Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
HYBRID OPTIMIZATION METHOD BASED ON GENETIC ALGORITHM FOR GRADUATES STUDENTS
Metode Optimasi Hibrida Berdasarkan Algoritma Genetik Untuk Kelulusan Mahasiswa
Graduation is a target that must be achieved by students, especially graduating on time will be very important. To determine students who graduate on time or cannot be determined before students reach the final semester and hold a trial, many students who fail to graduate on time cause delays and affect the quality assurance of a tertiary institution. The problem in this research is how to optimize student graduation in order to graduate on time. Therefore, to determine this decision, we conducted a graduation data trial using the SVM method with GA optimization. SVM with accurate learning skills and good generalizations in classifying non-linear data, but SVM is weak in terms of parameter optimization it requires optimization using GA. GA is a method that has evolved to produce a more optimal data. From the results of processing using SVM and GA, we get more optimal results with 86.57%. Then from these results can help students to graduate on time.
Ashok, M. V., and A. Apoorva. 2016. “Data Mining Approach for Predicting Student and Institution’s Placement Percentage.” 2016 International Conference on Computation System and Information Technology for Sustainable Solutions, CSITSS 2016: 336–40.
Bin, Li, and Yang Min. 2012. “Analysis Model of Drilling Tool Failure Based on PSO-SVM and Its Application.” Proceedings - 4th International Conference on Computational and Information Sciences, ICCIS 2012 (8): 1307–10.
Devasia, Ms.Tismy, Ms.Vinushree T P, and Mr.Vinayak Hegde. 2008. “Prediction of Students Performance Using Educational Data Mining.” International Journal of Cognitive Therapy 1(3): 266–79.
Freitas, Frances Anne, and Lora J. Leonard. 2011. “Maslow’s Hierarchy of Needs and Student Academic Success.” Teaching and Learning in Nursing 6(1): 9–13.
Gao, Xiang Ming, Shi Feng Yang, and Yu Hu. 2010. “Leakage Forecasting for Water Supply Network Based on GA-SVM Model.” Proceedings of the 2010 Symposium on Piezoelectricity, Acoustic Waves and Device Applications, SPAWDA10: 206–9.
Jiang, Huiyan, Fengzhen Tang, and Xiyue Zhang. 2010. “Liver Cancer Identification Based on PSO-SVM Model.” 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 (December): 2519–23.
Li, Hua, and YongXin Zhang. 2009. “An Algorithm of Soft Fault Diagnosis for Analog Circuit Based on the Optimized SVM by GA.” In 2009 9th International Conference on Electronic Measurement & Instruments, China: IEEE.
Liu, Han et al. 2019. “Effective Data Classification via Combining Neural Networks and SVM.” Proceedings of the 31st Chinese Control and Decision Conference, CCDC 2019: 4006–9.
Ridwansyah, Ridwansyah, Ganda Wijaya, and Jajang Jaya Purnama. 2020. Laporan Akhir Penelitian Mandiri. Jakarta.
Riyanto, Verry, Abdul Hamid, and Ridwansyah. 2019. “Prediction of Student Graduation Time Using the Best Algorithm.” Indonesian Journal of Artificial Intelligence and Data Mining 2(2): 1–9.
Suhardjono, Ganda Wijaya, and Abdul Hamid. 2019. “PREDIKSI WAKTU KELULUSAN MAHASISWA MENGGUNAKAN SVM BERBASIS PSO.” Bianglala Informatika 7(2): 97–101.
Wang, Gui Ping, Jian Xi Yang, and Ren Li. 2017. “Imbalanced SVM-Based Anomaly Detection Algorithm for Imbalanced Training Datasets.” ETRI Journal 39(5): 621–31.
Ye, Xuehui, Yuxia Li, Ling Tong, and Ling He. 2017. “Remote Sensing Retrieval of Suspended Solids in Longquan Lake Based on GA-SVM Model.” International Geoscience and Remote Sensing Symposium (IGARSS) 2017-July: 5501–4.
Yu, Ting Chun, and Jui Chung Hung. 2017. “Forecasting MLB Playoff Teams Using GA-SVM.” Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017: 446–48.
Abstract viewed = 250 times
PDF downloaded = 199 times
Copyright (c) 2020 Ridwansyah Ridwansyah, Ganda Wijaya, Jajang Jaya Purnama
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- 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
- 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.
- 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/.