Identification of Student Academic Performance using the KNN Algorithm

Authors

  • Aldi Nugroho Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
  • Osvaldo Richie Riady Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
  • Alexander Calvin Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480
  • Derwin Suhartono Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia 11480

Keywords:

KNN, Classification algorithm, Academic quality

Abstract

Students are an important asset in the world of education also an institution and therefore also need to pay attention to students' graduation rates on time. The ups and downs of the percentage of students' abilities in classroom learning is one important element for assessing university accreditation. Therefore, it is necessary to monitor and evaluate teaching and learning activities using the KNN Algorithm classification. By processing student complaints data and seeing the results of previous learning can obtain important things for higher education needs. In predicting graduation rates based on complaints, this study uses the K-Nearest Neighbor classification algorithm by grouping data k = 1, k = 2, k = 3 with the smallest value possible. In experiments using the KNN method the results were clearly visible and showed quite good accuracy. From the experiment it was concluded that if there were fewer complaints from one student it could minimize the level of student non-graduates at the university and ultimately produce good accreditation.

References

Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., ... & Olatunji, S. O. (2017, April). Student performance prediction using support vector machine and k-nearest neighbor. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) (pp. 1-4). IEEE.

Strecht, P., Cruz, L., Soares, C., & Mendes-Moreira, J. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance. International Educational Data Mining Society.

Nagesh, A. S., Satyamurty, C. V., & Akhila, K. (2017). Predicting Student Performance using KNN Classification in Bigdata Environment. CVR Journal of Science and Technology, 13, 83-87.

Hastarimasuci, R., Papilo, P., & Nazir, A. (2019, November). Variable Selection to Determine Majors of Student using K-Nearest Neighbor and Naïve Bayes Classifier Algorithm. In Journal of Physics: Conference Series (Vol. 1363, No. 1, p. 012057). IOP Publishing.

Alfere, S. S., & Maghari, A. Y. (2018). Prediction of Student's Performance Using Modified KNN Classifiers. Prediction of Student's Performance Using Modified KNN Classifiers.

Kumari, P., Jain, P. K., & Pamula, R. (2018, March). An efficient use of ensemble methods to predict students academic performance. In 2018 4th International Conference on Recent Advances in Information Technology (RAIT) (pp. 1-6). IEEE.

Amra, I. A. A., & Maghari, A. Y. (2017, May). Students performance prediction using KNN and Naïve Bayesian. In 2017 8th International Conference on Information Technology (ICIT) (pp. 909-913). IEEE.

Samuel, M. G. COMPARATIVE ANALYSIS OF KNN AND SVM CLASSIFIERS FOR STUDENTS’ACADEMIC PERFORMANCE PREDICTION. for National Safety & Security, 149.

Putpuek, N., Rojanaprasert, N., Atchariyachanvanich, K., & Thamrongthanyawong, T. (2018, June). Comparative study of prediction models for final GPA score: a case study of Rajabhat Rajanagarindra University. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 9297). IEEE.

Ab Razak, W. M. W., Baharom, S. A. S., Abdullah, Z., Hamdan, H., Abd Aziz, N. U., & Anuar, A. I. M. (2019). Academic Performance of University Students: A Case in a Higher Learning Institution. KnE Social Sciences, 1294-1304.

Maganga, J. H. (2016). Factors Affecting Students’ Academic Performance: A Case of Public Secondary Schools in Ilala District, Dar es Salaam (Doctoral dissertation, The Open University of Tanzania).

Kassarnig, V., Mones, E., Bjerre-Nielsen, A., Sapiezynski, P., Lassen, D. D., & Lehmann, S. (2018). Academic performance and behavioral patterns. EPJ Data Science, 7(1), 10.

Mushtaq, I., & Khan, S. N. (2012). Factors Affecting Students’ Academic Performance. Global journal of management and business research, 12(9).

Gbollie, C., & Keamu, H. P. (2017). Student academic performance: The role of motivation, strategies, and perceived factors hindering Liberian junior and senior high school students learning. Education Research International, 2017.

Dev, M. (2016). Factors Affecting the Academic Achievement: A Study of Elementary School Students of NCR Delhi, India. Journal of Education and Practice, 7(4), 70-74.

Sumbawati, M. S., & Anistyasari, Y. (2018, January). The impact of research-based learning on student’s academic performance and motivation. In IOP Conference Series: Materials Science and Engineering (Vol. 296, No. 1, p. 012043). IOP Publishing.

Singh, S. P., Malik, S., & Singh, P. (2016). Research paper factors affecting academic performance of students. Indian Journal of Research, 5(4), 176-178.

