EXPERIMENTAL COMPARISON OF MACHINE LEARNING ALGORITHM PERFORMANCE FOR OPTIMIZING ELECTIVE SUBJECT SELECTION IN PHASE F OF THE MERDEKA CURRICULUM

• Published in: Jurnal Teknik Informatika (Jutif) Vol. 5; no. 4; pp. 241 - 251
• Main Author: Mulyadi, Dedy
• Format: Journal Article
• Language: English
• Published: 01.08.2024
• ISSN: 2723-3863; 2723-3871
• DOI: 10.52436/1.jutif.2024.5.4.1983

In phase f of the Merdeka Curriculum, electives are an important element at the senior high school level. Students are faced with the challenge of choosing four out of twelve elective subjects that are relevant to their talents, interests, further study plans and career goals over a two-year study period. Applying machine learning with the right algorithm is a solution for the effectiveness and efficiency of elective selection. The dataset used comes from the 10th grade report card data, the results of the interest, aptitude, further study, and career choice tests, and the manual selection of electives chosen by students in the previous year. The use of a small data set requires a cross-validation method to improve the generalizability of the model and to optimize the data set, thereby increasing the validity of the results. The test will be conducted using an application that tests five machine learning algorithm models suitable for small datasets, namely Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and k-Nearest Neighbors. The test focuses on comparing the performance of the five algorithms based on the best accuracy, recall, and confusion matrix and the results obtained Support Vector Machine (SVM) algorithm has the best performance results by achieving the highest accuracy of 57.3770%, the highest recall of 0.574, and the highest true positive (TP) of 0.574. The Support Vector Machine (SVM) algorithm will be a recommendation for further research, namely the development of machine learning for the selection of f-stage elective subjects at Atisa Dipamkara senior high school, to provide relevant guidance to students in making decisions regarding the selection of elective subjects more accurately and according to their respective characteristics.