Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)
Saragih, Windi Sri Utami
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penerapan Text Mining Dengan Menggunakan Metode TF-IDF Untuk Menentukan Genre Dari Komik Saragih, Windi Sri Utami; Hasibuan, Nelly Astuti; Hondro, Rivalri Kristianto
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2679

Abstract

Comics are literary works whose story descriptions are displayed in the form of pictures and in the story there is one character who is favored. The division of comics into genres is less effective, because the words represent inappropriate genres. Therefore we need a system that can determine the genre of a comic automatically due to the many genres of the comic. This study uses text mining and TF-IDF for the process of determining the comic genre. Text mining can be defined as the discovery of new information that was previously unknown to computers by extracting automatic information from different sources. Meanwhile, resource data is used as a reference in determining the comic genre. In this study, comics were divided into 4 categories: horror, inspirational, mystery and romantic. The text entered is in the form of title, author, and synopsis. This synopsis will be processed to produce a comic genre. The first process is the process of document preparation and document selection. Then proceed with the word weighting process using TF-IDF, then determining the genre of comics is done by comparing the similarity value between the text and a node in the resource data. The text obtained will be classified in an existing genre or node, if it has the highest similarity value in one of the nodes in the data resource. It is hoped that the system to be built is in accordance with the expectations of researchers, so that the system to be built can determine the genre of a comic.Keywords: Classification, Comic, Text Mining, Resource Data, TFIDF
Co-Authors Nelly Astuti Hasibuan Rivalri Kristianto Hondro