forest algorithm was the best for both types of data, with an accuracy of 66.66% (supervisors) and 73.00% (fields). Further test results indicated that Random Forest is effective in predicting the alignment between fields and supervisors for final project titles.
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