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

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

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%

Styawati1
, Andi Nurkholis2
, Zaenal Abidin3
, Heni Sulistiani4

Universitas Teknokrat Indonesia

25-10-2021

Fajar bagus W

PDF

Indonesia

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