Cavilla, D. (2017). The effects of student reflection on academic performance and motivation. SAGE Open, 7(3), 2158244017733790.

Mesarić, J., & Šebalj, D. (2016). Decision trees for predicting the academic success of students. Croatian Operational Research Review, 7(2), 367-388.

Crisp, G., Taggart, A., & Nora, A. (2015). Undergraduate Latina/o students: A systematic review of research identifying factors contributing to academic success outcomes. Review of Educational Research, 85(2), 249-274.

Khan, A., Khan, S., Zia-Ul-Islam, S., & Khan, M. (2017). Communication Skills of a Teacher and Its Role in the Development of the Students' Academic Success. Journal of Education and Practice, 8(1), 18-21.

Costa, A., & Faria, L. (2018). Implicit theories of intelligence and academic achievement: A meta-analytic review. Frontiers in psychology, 9, 829.

Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students' academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 8(11), 36.

Seibert, G. S., Bauer, K. N., May, R. W., & Fincham, F. D. (2017). Emotion regulation and academic underperformance: the role of school burnout. Learning and Individual Differences, 60, 1-9.

Thomas, C. L., Cassady, J. C., & Heller, M. L. (2017). The influence of emotional intelligence, cognitive test anxiety, and coping strategies on undergraduate academic performance. Learning and Individual Differences, 55, 40-48.

Akessa, G. M., & Dhufera, A. G. (2015). Factors That Influences Students Academic Performance: A Case of Rift Valley University, Jimma, Ethiopia. Journal of Education and Practice, 6(22), 55-63.

Moè, A. (2015). Perceived control mediates the relations between depressive symptoms and academic achievement in adolescence. The Spanish journal of psychology, 18.

Rohman, A. (2015). Model Algoritma K-Nearest Neighbor (K-Nn) Untuk Prediksi Kelulusan Mahasiswa. Neo Teknika, 1(1).

Banjarsari, M. A., Budiman, I., & Farmadi, A. (2016). Penerapan K-Optimal Pada Algoritma Knn Untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer Fmipa Unlam Berdasarkan Ip Sampai Dengan Semester 4. Klik-Kumpulan Jurnal Ilmu Komputer, 2(2), 159-173.

Mustakim, M., & Oktaviani, G. (2016). Algoritma K-Nearest Neighbor Classification Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa. Jurnal Sains dan Teknologi Industri, 13(2), 195-202.

Fadillah, A. P. (2015). Penerapan Metode CRISP-DM untuk Prediksi Kelulusan Studi Mahasiswa Menempuh Mata Kuliah (Studi Kasus Universitas XYZ). Jurnal Teknik Informatika dan Sistem Informasi, 1(3).

Izzah, A., & Widyastuti, R. (2016). Prediksi Kelulusan Mata Kuliah Menggunakan Hybrid Fuzzy Inference System. Register: Jurnal Ilmiah Teknologi Sistem Informasi, 2(2), 60-67.

Kusumadewi, S. (2004). Fuzzy quantification theory I untuk analisis hubungan antara penilaian kinerja dosen oleh mahasiswa, kehadiran dosen, dan nilai kelulusan mahasiswa. Media Informatika, 2(1).

Christman, J. L. (2017). Performance-based cluster grouping in ninth grade honors physics.

Slavin, R. E. (1987). Ability grouping and student achievement in elementary schools: A best-evidence synthesis. Review of educational research, 57(3), 293-336.

Rees, D. I., Brewer, D. J., & Argys, L. M. (2000). How should we measure the effect of ability grouping on student performance?. Economics of Education Review, 19(1), 17-20.

Bunkar, K., Singh, U. K., Pandya, B., & Bunkar, R. (2012, September). Data mining: Prediction for performance improvement of graduate students using classification. In 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN) (pp. 1-5). IEEE.

Bekele, R., & Menzel, W. (2005). A bayesian approach to predict performance of a student (bapps): A case with ethiopian students. algorithms, 22(23), 24.

Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422.

Affendey, L. S., Paris, I. H. M., Mustapha, N., Sulaiman, M. N., & Muda, Z. (2010). Ranking of influencing factors in predicting students’ academic performance. Information Technology Journal, 9(4), 832-837.

Carter, A. S., Hundhausen, C. D., & Adesope, O. (2015, August). The normalized programming state model: Predicting student performance in computing courses based on programming behavior. In Proceedings of the eleventh annual International Conference on International Computing Education Research (pp. 141-150).

Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 982.

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Published

2020-09-30

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