Implementasi Algoritme Decision Tree (C4.5) dengan Optimize Weights (PSO) untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu

Sudriyanto Sudriyanto, Rudi Rizaldi, M. Ainun Rofiq Hariri

Abstract


The implementation of quality education is a requirement for higher education institutions to produce quality students. Government regulations regarding the limitation of the maximum length of lectures that students can take are intended so that the institutions involved in the educational process have careful planning in the learning process. Good planning is expected to be able to accreditation value of higher education institutions and deliver student studies so that they can graduate on time. In order to achieve these results, aspects that are so influential on the optimal value of accreditation results are needed, one of which is that students graduate on time and play an importan role in determining the results of accreditation. Decision tree itself is a very simple algorithm and easy understanding, decision tree algorithm alonei is enough to produc good and optimal accuracy values. Therefore, the optimization method particle swarm optimization (PSO) with its advantages can increase the level of accuracy by removing irrelevant features. The results of the study using a student dataset of class 2016 - 2017 explain that the optimization ofparticle swarm optimization (PSO) can produce 92.36% accuracy and increase the accuracy of 01.05%, with the decision tre method C4.5 with an accuracy rate of 91.31%. Furthermore, the T-test testing process is carried out on the decision tree algorithm C4.5 and the decison tree algorithm C4.5 optimization of particle swarm optimization (PSO) with the final result alpha = 0.635 where the results are less significant, it is said to be significant if the test results are below alpha = 0.050.

 


Keywords


Decision Tree (C4.5); Particle Swarm Optimization (PSO); Student Graduation

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References


Agus, B., Irwansyah, Egi, P., andi, A. P., & Ninuk, W. (2020). Komparasi Algoritma Decision Tree, Naive Bayes Dan KNearest Neighbor Untuk Memprediksi Mahasiswa Lulus Tepat Waktu. Juenal Ilmu Pengetahuan dan Teknilogi Komputer, 265 - 270.

Arie, Y. S., & Yogi, P. (2018). Penerapan Teknik Klasifikasi Untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritma KNearest Neighbour. Techno.COM, 395 - 403.

Brijesh, K. B., & Saurabh, P. (2011). Data Mining: A predictioon for performance improvement using classification. (IJCSIS) International Journal of Computer Science and Information Security.

Devi, S., Ichsan, T., Jumadi, & Wildan, B. Z. (2016). Klasifikasii Terjemahan Ayat Al-Quran Tentang Ilmuu Sains Menggunakan Algoritma Decision Tre Berbasis Mobile. (JOIN) Jurnal Online Informatika, 24 - 27.

Eko, P., Kusrini, & Sudarmawan. (2019). Prediksi Kelulusan tpat waktu Menggunakan Metode C 45 dan K-nn. TECHNO, 131 - 142.

F., G. (2011). Data Mining: Concepts, Models and Technique. USE.

Hong-qi, T., Hui, L., Chao, C., & Yan-fei, L. (2013). An experimental investigaton of two Wavelet-MLP hybrid frameworkss for wind speed prediction using GA and PSO optimization. Electrical Powerr and Energy Systems, 161–173.

Hongyan, C., Jianfang, C., Hao, S., & Lijuan, J. (2016). Big Data: A Parallel Partcle Swarm Optimization-Back-Propagation Neural Network Algorithm Based MapReduce. PLoS One, 1 - 17.

R.M., H., F., V., & Perez-, R. (2008). Priorities for fisheries in marine protected areea design and management: Implicatioons for artisanal-type fisheries as found in southern Europe. Journal forNature Conservation, 222 - 233.

S., H., & D., H. K. (2014). Implementasi Data Mining dengan Algoritma C4.5i untuk Memprediksi Tingkat Kelulusan Mahasiswa. Jurnal ULTIMATICS, 15 - 20.

Selvia, L. B., Wendi, Z., & Ida, H. (2014). Analisis Dan Penerapannn Algoritma C4.5 Dalam Data Mining Untukk Memprediksi Masa Studi Mahasiswa Berdasarkan Data Nilai Akademik. Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) (hal. 263 - 272). Yogyakarta: 15 November 2014.

Tao, X., Yinzhao, C., & Qinke, P. (2012). Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowledge-Based Systems, 279–289.

Verry, R., Abdul, H., & Ridwansyah. (2019). Prediction of Student Graduation Time Using The Best Algorthm. Indonesian Journal of Artificial Inteligence and Data Mining (IJAIDM), 1 - 9.

Wu, K. (2009). The Top Ten Algorithms in Data Mining. USE: Press CRC.




DOI: http://dx.doi.org/10.32493/informatika.v6i2.9197

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Jurnal Informatika Universitas Pamulang (ISSN: 2541-1004 e-ISSN: 2622-4615)



This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License