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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI DATA PESERTA DIDIK
Keywords:
Algoritma Data Mining, Decission Tree, C45, Classification Algorithm, CRISP-DM Methodology, Random Forest
Abstract
Klasifikasi peserta didik bertujuan untuk mengelompokkan peserta program pendidikan agar kegiatan pembelajaran dapat disesuaikan dengan kelompok-kelompok tersebut. Metode tradisional untuk melaksanakan klasifikasi ini adalah dengan mengurutkan peserta didik menggunakan satu atribut, yaitu nilai akhir mereka kemudian membagi mereka berdasarkan ukuran tertentu. Metode yang lebih baik adalah dengan menggunakan algoritma Data Mining yang mampu menggunakan lebih dari satu atribut. Pada penelitian ini, algoritma Decision Tree digunakan untuk melaksanakan klasifikasi. Metodologi yang digunakan adalah CRISP-DM. Algoritma Decision Tree yang diujicoba adalah C4.5 dan Random Forest. Validasi dilaksanakan menggunakan 10-Fold Cross Validation untuk dicari algoritma yang memberikan akurasi paling tinggi. Berdasarkan percobaan, didapatkan hasil bahwasanya Decission Tree C.45 memberikan hasil terbaik dengan akurasi 96,73 %. Oleh karena itu, pada tahap Deployment dari metodologi CRISP-DM, model dan rule dari C4.5 digunakan untuk membuat aplikasi untuk klasifikasi ini.Downloads
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References
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Cerón, J. D., López, D. M., & Eskofier, B. M. (2018). Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach. Proceedings, 2(19), 1265. https://doi.org/10.3390/proceedings2191265
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (3rd Editio). Waltham, USA: Morgan Kaufmann Publishers.
Larasati, D. A. H. D., & Sutrisno, T. (2018). Tourism Site Recommendation in Jakarta Using Decision Tree Method Based on Web Review, 195–209.
North, M. A. (2012). Data Mining for the Masses. Computer Global Text Project. Georgia: Global Text Project.
Saber Iraji, M., Aboutalebi, M., Seyedaghaee, N. R., & Tosinia, A. (2012). Students Classification With Adaptive Neuro Fuzzy. International Journal of Modern Education and Computer Science, 4(7), 42–49. https://doi.org/10.5815/ijmecs.2012.07.06
Sutoyo, I. (2018). Laporan Akhir Penelitian “Implementasi Algoritma Decision Tree Untuk Klasifikasi Data Peserta Didik.” Jakarta.
Wambui, E., George, N., & Kimani, S. (2018). An Intelligent Model for Fleet Management by Use of Sensor Enabled Tags Integrated With GPRS Technology, 4(11), 30–40. https://doi.org/10.31695/IJASRE.2018.32908
Yadav, S. K., & Pal, S. (2012). Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal (WCSIT), 2(2), 51–56. Retrieved from http://arxiv.org/abs/1203.3832
Ye, N. (2014). Data Mining Theories, Algorithms, and Examples. 6000 Broken Sound Parkway NW: Taylor & Francis Group, LLC.
How to Cite
Sutoyo, I. (1). IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI DATA PESERTA DIDIK. Jurnal Pilar Nusa Mandiri, 14(2), 217-224. https://doi.org/10.33480/pilar.v14i2.70
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