Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5 Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5
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Published:
May 31, 2020
Keywords:
Educational Data Mining (EDM), Decision Tree C4.5, K-Nearest Neighbor
Section
Computer Science
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Abstract
Data Mining is very useful and widely applied in various fields, one of which is in the field of education. Data mining can be applied to the field of Educational institutions and is also often referred to as Educational Data Mining (EDM). Colleges currently compete with each other to improve the quality of their education for the creation of competent and high-integrity human resources. The amount of data contained in Higher Education can be utilized for the need for useful information so that the data attributes can be known so that the data is analyzed to improve student performance and achievement. And also the results of the analysis are expected to be able to anticipate the problem of delays in the study period that is often experienced by students. In this study conducted using two algorithm models namely K-Nearest Neighbor and Decision Tree C4.5. The best accuracy value is the K-Nearest Neighbor algorithm model with an accuracy rate of 59.32%, whereas in the Decision Tree C4.5 model the accuracy rate is 54.80%, the application of EDM and is expected to be maximized and developed so that it can contribute and develop in education world especially in data mining
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How to Cite
Yulianto, L. D., Triayudi, A. and Sholihati, I. D. (2020) “Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5: Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5”, Jurnal Mantik, 4(1), pp. 441-451. Available at: https://iocscience.org/ejournal/index.php/mantik/article/view/770 (Accessed: 19April2026).
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[3] Khokhoni Innocentia Mpho Ramaphosa, Tranos Zuva, Raoul Kwuimi, " Educational Data Mining to Improve Learner Performance in Gauteng Primary Schools", international Conference on Information and Communications Technology, Agustus, 2018.
[4] Ms.Tismy Devasia, Ms.Vinushree T P, Mr.Vinayak Hegde, " Prediction of Students Performance using Educational Data Mining", International Conference on Data Mining and Advanced Computing, Maret 2016.
[5] Mokhairi Makhtar, Hasnah Nawang, Syadiah Nor Wan Shamsuddin, " Analysis On Students Performance Using Naïve Bayes Classifier", Journal of Theoretical and Applied Information Technology, Vol.95. No.16, Agustus 2017.
[6] Yoga Pristyanto, Irfan Pratama, Anggit Ferdita Nugraha, " Data Level Approach for Imbalanced Class Handling on Educational Data Mining Multiclass Classification", International Conference on Information and Communications Technology, Maret 2018.
[7] Maulana Aditya Rahman, Nurul Hidayat, Ahmad Afif Supianto, " Komparasi Metode Data Mining K-Nearest Neighbor Dengan Naïve Bayes Untuk Klasifikasi Kualitas Air Bersih (Studi Kasus PDAM Tirta Kencana Kabupaten Jombang)", Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, Vol. 2, No. 12, Desember 2018.
[8] Galih, " Data Mining di Bidang Pendidikan untuk Analisa Prediksi Kinerja Mahasiswas dengan Komparasi 2 Model Klasifikasi pada STMIK Jabar", Jurnal Teknologi Sistem Informasi dan Aplikasi, Vol. 2, No. 1, Januari 2019
[9] Sari Dewi, "Komparasi 5 metode Algoritma Klasifikasi Data Mining pada Prediksi Keberhasilan Pemasaran Produk Layanan Perbankan", Jurnal Techno Nusa Mandiri, Vol. XIII, No. 1 Maret 2016.
[10] Chitra Jalota, Rashmi Agrawal, " Analysis of Educational Data Mining using Classification", International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, Februari 2019.
[11] Langgeng Listiyoko, Rosalia Wati, Achmad Fahrudin, " Klasifikasi Siswa Untuk Meningkatkan Nilai Rata-Ratakelas Menggunakan Metode Data Mining", SeminarNasionalTeknologi dan Rekayasa, 2017.
[12] Senna Hendrian, "Algoritma Klasifikasi Data Mining Untuk Memprediksi Siswa Dalam Memperoleh Bantuan Dana Pendidikan" Universitas Indraprasta PGRI, 2018.
[13] Daniel David, Sani M. Isa, " Penerapan Educational Data Mining Untuk Memprediksi Hasil Belajar Siswa SMAK Ora et Labora", Department of Computer ScienceBina Nusantara (BINUS) University, VOL. XII No, September 2019.
[14] Diky Firdaus, " Penggunaan Data Mining dalam Kegiatan Sistem Pembelajaran Berbantuan Komputer", Universitas Mercu Buana, Volume 6, No 2, Tahun 2017.
[15] Sheena Angra, Sachin Ahuja, "Implementation of Data Mining Algorithms on Student's Data using Rapid Miner," International Conference on Cloud Computing, Data Science & Engineering,Maret, 2017.
[16] Anoopkumar M, Dr. A. M. J. Md. Zubair Rahman, " A Review on Data Mining Techniques and Factors Used in Educational Data Mining to Predict Student Amelioration", International Conference on Cloud Computing, Data Science & Engineering, Maret, 2016.
[17] Edona Doko, Lejla Abazi Bexheti, "A Systematic Mapping Study of Educational Technologies based on Educational Data Mining and Learning Analytics, Mediterranean Conference On Embedded Computing, Juni, 2018.
[18] Nurul Hidayat, Retantyo Wardoyo, Azhari SN, " Educational Data Mining (EDM) as a Model for Students’ Evaluation in Learning Environment", Third International Conference on Informatics and Computing (ICIC), Oktober,2018.
[19] Triayudi, Agung, and Iskandar Fitri. "A new agglomerative hierarchical clustering to model student activity in online learning." Telkomnika 17.3 (2019): 1226-1235.
[20] Prameswari, Eka Ayunda, Agung Triayudi, and Ira Diana Sholihati. "Web-based E-diagnostic for Digestive System Disorders in Humans using the Demster Shafer Method." International Journal of Computer Applications 975: 8887
[21] Triayudi, Agung, and Iskandar Fitri. "Comparison of parameter-free agglomerative hierarchical clustering methods." ICIC Express Letters 12.10 (2018): 973-980.