Klasifikasi Performa Akademik Siswa Menggunakan Metode Decision Tree dan Naive Bayes





academic, classification, data mining, decision tree, naive bayes


Getting good academic performance is the goal of the learning process carried out by the education office under the auspices of the Ministry of Education and supervised by the government. Governments that want to be successful in educating students should pay attention to their new generation because they are the future successors of the nation. Students of all levels are the benchmark for a country's success.  Therefore, it is necessary to know the student's academic performance from an early age, in order to get special treatment related to the student's learning achievement.  In this study, the academic achievement of students from various levels of education such as elementary, middle, and high schools was tried to be determined by applying various data mining classification methods such as Decision Tree and Naive Bayes. This data grouping is open where the data can be accessed easily and can be used in future research.  The data grouping is divided into 3 categories, namely Low (L), Medium (M) and High (H). From the results of the dataset trial, it shows that the highest classification accuracy is Decision Tree of 83.89% and Naive Bayes of 85.97%. Thus the Naïve Bayes method is more accurate in grouping students' academic performance data.   The results of this study will be used to create a student grouping system based on student learning performance using the appropriate algorithm. So that his contribution later when creating the application system for the formation of study groups can use the Naïve Bayes method.


Download data is not yet available.


Abdul Rahman, Destiarini, & Kuswanto, J. (2021). Fuzzy Logic Recommended Student Learning Levels. Jurnal Informatika Polinema, 7(2). https://doi.org/10.33795/jip.v7i2.531

Aljarah, I. (2022). Students’ Academic Performance Dataset. Kaggle. Retrieved April 15, 2022, from https://www.kaggle.com/datasets/aljarah/xAPI-Edu-Data

Alturki, S., Alturki, N., & Stuckenschmidt, H. (2021). Using Educational Data Mining To Predict Students’ Academic Performance For Applying Early Interventions. Journal of Information Technology Education: Innovations in Practice, 20. https://doi.org/10.28945/4835

Asruddin, Rahman, A., & Rambe, J. K. (2020). Analisa SWOT Pengembangan Media Belajar Sejarah Di Sekolah Menengah Pertama Kelas IX Semester Ganjil. INTECH, 1(1), 8–16.

Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01). https://doi.org/10.38094/jastt20165

Hameed, I. A. (2016). A simplified implementation of interval type-2 fuzzy system and its application in students’ academic evaluation. 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2019, 650–656. https://doi.org/10.1109/FUZZ-IEEE.2016.7737748

Jena, M., & Dehuri, S. (2020). Decision tree for classification and regression: A state-of-the art review. In Informatica (Slovenia) (Vol. 44, Issue 4). https://doi.org/10.31449/INF.V44I4.3023

Karacan, I., Sennaroglu, B., & Vayvay, O. (2020). Analysis of life expectancy across countries using a decision tree. Eastern Mediterranean Health Journal, 26(2). https://doi.org/10.26719/2020.26.2.143

Nida Uzel, V., Sevgi Turgut, S., & Ay?e Özel, S. (2018). Prediction of Students’ Academic Success Using Data Mining Methods. Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018. https://doi.org/10.1109/ASYU.2018.8554006

Nugroho, F. A., Solikin, A. F., Anggraini, M. D., & Kusrini, K. (2021). Sistem Pakar Diagnosa Virus Corona Dengan Metode Naïve Bayes. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 9(1). https://doi.org/10.30646/tikomsin.v9i1.553

Rahman, A., & Budiyanto, U. (2019). Case based reasoning adaptive e-learning system based on visual-auditory-kinesthetic learning styles. In Irawan, H. Irawan, M. A. Riyadi, & M. Facta (Eds.), International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 177–182). IEEE. https://doi.org/10.23919/EECSI48112.2019.8976921

Rahman, A., Mutiarawan, R. A., Darmawan, A., Rianto, Y., & Syafrullah, M. (2019). Prediction of students academic success using case based reasoning. In Irawan, H. Irawan, M. A. Riyadi, & M. Facta (Eds.), International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 171–176). IEEE. https://doi.org/10.23919/EECSI48112.2019.8977104

Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 2. https://doi.org/10.14569/ijacsa.2020.0110277

Tanoli, Z., Seemab, U., Scherer, A., Wennerberg, K., Tang, J., & Vähä-Koskela, M. (2021). Exploration of databases and methods supporting drug repurposing: A comprehensive survey. In Briefings in Bioinformatics (Vol. 22, Issue 2). https://doi.org/10.1093/bib/bbaa003

Zhou, H. F., Zhang, J. W., Zhou, Y. Q., Guo, X. J., & Ma, Y. M. (2021). A feature selection algorithm of decision tree based on feature weight. Expert Systems with Applications, 164. https://doi.org/10.1016/j.eswa.2020.113842


PlumX Metrics



How to Cite

Rahman, Abdul. 2023. “Klasifikasi Performa Akademik Siswa Menggunakan Metode Decision Tree Dan Naive Bayes”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 13 (1):22-31. https://doi.org/10.33020/saintekom.v13i1.349.