IMPLEMENTATION OF K-NEAREST NEIGHBOR AND GINI INDEX METHOD IN CLASSIFICATION OF STUDENT PERFORMANCE
Penerapan Metode K-Nearest Neighbor Dan Gini Index Pada Klasifikasi Kinerja Siswa
Predicting student academic performance is one of the important applications in data mining in education. However, existing work is not enough to identify which factors will affect student performance. Information on academic values or progress on student learning is not enough to be a factor in predicting student performance and helps students and educators to make improvements in learning and teaching. K-Nearest Neighbor is a simple method for classifying student performance, but K-Nearest Neighbor has problems in terms of high feature dimensions. To solve this problem, we need a method of selecting the Gini Index feature in reducing the high feature dimensions. Several experiments were conducted to obtain an optimal architecture and produce accurate classifications. The results of 10 experiments with values of k (1 to 10) in the student performance dataset with the K-Nearest Neighbor method showed the highest average accuracy of 74.068 while the K-Nearest Neighbor and Gini Index methods showed the highest average accuracy of 76.516. From the results of these tests it can be concluded that the Gini Index is able to overcome the problem of high feature dimensions in K-Nearest Neighbor, so the application of the K-Nearest Neighbor and Gini Index can improve the accuracy of student performance classification better than using the K-Nearest Neighbor method.
Adeniyi, D. A., Wei, Z., & Yongquan, Y. (2016). Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics, 12(1), 90–108. https://doi.org/10.1016/j.aci.2014.10.001
Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., … Olatunji, S. O. (2017). Student performance prediction using Support Vector Machine and K-Nearest Neighbor. Canadian Conference on Electrical and Computer Engineering, 17–20. https://doi.org/10.1109/CCECE.2017.7946847
Alkhasawneh, R., & Hobson, R. (2011). Modeling student retention in science and engineering disciplines using neural networks. In 2011 IEEE Global Engineering Education Conference, EDUCON 2011 (pp. 660–663). https://doi.org/10.1109/EDUCON.2011.5773209
Altujjar, Y., Altamimi, W., Al-Turaiki, I., & Al-Razgan, M. (2016). Predicting Critical Courses Affecting Students Performance: A Case Study. Procedia Computer Science, 82(March), 65–71. https://doi.org/10.1016/j.procs.2016.04.010
Breiman, L. (2001). Classification and regression tree.
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting student performance from LMS data: A comparison of 17 blended courses using moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29. https://doi.org/10.1109/TLT.2016.2616312
Cortez, P., & Silva, A. (2008). Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008), 5–12.
Cover, T. M., & Hart, P. E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964
de Vries, A. P., Mamoulis, N., Nes, N., & Kersten, M. (2003). Efficient k-NN search on vertically decomposed data (p. 322). https://doi.org/10.1145/564728.564729
Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. Van. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94(February), 335–343. https://doi.org/10.1016/j.jbusres.2018.02.012
Gou, J., Zhan, Y., Rao, Y., Shen, X., Wang, X., & He, W. (2014). Improved pseudo nearest neighbor classification. Knowledge-Based Systems, 70, 361–375. https://doi.org/10.1016/j.knosys.2014.07.020
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques. Data Mining. https://doi.org/10.1016/b978-0-12-381479-1.00001-0
Koncz, P., & Paralic, J. (2011). An approach to feature selection for sentiment analysis. In INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings (pp. 357–362). https://doi.org/10.1109/INES.2011.5954773
Lin, Y., Li, J., Lin, M., & Chen, J. (2014). A new nearest neighbor classifier via fusing neighborhood information. Neurocomputing, 143, 164–169. https://doi.org/10.1016/j.neucom.2014.06.009
Lopez Guarin, C. E., Guzman, E. L., & Gonzalez, F. A. (2015). A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining. Revista Iberoamericana de Tecnologias Del Aprendizaje, 10(3), 119–125. https://doi.org/10.1109/RITA.2015.2452632
López, J., & Maldonado, S. (2018). Redefining nearest neighbor classification in high-dimensional settings. Pattern Recognition Letters, 110, 36–43. https://doi.org/10.1016/j.patrec.2018.03.023
Pandey, M., & Taruna, S. (2016). Towards the integration of multiple classifier pertaining to the Student’s performance prediction. Perspectives in Science, 8, 364–366. https://doi.org/10.1016/j.pisc.2016.04.076
Setiyorini, T., & Asmono, R. T. (2017). Penerapan Gini Index dan K-Nearest Neighbor untuk Klasifikasi Tingkat Kognitif Soal pada Taksonomi Bloom. Jurnal Pilar Nusa Mandiri, 13(2), 209–216.
Setiyorini, T., & Asmono, R. T. (2019). Laporan Akhir Penelitian Mandiri.
Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Shang, W., Huang, H., Zhu, H., Lin, Y., Qu, Y., & Wang, Z. (2007). A novel feature selection algorithm for text categorization. Expert Systems with Applications, 33(1), 1–5. https://doi.org/10.1016/j.eswa.2006.04.001
Shankar, S., & Karypis, G. (2000). A Feature Weight Adjustment Algorithm for Document Categorization.
Villagrá-Arnedo, C. J., Gallego-Durán, F. J., Llorens-Largo, F., Compañ-Rosique, P., Satorre-Cuerda, R., & Molina-Carmona, R. (2017). Improving the expressiveness of black-box models for predicting student performance. Computers in Human Behavior, 72, 621–631. https://doi.org/10.1016/j.chb.2016.09.001
Wang, S., Li, D., Song, X., Wei, Y., & Li, H. (2011). A feature selection method based on improved fisher’s discriminant ratio for text sentiment classification. Expert Systems with Applications, 38(7), 8696–8702. https://doi.org/10.1016/j.eswa.2011.01.077
Won Yoon, J., & Friel, N. (2015). Efficient model selection for probabilistic K nearest neighbour classification. Neurocomputing, 149(PB), 1098–1108. https://doi.org/10.1016/j.neucom.2014.07.023
Xu, T., Peng, Q., & Cheng, Y. (2012). Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowledge-Based Systems, 35, 279–289. https://doi.org/10.1016/j.knosys.2012.04.011
Yang, F., & Li, F. W. B. (2018). Study on student performance estimation, student progress analysis, and student potential prediction based on data mining. Computers and Education, 123(October 2017), 97–108. https://doi.org/10.1016/j.compedu.2018.04.006
Abstract viewed = 199 times
PDF downloaded = 173 times
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.
- The copyright in each article belongs to the author.
- Authors retain all their rights to published works, not limited to the rights set out on this page.
- The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).
- Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);
- The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.
- If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.