HYBRID OPTIMIZATION METHOD BASED ON GENETIC ALGORITHM FOR GRADUATES STUDENTS

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

Abstract

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.

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Published
2020-03-15
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
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