THE EFFECT OF AMOUNT OF DATA ON RESULTS OF ACCURACY VALUE OF C4.5 ALGORITHM ON STUDENT ACHIEVEMENT INDEX DATA

Authors

  • Anton Sunardi STMIK LIKMI
  • Sienny Rusli STMIK LIKMI
  • Christina Juliane STMIK LIKMI
(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v4i2.157

Keywords:

the amount of data, C4.5, achievement index, data mining

Abstract

Of the many academic data, data in the form of an achievement index needs to be used in-depth so that it does not become a display of numbers and information only. This achievement index evaluation data reflects the educational process students and teaching staff carries out in an educational process. This study aims to measure the accuracy of data mining processing based on differences in test data by analyzing the C4.5 algorithm using RapidMiner as a data processing tool and determining the decisions students can make and academic institutions in developing study strategies and educational curricula to be maximized. The data processing is carried out by classifying the student achievement index data at a private university using data analysis test equipment. The data source comes from kaggle.com, which consists of 1687 data that have been processed and processed. The conclusion from the results of this study is that the amount of data turns out to have a significant influence on the accuracy value of the C4.5 algorithm, where an accuracy rate of 91.69% is obtained from the test results of 1687 data with four main attributes, namely IPK1, IPK2, IPK3, IPK4 and correctly or not as a label.

Downloads

Download data is not yet available.

References

Amir, S., & Abijono, H. (2018). Penerapan Data Mining untuk Mendukung Pemasaran Produk. CAHAYAtech, 7(2), 161–182. Retrieved from https://ojs.cahayasurya.ac.id/index.php/CT/article/view/102

Azhari, M., Situmorang, Z., & Rosnelly, R. (2021). Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4. 5, Random Forest, SVM dan Naive Bayes. JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(2), 640–651. https://doi.org/10.30865/mib.v5i2.2937

Budiman, I., & Ramadina, R. (2015). Penerapan Fungsi Data Mining Klasifikasi untuk Prediksi Masa Studi Mahasiswa Tepat Waktu pada Sistem Informasi Akademik Perguruan Tinggi. JUPITER (Jurnal Penelitian Ilmu Dan Teknologi Komputer), 7(1), 39–50. Retrieved from https://jurnal.polsri.ac.id/index.php/jupiter/article/view/709

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T. P., Shearer, C., & Wirth, R. (2000). CRISP-DM 1.0: Step-by-step data mining guide. In SPSS inc. CRISP-DM consortium. Retrieved from https://www.kde.cs.uni-kassel.de/wp-content/uploads/lehre/ws2012-13/kdd/files/CRISPWP-0800.pdf

Dengen, C. N., Kusrini, K., & Luthfi, E. T. (2020). Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu. SISFOTENIKA, 10(1), 1–11. Retrieved from https://www.stmikpontianak.ac.id/ojs/index.php/ST/article/view/484

Handini, D., Hidayat, F., Attamimi, A. N. R., Putri, D. A. V., Rouf, M. F., & Anjani, N. R. (2020). Statistik Pendidikan Tinggi Tahun 2020. Jakarta: Sekretaris Direktorat Jenderal Pendidikan Tinggi. Retrieved from Sekretaris Direktorat Jenderal Pendidikan Tinggi website: https://pddikti.kemdikbud.go.id/asset/data/publikasi/Statistik Pendidikan Tinggi 2020.pdf

Hermawanti, S. N., Asriyanik, A., & Sunarto, A. A. (2019). Implementasi Algoritma C4.5 untuk Prediksi Kelulusan Tepat Waktu ( Studi Kasus : Program Studi Teknik Informatika ). Jurnal Ilmiah SANTIKA, 9(1), 853–864. https://doi.org/10.37150/jsa.v9i1.552

Maryanto, B. (2017). Big Data dan Pemanfaatannya dalam Berbagai Sektor. Media Informatika, 16(2), 14-19. Retrieved from https://jurnal.likmi.ac.id/Jurnal/7_2017/0717_02_BudiMaryanto.pdf

Megna, A. A. K. (2021). Big Data: Development of Revolutionary technologies in Business. Istanbul.

Muis, I. A., & Affandes, M. (2015). Penerapan Metode Support Vector Machine (SVM) Menggunakan Kernel Radial Basis Function (RBF) Pada Klasifikasi Tweet. Jurnal Sains, Teknologi Dan Industri, 12(2), 189–197. Retrieved from http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/1010

Musu, W., Ibrahim, A., & Heriadi, H. (2021). Pengaruh Komposisi Data Training dan Testing terhadap Akurasi Algoritma C4.5. Seminar Sistem Informasi Dan Teknologi Informasi (SISITI), 186–195. Makasar: STMIK Dipanegara Makassar. Retrieved from https://www.ejurnal.dipanegara.ac.id/index.php/sisiti/article/view/802

Olson, D., & Shi, Y. (2007). Pengantar Ilmu Penggalian Data Bisnis - Introduction to Business Data Mining. Jakarta: Salemba Empat.

Romadhona, A., Suprapedi, S., & Himawan, H. (2017). Prediksi Kelulusan Mahasiswa Tepat Waktu Berdasarkan Usia, Jenis Kelamin, Dan Indeks Prestasi Menggunakan Algoritma Decision Tree. Jurnal Teknologi Informasi CyberKU, 13(1), 69–83. Retrieved from http://research.pps.dinus.ac.id/index.php/Cyberku/article/view/10

Sabna, E., & Muhardi, M. (2016). Penerapan Data Mining Untuk Memprediksi Prestasi Akademik Mahasiswa Berdasarkan Dosen, Motivasi, Kedisiplinan, Ekonomi, dan Hasil Belajar. Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer Dan Teknologi Informasi, 2(2), 41. https://doi.org/10.24014/coreit.v2i2.2392

Saefulloh, A., & Moedjiono, M. (2013). Penerapan Metode Klasifikasi Data Mining Untuk Prediksi Kelulusan Tepat Waktu. InfoSys Journal, 2(1), 41–54.

Windarti, M., & Suradi, A. (2019). Perbandingan Kinerja 6 Algoritme Klasifikasi Data Mining untuk Prediksi Masa Studi Mahasiswa. Telematika, 12(1), 14–30. Retrieved from https://ejournal.amikompurwokerto.ac.id/index.php/telematika/article/view/778

Downloads

Published

2022-03-24

How to Cite

Sunardi , A., Rusli , S., & Juliane , C. (2022). THE EFFECT OF AMOUNT OF DATA ON RESULTS OF ACCURACY VALUE OF C4.5 ALGORITHM ON STUDENT ACHIEVEMENT INDEX DATA. Jurnal Riset Informatika, 4(2), 191–198. https://doi.org/10.34288/jri.v4i2.157

Issue

Section

Articles