QSAR Study of Larvicidal Phytocompounds as Anti-Aedes Aegypti by using GA-SVM Method

  • Komang Triolascarya Telkom University
  • Reza Rendian Septiawan Telkom University
  • Isman Kurniawan Telkom University
Keywords: Aedes aegypti, Genetic Algorithm, QSAR, Support Vector Machine


Aedes aegypti is one of the most dangerous mosquitoes that can cause several deadly diseases, such as dengue fever, Chikungunya, Zika, and jaundice with high mortality rate. For now, no specific drug has been found that can cure the disease caused by Aedes Aegypti. One possible solution for handling this problem is to inhibit the growth and development of Aedes aegypti larvae. This study aims to implement Genetic Algorithm-Support Vector Machine to develop Quantitative Structure-Activity Relationship model for identification larvicidal phytocompounds as anti-aedes-aegypti. Hyperparameter tuning was performed to improve the performance of the models. Based on the result, we found that the best model was developed by the RBF kernel with the value of    and  score are 0.64 and 0.64, respectively.


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How to Cite
Triolascarya, K., Rendian Septiawan, R., & Kurniawan, I. (2022). QSAR Study of Larvicidal Phytocompounds as Anti-Aedes Aegypti by using GA-SVM Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 632 - 638. https://doi.org/10.29207/resti.v6i4.4273
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