Data Mining di Bidang Pendidikan untuk Analisa Prediksi Kinerja Mahasiswa dengan Komparasi 2 Model Klasifikasi pada STMIK Jabar

Authors

  • Galih Galih STMIK JABAR

Keywords:

Educational Data Mining (EDM), Naive Bayes Classifier (NBC), Decision Tree C4.5, Classification

Abstract

Until now many universities that helped the government in accelerating improving the quality of education so the creation of a competitive environment.. The abundance of data contained in the College can be put to good use in accordance with the needs and processed into useful information so as to find out the relationship between the attributes of the data in it can be on the analysis and expected output in the form of student performance has related to the period of study i.e. can be categorized into appropriate or too late in the anticipated period of study. Data mining can be used for educational institutions or institutions and often called as Educational Data Mining (EDM). In the study carried out using two models of Naive Bayes Classifier i.e. algorithms and C 4.5. as for the value of the best accuracy in the Naive Bayes Classifier algorithm model (NBC) was 86.83% with ratio 80% training data, whereas in model algorithm C 4.5 was 88.10% with 90% training data ratio. Application of EDM and expected to be maximized and developed so that it can contribute to and progress in the world of education especially in data mining.

Author Biography

Galih Galih, STMIK JABAR

Program Studi Sistem Informasi

References

Amra, I. A. A., & Maghari, A. Y. A. (2017). Students performance prediction using KNN and Naïve Bayesian. ICIT 2017 - 8th International Conference on Information Technology, Proceedings, 909–913. https://doi.org/10.1109/ICITECH.2017.8079967

Badr, A., Din, E., & Elaraby, I. S. (2014). Data Mining?: A prediction for Student ’ s Performance Using Classification Method. 2(2), 43–47. https://doi.org/10.13189/wjcat.2014.020203

BAN-PT. (2011). 6 Buku 6 Matriks Penilaian Borang Dan Evaluasi Diri Aipt 2011 (pp. 21–23). pp. 21–23. Retrieved from https://banpt.or.id/instrumen/APT.rar

Bustami. (2014). Penerapan Algoritma Naive Bayes. Jurnal Informatika, 8(1), 884–898.

Devasia, T., Vinushree, T. P., & Hegde, V. (2016). Prediction of students performance using Educational Data Mining. Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016, 1(3), 91–95. https://doi.org/10.1109/SAPIENCE.2016.7684167

Gorunescu, F. (2011). Data Mining Concepts, Models and Techniques. Verlag Berlin Heidelberg.

Larose, D. T. (2006). DATA MINING METHODS AND MODELS. In Contemporary Psychology: A Journal of Reviews (Vol. 21). https://doi.org/10.1037/014836

Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data. In Discovering Knowledge in Data. https://doi.org/10.1002/9781118874059

Makhtar, M., Nawang, H., & Nor, S. W. S. (2017). ANALYSIS ON STUDENTS PERFORMANCE USING NAI¨VE BAYES CLASSIFIER. Journal of Theoretical and Applied Information Technology, 95(16), 1–8. Retrieved from www.jatit.org

Rapidminer Inc. (2019). Split Validation (RapidMiner Studio Core). Retrieved May 8, 2019, from https://docs.rapidminer.com/latest/studio/operators/validation/split_validation.html

Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532

Shakeel, K., & Anwer Butt, N. (2015). Educational Data Mining to Reduce Student Dropout Rate by Using Classification. 253rd OMICS International Conference on Big Data Analysis & Data Mining, (May). Retrieved from https://www.researchgate.net/publication/281149091_Educational_Data_Mining_to_Reduce_Student_Dropout_Rate_by_Using_Classification?enrichId=rgreq-95ac86e292913b9c0a37d546c75096e5-XXX&enrichSource=Y292ZXJQYWdlOzI4MTE0OTA5MTtBUzoyNjUyNzk2MzI1NzI0MTZAMTQ0MDI1

Tair, M. M. A., & El-halees, A. M. (2012). Mining Educational Data to Improve Students ’Performance?: A Case Study. Journal of Theoretical and Applied Information Technology, 2(2), 140–146.

Tempola, F., Muhammad, M., & Khairan, A. (2018). Perbandingan Klasifikasi Antara Knn Dan Naive Bayes Pada Penentuan Status Gunung Berapi Dengan K-Fold Cross Validation Comparison of Classification Between Knn and Naive Bayes At the Determination of the Volcanic Status With K-Fold Cross. 5(5), 577–584. https://doi.org/10.25126/jtiik20185983

Yu, H., Huang, X., Hu, X., & Cai, H. (2010). A Comparative Study on Data Mining Algorithms for Individual Credit Risk Evaluation. https://doi.org/10.1109/ICMeCG.2010.16

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 2 No. 1 Januari 2019

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Published

2019-01-30

How to Cite

Galih, G. (2019). Data Mining di Bidang Pendidikan untuk Analisa Prediksi Kinerja Mahasiswa dengan Komparasi 2 Model Klasifikasi pada STMIK Jabar. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 2(1), 23–30. Retrieved from https://openjournal.unpam.ac.id/index.php/JTSI/article/view/2643

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Vol. 2 No. 1 (2019): Jurnal Teknologi Sistem Informasi dan Aplikasi

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Copyright (c) 2019 Galih Galih

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