Classification of Student Graduation using The Rough Set Method at Public Elementary School


Authors

  • Sigit Prabowo Universitas Pembangunan Panca Budi, Medan, Indonesia
  • Muhammad Iqbal Universitas Pembangunan Panca Budi, Medan, Indonesia

DOI:

https://doi.org/10.47065/jimat.v6i1.961

Keywords:

Classification; Student Graduation; Rough Set; Academic Data

Abstract

Education is an important process in developing individual potential, including intellectual, emotional, social, and moral aspects. The student graduation rate is the main indicator of educational success at the elementary school level. However, Vocational High School Putra Anda Binjai faces challenges in determining student graduation due to inaccuracy in classification, which can impact the quality of education. This study implements the Rough Set method as an approach in classifying student graduation based on academic factors such as grades, attendance, behavior, and character. The Rough Set method is able to handle inaccurate and inconsistent data and find hidden patterns that can improve classification accuracy. This study uses student academic datasets to build a classification model that will be evaluated using accuracy and effectiveness measures. This study contributes to improving academic decision making and the quality of education in elementary schools through more accurate graduation classification.

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References

K. Kuczera and D. Dziembek, “Application of Rough Set Theory to Improve the Efficiency of Higher Education Systems,” in European Conference on Artificial Intelligence, Springer, 2024, pp. 237–249, doi: 10.1007/978-3-031-78468-2_18

F. Ekundayo, I. Atoyebi, A. Soyele, and E. Ogunwobi, “Predictive analytics for cyber threat intelligence in fintech using big data and machine learning,” Int J Res Publ Rev, vol. 5, no. 11, pp. 1–15, 2024, doi: 10.55248/gengpi.5.1124.3352

S. A. Putri, N. Selayanti, M. Kristanaya, M. P. Azzahra, M. G. Navsih, and K. M. Hindrayani, “Penerapan Machine Learning Algoritma Random Forest Untuk Prediksi Penyakit Jantung,” in Prosiding Seminar Nasional Sains Data, 2024, pp. 895–906, doi: 10.33005/senada.v4i1.376

G. Saputri, “Using The Borda Methode On A Decision Support System,” J. Data Anal. Information, Comput. Sci., vol. 1, no. 1, pp. 19–24, 2024, doi: 10.59407/jdaics.v1i1.422

A. Arif, “Penerapan Metode Extreme Programming Pada E-Voting Pemilihan Ketua Unit Kegiatan Mahasiswa (UKM) Sekolah Tinggi Teknologi XYZ,” J. Sist. dan Teknol. Inf., vol. 9, no. 2, p. 234, 2021, doi: 10.26418/justin.v9i2.44266.

N. Nurdiansyah, F. S. Febriyan, Z. Gesit, and D. Amanta, “Mental Health Analysis to Prevent Mental Disorders in Students Using The K-Nearest Neighbor ( K-NN ),” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 5, no. January, pp. 1–9, 2025, doi: https://doi.org/10.57152/malcom.v5i1.1537.

A. E. Widjaja, A. Fransisko, and C. A. Haryani, “Text Mining Application with K-Means Clustering to Identify Sentiments and Popular Topics?: a Case Study of the three Largest Online Marketplaces in Indonesia,” Inform. J. Ilmu Komput., vol. 4, no. 4, pp. 441–453, 2023, doi: 10.47738/jads.v4i4.134

S. Dewi, A. Kresnawati, S. Sopyanti, and A. Sulmainah, “Mapping Library User Behavior Base On K-Means Clustering Of Ma ’ soem University Student Pemetaan Perilaku Pengguna Perpustakaan Berbasis K-Means Pada Mahasiswa Universitas Ma ’ soem,” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 5, no. 2, pp. 130–136, 2025, doi: 10.57152/ijirse.v5i2.2238

S. Howay and S. Suhirman, “Comparison of SVM, NBC, and KNN Classification Methods in Determining Students’ Majors at SMK N02 Manokwari,” J. Comput. Sci. Technol. Stud., vol. 5, no. 1, pp. 15–23, 2023, doi: 10.32996/jcsts.2023.5.1.3.

M. T. Hidayat, M. Arifin, and S. Muzid, “Prediction Sentiment Analysis Grab Reviews using SVM Linear Based Streamlit,” Indones. J. Comput. Cybern. Syst., vol. 19, no. 2, pp. 1–12, 2025, doi: 10.22146/ijccs.104924.

M. Herviany, S. Putri Delima, T. Nurhidayah, and Kasini, “Comparison of K-Means and K-Medoids Algorithms for Grouping Landslide Prone Areas in West Java Province,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 34–40, 2021.

H. Mustakim and S. Priyanta, “Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 16, no. 2, p. 113, 2022, doi: 10.22146/ijccs.68903.

H. Sunaryanto, M. A. Hasan, and G. Guntoro, “Classification Analysis of Unilak Informatics Engineering Students Using Support Vector Machine (SVM), Iterative Dichotomiser 3 (ID3), Random Forest and K-Nearest Neighbors (KNN),” IT J. Res. Dev., vol. 7, no. 1, pp. 36–42, 2022, doi: 10.25299/itjrd.2022.8912.

N. A. Ochuba, O. O. Amoo, E. S. Okafor, O. Akinrinola, and F. O. Usman, “Strategies for leveraging big data and analytics for business development: a comprehensive review across sectors,” Comput. Sci. IT Res. J., vol. 5, no. 3, pp. 562–575, 2024.

A. Pambudi and S. Suprapto, “Effect of Sentence Length in Sentiment Analysis Using Support Vector Machine and Convolutional Neural Network Method,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 15, no. 1, p. 21, 2021, doi: 10.22146/ijccs.61627.

S. Samaray, “Implementasi Algoritma Rough Set dengan Software Rosetta untuk Prediksi Hasil Belajar,” J. Eksplora Inform., vol. 11, no. 1, pp. 57–66, 2022, doi: 10.30864/eksplora.v11i1.498.

A. A. Ahmed et al., “Arabic text detection using rough set theory: Designing a novel approach,” IEEE Access, vol. 11, pp. 68428–68438, 2023, doi: 10.1109/ACCESS.2023.3278272

N. F. Munazhif, G. J. Yanris, and M. N. S. Hasibuan, “Implementation of the K-Nearest Neighbor (kNN) Method to Determine Outstanding Student Classes,” SinkrOn, vol. 8, no. 2, pp. 719–732, 2023, doi: 10.33395/sinkron.v8i2.12227.

J. Maulani and M. Sari, “Komparasi Metode K-Nearest Neighbor (Knn) Dengan Support Vector Machine (Svm) Terhadap Tingkat Akurasi Klasifikasi Kualitas Air,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 12, no. 2, pp. 430–435, 2023, doi: 10.30591/smartcomp.v12i2.4205.

S. Suryanto and W. Andriyani, “Sentiment Analysis of X Platform on Viral ‘Fufufafa’ Account Issue in Indonesia Using SVM,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 19, no. 1, p. 95, 2025, doi: 10.22146/ijccs.104158.


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Published: 2026-01-31

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