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

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...

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Autores principales:	Styawati, Andi Nurkholis, Zaenal Abidin, Heni Sulistiani
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	svm, fa-svm, classification, optimization, public opinion Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/7d5a8cd39bd446f796a5db12d597b93c
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spelling	oai:doaj.org-article:7d5a8cd39bd446f796a5db12d597b93c2021-11-16T13:16:12ZOptimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film2580-076010.29207/resti.v5i5.3380https://doaj.org/article/7d5a8cd39bd446f796a5db12d597b93c2021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3380https://doaj.org/toc/2580-0760The 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%.StyawatiAndi NurkholisZaenal AbidinHeni SulistianiIkatan Ahli Indormatika Indonesiaarticlesvm, fa-svm, classification, optimization, public opinionSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 904-910 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	svm, fa-svm, classification, optimization, public opinion Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	svm, fa-svm, classification, optimization, public opinion Systems engineering TA168 Information technology T58.5-58.64 Styawati Andi Nurkholis Zaenal Abidin Heni Sulistiani Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film
description	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%.
format	article
author	Styawati Andi Nurkholis Zaenal Abidin Heni Sulistiani
author_facet	Styawati Andi Nurkholis Zaenal Abidin Heni Sulistiani
author_sort	Styawati
title	Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film
title_short	Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film
title_full	Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film
title_fullStr	Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film
title_full_unstemmed	Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film
title_sort	optimasi parameter support vector machine berbasis algoritma firefly pada data opini film
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/7d5a8cd39bd446f796a5db12d597b93c
work_keys_str_mv	AT styawati optimasiparametersupportvectormachineberbasisalgoritmafireflypadadataopinifilm AT andinurkholis optimasiparametersupportvectormachineberbasisalgoritmafireflypadadataopinifilm AT zaenalabidin optimasiparametersupportvectormachineberbasisalgoritmafireflypadadataopinifilm AT henisulistiani optimasiparametersupportvectormachineberbasisalgoritmafireflypadadataopinifilm
_version_	1718426488634605568

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

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