Perbandingan Unjuk Kerja Algoritme Klasifikasi Data Mining dalam Sistem Peringatan Dini Ketepatan Waktu Studi Mahasiswa

Performance Comparison of Data Mining Classification Algorithms for Early Warning System of Students Graduation Timeliness

Ari Fadli* -  Electrical Department, Universitas Jenderal Soedirman, Indonesia
Mulki Indana Zulfa -  Electrical Department, Universitas Jenderal Soedirman, Indonesia
Yogi Ramadhani -  Electrical Department, Universitas Jenderal Soedirman, Indonesia
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
Observation of growing academic data can be carried using data mining methods, for example, to obtain knowledge related to the determinants of timeliness of students graduation. This study conducted a performance comparison of the classification algorithms using decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). This study used students academic data from Faculty of Engineering, Universitas Jenderal Soedirman in the 2014/2015 odd semester until the 2017/2018 odd semester and the attributes that conform to the academic regulations. The analytical method used is CRISP-DM. The results showed that SVM provided the best performance in an accuracy of 90.55% and AUC of 0.959, compared to other algorithms. A Model with SVM algorithm can be implemented in an early warning system for timeliness of student graduation.
Keywords
graduation timeliness; data mining; data classification; data mining algorithms comparison
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Article Info
Submitted: 2018-09-20
Published: 2018-10-31
Section: Articles
Language: ID
Statistics: 116 94
  1. BAN-PT, Buku VI. Matriks Penilaian Instrumen Akreditasi Program Studi. Jakarta: Badan Akreditasi Nasional Perguruan Tinggi, 2008.
  2. D. Kabakchieva, “Predicting Student Performance by Using Data Mining,” Cybernetics and Information Technologies, vol. 13, no. 1, pp. 61–72, 2013.
  3. E. Osmanbegovic and M. Suljic, “Data Mining Approach for Predicting Student Performance,” Economic Review – Journal of Economics and Business, vol. X, issue 1, pp. 3–12, 2012.
  4. B. Santoso, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu, 2007.
  5. A. El-Halees, “Mining Students Data To Analyze Learning Behavior : a Case Study Educational Systems,” in Proc. of the 2008 International Arab Conference of Information Technology (ACIT2008), 15-18 Dec 2008, University of Sfax, Tunisia.
  6. S. Ahmed, R. Paul, A. Sayed, and L. Hoque, “Knowledge Discovery from Academic Data using Association Rule Mining,” in Proc. of 17th International Conference on Computer and Information Technology (ICCIT), 22-23 Dec 2014, Dhaka, Bangladesh, pp. 22–23.
  7. M. I. Al-Twijri and A. Y. Noaman, “A New Data Mining Model Adopted for Higher Institutions,” Procedia Computer Science, vol. 65, pp. 836–844, 2015.
  8. S. T. Karamouzis and A. Vrettos, “Sensitivity Analysis of Neural Network Parameters for Identifying the Factors for College Student Success,“ in Proc. of the 2019 World Congress on Computer Science and Information Engineering, 2 Apr 2009, Los Angeles, USA.
  9. M. S. Suhartinah and Ernastuti, “Graduation Prediction of Gunadarma University Students Using Algorithm Naive Bayes and C4.5 Algorithm,” Skripsi, Universitas Gunadarma, 2010.
  10. S. Salmu and A. Solichin, “Prediksi Tingkat Kelulusan Mahasiswa Tepat Waktu Menggunakan Naive Bayes: Studi Kasus UIN Syarif Hidayatullah Jakarta,” dalam Prosiding Seminar Nasional Multidisiplin Ilmu, 22 Apr 2017, Jakarta, Indonesia.
  11. Y.S. Samponu dan k. Kusrini, “Optimasi Algoritma Naive Bayes Menggunakan Metode Cross Validation Untuk Meningkatkan Akurasi Prediksi Tingkat Kelulusan Tepat Waktu,” Jurnal ELTIKOM, Vol. 1 No. 2, pp. 56–63, 2017.
  12. M. A. Banjarsari, H. I. Budiman dan A. Farmadi, “Penerapan K-Optimal pada Algoritma KNN untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer FMIPA UNLAM berdasarkan IP sampai dengan Semester 4,” Kumpulan Jurnal Ilmu Komputer, vol. 02, no.02, pp. 50–64, 2015.
  13. G. I. Marthasari, “Implementasi Teknik Data Mining untuk Evaluasi Kinerja Mahasiswa Berdasarkan Data Akademik,” Fountain of Informatics Journal, vol. 2, no. 2, pp. 56–63, 2017.
  14. M. H. Meinanda, M. Annisa, N. Muhandri, and K. Suryadi, “Prediksi Masa Studi Sarjana dengan Artificial Neural Network,” Internetworking Indonesia Journal, vol. 1, no. 2, pp. 31–35, 2009.
  15. I. Tahyudin, E. Utami, and A. Amborowati, “Comparing Clasification Algorithm of Data Mining to Predict the Graduation Students on Time,” in Proc. of the Information Systems International Conference (ISICO), 2-4 Dec 2013, Surabaya, Indonesia, pp. 379–384.
  16. R. Wirth and J. Hipp, “CRISP-DM: Towards a Standard Process Model for Data Mining,” in Proceedings of the 4th International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 2012, pp. 23-39.