Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah)

 Mokhamad Ramdhani Raharjo (Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, Banjarmasin, Indonesia)
 (*)Agus Perdana Windarto Mail (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

(*) Corresponding Author

Submitted: December 30, 2020; Published: January 22, 2021

DOI: http://dx.doi.org/10.30865/mib.v5i1.2745

Abstract

The Rough Set (RS) method is part of machine learning that analyzes the uncertainty of the dataset used to determine the attributes of important objects (classification). The purpose of this study was to extract information from the rough set using the Rosetta application in predicting cases of students' level of understanding of the course. The attributes used are communication (F1), learning atmosphere (F2), learning media (F3), appearance (F4), and teaching methods (F5). Sources of data obtained from the output of the Journal of Physics: Conference Series, 1255 (1). https://doi.org/10.1088/1742-6596/1255/1/012005. The results of the application of the Rough Set method in determining the prediction of the level of student understanding of the course, produce new knowledge, namely learning outcomes based on the subject. There are 15 Reductions with 90 Generate Rules. But overall, the attributes that affect the level of student understanding of the subject are communication (F1) and learning media (F3)

Keywords


Data Mining; Rough Set; Generate Rules; Level of Understanding; Subjects; Students

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References

Jeprianto and R. A. Aziz, “Implementasi Algoritma Rough Set Dan Naive Bayes Untuk Mendapatkan Rule Dalam Menyeleksi Pemohon Bantuan Fasilitas Rumah Ibadah ( Studi Kasus : Pemerintah Kabupaten Pringsewu ),” JTKSI, vol. 03, no. 02, pp. 74–83, 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.

Z. R. S. Elsi et al., “Utilization of Data Mining Techniques in National Food Security during the Covid-19 Pandemic in Indonesia,” J. Phys. Conf. Ser., vol. 1594, no. 1, 2020, doi: 10.1088/1742-6596/1594/1/012007.

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.

A. Putra, Z. A. Matondang, N. Sitompul, I. Pendahuluan, and A. Prediksi, “Implementasi Algoritma Rough Set Dalam Memprediksi Kecerdasan Anak,” J. Pelita Inform., vol. 7, no. 2, pp. 149–156, 2018.

A. Apriani, I. T. R. Yanto, S. Fathurrohmah, S. Haryatmi, and Danardono, “Variable precision rough set model for attribute selection on environment impact dataset,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, pp. 70–75, 2018, doi: 10.26555/ijain.v4i1.109.

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.

S. Sulaiman, N. A. A. Rahim, and A. Pranolo, “Generated rules for AIDS and e-learning classifier using rough set approach,” Int. J. Adv. Intell. Informatics, vol. 2, no. 2, pp. 103–122, 2016, doi: 10.26555/ijain.v2i2.74.

M. T. J. Sinaga, R. Goejantoro, and F. D. T. Amijaya, “Penerapan Metode If-Then dari Rough Set Theory dalam Menangani Kecelakaan Lalu Lintas di Kota Samarinda Tahun 2016,” J. EKSPONENSIAL, vol. 8, no. 2, pp. 145–150, 2017.

U. Indriani, “Penerapan Metode Rough Set Dalam Menentukan Pembelian Smartphone Android Oleh Konsumen,” J. Tek. Inform. Kaputama, vol. 2, no. 1, pp. 85–92, 2018.

M. Jamaris, “Implementasi Metode Rough Set Untuk Menentukan Kelayakan Bantuan Dana Hibah Fasilitas Rumah Ibadah,” INOVTEK Polbeng - Seri Inform., vol. 2, no. 2, p. 161, 2017, doi: 10.35314/isi.v2i2.203.

H. Juliansa, S. Defit, and Sumijan, “Identifikaasi Tingkat Kerusakan Peralatan Laboratorium Komputer Menggunakan Metode Rough Set,” J. Resti, vol. 2, no. 1, pp. 410–415, 2018.

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