Selection of Student Extracurricular using Hybrid Multi-Criteria Recommendation System and Particle Swarm Optimization
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Arif Budiman Harahap, Antoni Wibowo
|DOI : 10.14445/22315381/IJETT-V70I6P218|
How to Cite?
Arif Budiman Harahap, Antoni Wibowo, "Selection of Student Extracurricular using Hybrid Multi-Criteria Recommendation System and Particle Swarm Optimization," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 144-154, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P218
Along with the times, the latest technology can help users find needs according to their conditions and interests by using a recommendation system. The development of user needs also makes the techniques used in the recommendation system more varied. One of the benefits can be used to determine extracurricular according to the talents and interests of students. Extracurricular is an additional activity at school that can be a means to hone students' talents and interests. Therefore, students need to be able to identify the appropriate talents and interests from an early age so that the talents possessed by students can develop properly. Several studies have been done previously, one of which is to combine the hybrid method and MCRS with the GA method. However, other studies have been carried out and found that the PSO method can produce better outputs than GA. Therefore, a hybrid method between the MCRS and PSO methods is combined with student extracurricular recommendations. As a result, the proposed method produces a longer execution time of 19.108 seconds but produces a better error percentage of 2.45% compared to the hybrid MCRS and GA methods.
Genetic Algorithm, Hybrid, Multi-Criteria, Particle Swarm Optimization, Recommendation System.
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