Analisa Klasifikasi C4.5 Terhadap Faktor Penyebab Menurunnya Prestasi Belajar Mahasiswa Pada Masa Pandemi
DOI:
https://doi.org/10.30865/mib.v5i1.2763Keywords:
Classification, Datamining, C4.5, Learning Achievement, PandemicAbstract
The purpose of the study was to classify the factors causing the decline in student achievement during the pandemic using the C4.5 datamining method. Sources of research data were obtained by conducting interviews and distributing questionnaires to 7th semester students of the 2020-2021 school year information system study program. Attributes that used in the classification of the factors causing the decline in student achievement include: Learning Method (C1), Study Time (C2), Material Understanding (C3), Giving Assignments (C4) and Environment (C5). The results of the calculation show that the Material Understanding (C3) attribute is the attribute that most influences the decline in student learning achievement. Testing was also carried out using the help of Rapidminer software and obtained an accuracy of 97.5%.
References
F. Suasty and A. A. Hadi, “Penggunan Media Pembelajaran Video untuk Solusi Penurunan Pemahaman Materi Pembelajaran Ketika Belajar Online Akibat Pandemic Covid-19,†vol. 1, no. 1, pp. 12–16, 2020.
B. Nadeak, C. P. Juwita, and E. Sormin, “Hubungan kemampuan berpikir kritis mahasiswa dengan penggunaan media sosial terhadap capaian pembelajaran pada masa pandemi Covid-19,†vol. 8, no. 2, pp. 98–104, 2020.
A. Cahyani et al., “Motivasi Belajar Siswa SMA pada Pembelajaran Daring di Masa Pandemi Covid-19,†vol. 3, no. 01, pp. 123–140, 2020.
M. Belajar, S. Pada, M. Pandemi, C. Di, and K. V. S. D. N. Ngembel, “The Effectiveness Of Bion ( Bintang Online ) In Improving The Learning Motivation Of 5 Th Grade Students In State Elementary School 1 Of Ngembel,†vol. 6, pp. 184–198, 2020.
A. P. Windarto, U. Indriani, M. R. Raharjo, and L. S. Dewi, “Bagian 1: Kombinasi Metode Klastering dan Klasifikasi (Kasus Pandemi Covid-19 di Indonesia),†J. Media Inform. Budidarma, vol. 4, no. 3, p. 855, 2020, doi: 10.30865/mib.v4i3.2312.
B. Supriyadi, A. P. Windarto, T. Soemartono, and Mungad, “Classification of natural disaster prone areas in Indonesia using K-means,†Int. J. Grid Distrib. Comput., vol. 11, no. 8, pp. 87–98, 2018, doi: 10.14257/ijgdc.2018.11.8.08.
A. Waluyo, H. Jatnika, M. R. S. Permatasari, T. Tuslaela, I. Purnamasari, and A. P. Windarto, “Data Mining Optimization uses C4.5 Classification and Particle Swarm Optimization (PSO) in the location selection of Student Boardinghouses,†IOP Conf. Ser. Mater. Sci. Eng., vol. 874, no. 1, pp. 1–9, 2020, doi: 10.1088/1757-899X/874/1/012024.
M. Widyastuti, A. G. Fepdiani Simanjuntak, D. Hartama, A. P. Windarto, and A. Wanto, “Classification Model C.45 on Determining the Quality of Custumer Service in Bank BTN Pematangsiantar Branch,†J. Phys. Conf. Ser., vol. 1255, no. 1, pp. 1–6, 2019, doi: 10.1088/1742-6596/1255/1/012002.
S. Sundari, Karmila, M. N. Fadli, D. Hartama, A. P. Windarto, and A. Wanto, “Decision Support System on Selection of Lecturer Research Grant Proposals using Preferences Selection Index,†J. Phys. Conf. Ser., vol. 1255, no. 1, pp. 1–8, 2019, doi: 10.1088/1742-6596/1255/1/012006.
W. Katrina, H. J. Damanik, F. Parhusip, D. Hartama, A. P. Windarto, and A. Wanto, “C.45 Classification Rules Model for Determining Students Level of Understanding of the Subject,†J. Phys. Conf. Ser., vol. 1255, no. 1, 2019, doi: 10.1088/1742-6596/1255/1/012005.
A. P. Windarto, J. Na, and A. Wanto, “Bagian 2 : Model Arsitektur Neural Network dengan Kombinasi K- Medoids dan Backpropagation pada kasus Pandemi COVID-19 di Indonesia,†vol. 4, pp. 1175–1180, 2020, doi: 10.30865/mib.v4i4.2505.
K. F. Irnanda and A. P. Windarto, “Penerapan Klasifikasi C4 . 5 Dalam Meningkatkan Kecakapan Berbahasa Inggris dalam Masyarakat,†pp. 304–308, 2020.
F. Rahman, I. I. Ridho, M. Muflih, S. Pratama, M. R. Raharjo, and A. P. Windarto, “Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country,†IOP Conf. Ser. Mater. Sci. Eng., vol. 835, no. 1, 2020, doi: 10.1088/1757-899X/835/1/012058.
M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,†vol. 7, no. 1, pp. 59–64, 2013.
S. Sivakumari, R. P. Priyadarsini, and P. Amudha, “Accuracy Evaluation Of C4.5 And Naive Bayes Classifier Using Attribute Ranking Method,†Int. J. Comput. Intell. Syst., vol. 2, no. 1, pp. 60–68, 2009.
E. Indra, K. Ho, Arlinanda, R. Hakim, D. Sitanggang, and O. Sihombing, “Application of C4.5 Algorithm for Cattle Disease Classification,†J. Phys. Conf. Ser., vol. 1230, no. 1, 2019, doi: 10.1088/1742-6596/1230/1/012070.
Hermanto, S. J. Kuryanti, and S. N. Khasanah, “Comparison of Naïve Bayes Algorithm , C4 . 5 and Random Forest for Service Classification Ojek Online,†J. Publ. Informatics Eng. Res., vol. 3, no. April 2019, pp. 266–274, 2019.
B. R. C. T. I et al., “Implemetasi k-means clustering pada rapidminer untuk analisis daerah rawan kecelakaan,†no. April, pp. 58–62, 2017.
A. P. Windarto, P. Studi, S. Informasi, and D. Mining, “Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering,†vol. 16, no. 4, pp. 348–357, 2017.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).