Metode Optimasi Hibrida Berdasarkan Algoritma Genetik Untuk Kelulusan Mahasiswa

  • Ridwansyah Ridwansyah (1*) STMIK Nusa Mandiri, Jakarta, Indonesia
  • Ganda Wijaya (2) STMIK Nusa Mandiri, Jakarta, Indonesia
  • Jajang Jaya Purnama (3) Universitas Bina Sarana Informatika, Jakarta, Indonesia

  • (*) Corresponding Author
Keywords: Genetic Algorithm, Student Graduation, Hybrid Optimization


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.


Download data is not yet available.


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.

How to Cite
Ridwansyah, R., Wijaya, G., & Purnama, J. (2020). HYBRID OPTIMIZATION METHOD BASED ON GENETIC ALGORITHM FOR GRADUATES STUDENTS. Jurnal Pilar Nusa Mandiri, 16(1), 53-58. https://doi.org/10.33480/pilar.v16i1.1180
Article Metrics

Abstract viewed = 250 times
PDF downloaded = 199 times