DOI: https://doi.org/10.14710/jtsiskom.6.4.2018.158-163

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

Electrical Department, Universitas Jenderal Soedirman, Indonesia

Received: 20 Sep 2018; Published: 31 Oct 2018.
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
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.
Fulltext View|Download Email colleagues
Keywords: graduation timeliness; data mining; data classification; data mining algorithms comparison
Funding: LPPM Unsoed Riset Skim Dosen Pemula

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Decision Support System in Recommending Best Unit in PDAM Tirta Lihou Using Promethee Method Sistem Pengukur Suhu dan Kelembaban Ruang Server Pemanfaatan Augmented Reality untuk Pembelajaran Pengenalan Alat Musik Piano Sistem Pengendali Peralatan Elektronik dalam Rumah secara Otomatis Menggunakan Sensor PIR, Sensor LM35, dan Sensor LDR Pembuatan Aplikasi Antar-Jemput Laundry Berbasis Web Service pada Platform Android More articles
Most cited articles
Aplikasi Pengolahan Citra Digital Meat Detection Dengan Metode Segmentasi K-Mean Clustering Berbasis OpenCV Dan Eclipse Location Based Service Panduan Pencarian Rumah Sakit dengan Platform Android di Kota Semarang Sistem Pendeteksi Kualitas Daging Dengan Ekualisasi Histogram Dan Thresholding Berbasis Android Intelligent Robot for Measuring Consumer Satisfaction at Shopping Center Perancangan Permainan “UangAndro” Sebagai Media Pembelajaran Pengenalan Mata Uang Rupiah Pada Anak Berbasis Android More cited articles
  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

Last update:

  1. Application caching strategy based on in-memory using Redis server to accelerate relational data access

    Mulki Indana Zulfa, Ari Fadli, Arief Wisnu Wardhana. Jurnal Teknologi dan Sistem Komputer, 8 (2), 2020. doi: 10.14710/jtsiskom.8.2.2020.157-163

  2. SPOC online video learning clustering analysis: Identifying learners' group behavior characteristics

    Fei Li, Yang Lu, Qiang Ma, Juntao Gao, Zhibao Wang, Lu Bai. Computer Applications in Engineering Education, 31 (4), 2023. doi: 10.1002/cae.22624

  3. Predicting Student Performance Through Data Mining: A Case Study in Sultan Ageng Tirtayasa University

    Rocky Alfanz, Raphael Kusumo Hendrianto, Al Hafiz Akbar Maulana Siagian. Journal of Advanced Computational Intelligence and Intelligent Informatics, 27 (6), 2023. doi: 10.20965/jaciii.2023.p1159

Last update: 2024-11-21 22:30:35

No citation recorded.