Meta-Algoritme Adaptive Boosting untuk Meningkatkan Kinerja Metode Klasifikasi pada Prestasi Belajar Mahasiswa

  • Yuni Yamasari Institut Teknologi Sepuluh Nopember
  • Supeno M. S. Nugroho Institut Teknologi Sepuluh Nopember
  • Dwi F. Suyatno Institut Teknologi Sepuluh Nopember
  • Mauridhi Hery Purnomo Institut Teknologi Sepuluh Nopember
Keywords: klasifikasi, prestasi belajar, kinerja, adaptive boosting

Abstract

Determining the right class on student achievement is important in an evaluation process, because placing students in the right class helps lecturer in reflecting the successfullness of learning process. This problem relates to the performance of classification method which is measured by the classifier metrics. High performance is indicated by the optimality of these classifier's metrics. Besides, meta-algorithm adaptive boosting has been proven to be able to improve the performance of classifier in various fields. Therefore, this paper employs adaptive boosting to reduce the number of incorrect student placement in a class. The experimental results of implementing adaptive boosting in classifying student achievement shows that there is an increase of performance of all classification metrics, i.e., Kappa, Precision, Recall, F-Measure, ROC, and MAE. In terms of accuracy, J-48 is able to rise about 3.09%, which means this method reduces three misclassified students. Additionally, decisionStump increases 12.37% of accuracy. This also means this method is able to decrease 12 misclassified students. Finally, Simple Cart reaches the highest accuracy of about 23.71%, while the number of misclassified students is reduced to 24 students. However, there is no improvement in Random Forest method by using this adaptive boosting.

References

P. Wanarti, E. Ismayanti, H. Peni, and Y. Yamasari, “The

Enhancement of Teaching-Learning Process Effectiveness through the Development of Instructional Media Based on E-learning of Surabaya’s Vocational Student,” Advances in Economics, Business and Management Research, International Conference on Educational, Management, Administration and Leadership (ICEMAL 2016), vol. 14, pp. 342–346, 2016.

T. Mchichi, P. Estraillier, and K. Afdel, “Web 2.0 Based E-Learning,” 2011 International Conference on Multimedia Computing and Systems, 2010.

R. Yunis and K. Telaumbanua, “Pengembangan E-Learning

Berbasiskan LMS untuk Sekolah, Studi Kasus SMA/SMK di Sumatera Utara,” J. Nas. Tek. Elektro dan Teknol. Inf. (JNTETI, vol. 6, no. 1, pp. 32–36, 2017.

A. Zeileis, N. Umlauf, and F. Leisch, “Flexible Generation of ELearning Exams in R : Moodle Quizzes, OLAT Assessments, and

Beyond,” J. Stat. Softw., 2014.

K. F. Hew and W. S. Cheung, “Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges,” Educ. Res. Rev., vol. 12, pp. 45–58, 2014.

K. Hastuti, “Analisis komparasi algoritma klasifikasi data mining untuk prediksi mahasiswa non aktif,” Seminar Nasional Teknoogi Informasi & Komunikasi Terapan, Semantik, pp. 241–249, 2012.

J. N. Purwaningsih and Y. Suwarno, “Predicting students achievement based on motivation in vocational school using data mining approach,” 2016 4th International Conference on Information and Communication Technology (ICoICT), pp. 1–5, 2016.

A. Ktona, D. Xhaja, and I. Ninka, “Extracting Relationships Between Students â€TM Academic Performance and Their Area of Interest Using Data Mining Techniques,” Sixth International Conference on Computational Intelligence, Communication System and Network, pp. 6–11, 2014.

M. Imtiyaz, “Evaluating the Quality of Teaching in Higher Education Institutes using Clustering Approach and Set Pair Analysis,” 1st International Conference on Next Generation Computing Technologies (NGCT-2015), no. September, pp. 4–5, 2015.

S. Rana and R. Garg, “Application of Hierarchical Clustering Algorithm to Evaluate Students Performance of an Institute,” 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), pp. 692–697, 2016.

Y. Yamasari, D. Siahaan, and A. Z. Arifin, “Association Analysis using the Genetic Algorithms and Hybrid Measure for An Evaluation Process in the e-Learning System,” International Conference on Information and Communication Technology and Systems, ICTS 2008, 2008.

J. Zhou, L. Chen, C. L. P. Chen, Y. Zhang, and H. X. Li, “Fuzzy clustering with the entropy of attribute weights,” Neurocomputing, pp. 1–10, 2015.

C.-H. Li, B.-C. Kuo, and C.-T. Lin, “LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction,” IEEE Trans. Fuzzy Syst., vol. 19, no. 1, pp. 152–163, 2011.

R. Kang, T. Zhang, H. Tang, and W. Zhao, “Adaptive Region Boosting method with biased entropy for path planning in changing environment,” CAAI Trans. Intell. Technol., vol. 1, pp. 179–188, 2016.

Y. Chang and M. Hsu, “Development of a Visual Compressive Trackng System Enhanced by Adaptive Boosting,” in 41st Annual Conference of the IEEE Industrial Electronics Society (IECON 2015), pp. 3678–3682, 2015.

R. A. Galvan, “Integrase Inhibition Using Differential Evolution-Binary Particle Swarm Optimization and Non-Linear Adaptive Boosting Random Forest Regression,” 16th International Conference on Information Reuse and Integration, pp. 485–490, 2015.

K. Chen, Y. Li, and X. Xu, “Rotating Target Classification base on Micro-doppler Features Using a Modified Adaptive Boosting Algorithm,” International Conference on Computers, Communication, adn Systems, pp. 236–240, 2015.

A. R. Hassan, “Biomedical Signal Processing and Control Computeraided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting,” Biomed. Signal Process. Control, vol. 29, pp. 22–30, 2016.

B. Wang, T. Lu, and Z. Xiong, “Adaptive Boosting for Image Denoising : Beyond Low-Rank Representation and Sparse Coding,” 23rd International Conference on Pattern Recognition (ICPR), 2016.

Y. Yamasari, S. M. S. Nugroho, I. N. Sukajaya, and M. H. Purnomo, “Features extraction to improve performance of clustering process on student achievement,” 2016 International Computer Science and Engineering Conference (ICSEC), pp. 1–5, 2016.

Y. Freund and R. E. Schapire, “A Short Introduction to Boosting,” J. Japanese Soc. Artif. Intell., vol. 14, no. 5, pp. 771–780, 1999.

How to Cite
Yuni Yamasari, Supeno M. S. Nugroho, Dwi F. Suyatno, & Mauridhi Hery Purnomo. (1). Meta-Algoritme Adaptive Boosting untuk Meningkatkan Kinerja Metode Klasifikasi pada Prestasi Belajar Mahasiswa. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 6(3), 333-341. Retrieved from https://journal.ugm.ac.id/v3/JNTETI/article/view/2837
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