Perbandingan Algoritma Decision Tree, ID3, dan Random Forest dalam Klasifikasi Faktor-Faktor yang Mempengaruhi Karier Mahasiswa Ilmu Komputer
Article Sidebar
Main Article Content
Abstract
Article Summary
This study aims to compare the performance of three classification algorithms, namely Decision Tree, ID3, and Random Forest, in identifying factors that influence the careers of computer Science students. These algorithms are applied to a dataset that includes various student attributes, such as GPA, programming skills, and completed projects. The results show that Random Forest provides more accurate and stable prediction results than Decision Tree and ID3, especially in reducing the risk of overfitting. Students with high skills in Python and SQL and who focus on software development tend to choose a career in Software Engineering. While those involved in AI/ML-based projects tend to choose Data Science. The conclusions of this study provide valuable insights for educational institutions to design more effective career development strategies for students.
Keywords
Article Keywords
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
Ali, J., Khan, R., Ahmad, N., & Maqsood, I. (2012). Random forests and decision trees. International Journal of Computer Science Issues (IJCSI), 9(5), 272.
Azam, Z., Islam, M. M., & Huda, M. N. (2023). Comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree. IEEE Access.
Chaudhuri, A. K., Ray, A., Banerjee, D. K., & Das, A. (2021). A multi-stage approach combining feature selection with machine learning techniques for higher prediction reliability and accuracy in cervical cancer diagnosis. International Journal of Intelligent Systems and Applications, 10(5), 46.
Gupta, U. G., & Houtz, L. E. (2000). High school students’ perceptions of information technology skills and careers. Journal of Industrial Technology, 16(4), 2-8.
Jha, K., Likhitha, D., Chandana, M. S., Reddy, M. R. P., & Bhargavi, M. (2024, July). Career Prediction Using Machine Learning. In 2024 8th International Conference on Inventive Systems and Control (ICISC) (pp. 118-122). IEEE.
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
Lewis, C. P. (2004). The relation between extracurricular activities with academic and social competencies in school-age children: A meta-analysis. Texas A&M University.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News 2 (3): 18–22.
Mikhalkina, E. V., Skachkova, L. S., & Gerasimova, O. Y. (2019). Academic or non-academic career: What choice do graduates of federal universities make. Terra Economicus, 17(4), 148-173.
Pranckevičius, T., & Marcinkevičius, V. (2017). Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Baltic Journal of Modern Computing, 5(2), 221.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1, 81-106.
Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3-29. DOI: https://doi.org/10.1177/1536867X20909688.
VidyaShreeram, N., & Muthukumaravel, A. (2021, June). Student career prediction using machine learning approaches. In First International Conference on Computing, Communication and Control System (p. 444).
Yang, Z., Yang, J., Rice, K., Hung, J. L., & Du, X. (2020). Using convolutional neural network to recognize learning images for early warning of at-risk students. IEEE Transactions on Learning Technologies, 13(3), 617-630.