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JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika

Classification of short stories category based on age of the reader is still difficult. Therefore, a decision support system to classify the short stories category is needed. Naïve Bayes is one of methods suitable for short stories classification. However, Naïve Bayes has flaws in accuracy level,...

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Main Author: Oman Somantri, Mohammad Khambali
Format: Jurnal
Language: Bahasa Indonesia
Published: Teknik elektro UGM 2017
Subjects:
Online Access: http://oaipmh-jogjalib.umy.ac.idkatalog.php?opo=lihatDetilKatalog&id=82869
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spelling oai:lib.umy.ac.id:828692021-06-16T13:11:06ZJURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme GenetikaOman Somantri, Mohammad Khambali Classification of short stories category based on age of the reader is still difficult. Therefore, a decision support system to classify the short stories category is needed. Naïve Bayes is one of methods suitable for short stories classification. However, Naïve Bayes has flaws in accuracy level, and needs to be optimized. In this paper, Genetic algorithm is proposed to increase the level of accuracy. In this case, genetic algorithm is used for feature selection. The results show an increase in the level of accuracy produced. The accuracy increases from 78,59% to 84,29%. In conclusion, the application of genetic algorithm on Naïve Bayes in classifying the online short stories category can improve the accuracy. Teknik elektro UGM2017JurnalISBN:ISSN : 2301-4156JNTETI TAHUN VOL NO 2015 6 3 Bahasa Indonesiahttp://oaipmh-jogjalib.umy.ac.idkatalog.php?opo=lihatDetilKatalog&id=82869
institution Universitas Muhammadiyah Yogyakarta
collection Perpustakaan Yogyakarta
language Bahasa Indonesia
topic
spellingShingle
Oman Somantri, Mohammad Khambali
JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika
description Classification of short stories category based on age of the reader is still difficult. Therefore, a decision support system to classify the short stories category is needed. Naïve Bayes is one of methods suitable for short stories classification. However, Naïve Bayes has flaws in accuracy level, and needs to be optimized. In this paper, Genetic algorithm is proposed to increase the level of accuracy. In this case, genetic algorithm is used for feature selection. The results show an increase in the level of accuracy produced. The accuracy increases from 78,59% to 84,29%. In conclusion, the application of genetic algorithm on Naïve Bayes in classifying the online short stories category can improve the accuracy.
format Jurnal
author Oman Somantri, Mohammad Khambali
author_sort Oman Somantri, Mohammad Khambali
title JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika
title_short JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika
title_full JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika
title_fullStr JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika
title_full_unstemmed JURNAL NASIONAL TEKNIK ELEKTRO DAN TEKNOLOGI INFORMASI: Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika
title_sort jurnal nasional teknik elektro dan teknologi informasi: feature selection klasifikasi kategori cerita pendek menggunakan naïve bayes dan algoritme genetika
publisher Teknik elektro UGM
publishDate 2017
url http://oaipmh-jogjalib.umy.ac.idkatalog.php?opo=lihatDetilKatalog&id=82869
isbn ISBN:ISSN : 2301-4156
callnumber-raw JNTETI TAHUN VOL NO 2015 6 3
callnumber-search JNTETI TAHUN VOL NO 2015 6 3
_version_ 1702754778097909760
score 14.79448

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