Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film

  • Styawati Universitas Teknokrat Indonesia
  • Andi Nurkholis Universitas Teknokrat Indonesia
  • Zaenal Abidin Universitas Teknokrat Indonesia
  • Heni Sulistiani Universitas Teknokrat Indonesia
Keywords: SVM, FA-SVM, Classification, Optimization, Public Opinion


The Support Vector Machine (SVM) method is a method that is widely used in the classification process. The success of the classification of the SVM method depends on the soft margin coefficient C, as well as the parameter  of the kernel function. The SVM parameters are usually obtained by trial and error, but this method takes a long time because they have to try every combination of SVM parameters, therefore the purpose of this study is to find the optimal SVM parameter value based on accuracy. This study uses the Firefly Algorithm (FA) as a method for optimizing SVM parameters. The data set used in this study is data on public opinion on several films. Class labels used in data classification are positive class labels and negative class labels. The amount of data used in this study is 2179 data, with the distribution of 436 data as test data and 1743 data as training data. Based on this data, an evaluation process was carried out on the Firefly Algorithm-Support Vector Machine (FA-SVM). The results of this study indicate that the Firefly Algorithm can obtain the optimal combination of SVM parameters based on accuracy, so there is no need for trial and error to get that value. This is evidenced by the results of the FA-SVM evaluation using a value range of C=1.0-3.0 and =0.1-1.0 resulting in the highest accuracy of 87.84%. The next evaluation using a range of values ​​C=1.0-3.0 and =1.0-2.0 resulted in the highest accuracy of 87.15%.


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How to Cite
Styawati, Andi Nurkholis, Zaenal Abidin, & Heni Sulistiani. (2021). Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(5), 904 - 910. https://doi.org/10.29207/resti.v5i5.3380
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