IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI DATA PESERTA DIDIK

  • Imam Sutoyo Teknologi Informasi Universitas Bina Sarana Informatika Jakarta
DOI: https://doi.org/10.33480/pilar.v14i2.70
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

Download data is not yet available.

References

Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data Mining: A prediction for Student’s Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43–47. https://doi.org/10.13189/WJCAT.2014.020203

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
Issue
Vol 14 No 2 (2018): Pilar Nusa Mandiri : Journal of Computing and Information System Periode September 2018
Section
Articles
License

An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:

  1. Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
  2. Author is 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 the acknowledgement of its initial publication in this journal.
  3. Author is permitted and encouraged to post his/her 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 the published work (See The Effect of Open Access).
    Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.