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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Wibawa, Putu Widyantara Artanta
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data Wibawa, Putu Widyantara Artanta; Pramartha, Cokorda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1787

Abstract

Emotions are a person's response to an event. Emotions can be expressed verbally or nonverbally. Over time people can express their emotions through social media. Considering that emotion is a reflection of society's response, it is important to classify emotions in society to find out the community's response as information for consideration in decision-making. This study is aimed to identify and analyze the datasets, methods, and evaluation metrics that are being used in the classification of emotional texts in textual data from research data from 2013 to 2022. Based on the inclusion and exclusion design in selecting literature, a total of 50 kinds of literature were used in extracting and synthesizing data. Analysis of the data shows that out of 50 pieces of literature, there are 36 works of literature that use public datasets while 14 kinds of literature use private datasets. In the method of developing models for classifying, the SVM and Naive Bayes models are the most popular among the other models. In evaluating the model, the F-measure or F1-score metric is the most widely used metric compared to other metrics. There are three main contributions identified in this study, namely methods, models, and evaluation
Analisis Sentimen pada Teks Berbahasa Bali Menggunakan Metode Multinomial Naive Bayes dengan TF-IDF dan BoW Wibawa, Putu Widyantara Artanta; Pramartha, Cokorda Rai Adi
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 1 (2023): JNATIA Vol. 2, No. 1, November 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Many people want a fast and efficient search method as technology advances. A song search is one example of this kind of search. A song is a collection of sing-along lyrics with rhythms and melodies for many to enjoy. Due to the large number of song lovers, some people are often constrained by the title of the song to be sung. This is caused by one factor, namely only memorizing some of the lyrics of the song to be sung. Given these problems, in this study a solution was developed, namely the application of identifying song titles based on input from the user's lyrics. The algorithm used by researchers in this study is the Boyer-Moore Algorithm, which is considered better in terms of matching substrings in longer texts. The research method used includes literature study, data collection, implementation, and testing. The implementation results show that the system successfully recognizes song titles with high accuracy based on the given piece of lyrics. In conclusion, this study proves that the development of a song title identification system based on snippets of lyrics using the website-based Boyer-Moore algorithm is an effective method. This system can help users recognize song titles based on the snippets of lyrics they remember with high accuracy. Keyword: song, lyrics, boyer-moore
Systematic Literature Review: Machine Learning Methods in Emotion Classification in Textual Data Wibawa, Putu Widyantara Artanta; Pramartha, Cokorda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1787

Abstract

Emotions are a person's response to an event. Emotions can be expressed verbally or nonverbally. Over time people can express their emotions through social media. Considering that emotion is a reflection of society's response, it is important to classify emotions in society to find out the community's response as information for consideration in decision-making. This study is aimed to identify and analyze the datasets, methods, and evaluation metrics that are being used in the classification of emotional texts in textual data from research data from 2013 to 2022. Based on the inclusion and exclusion design in selecting literature, a total of 50 kinds of literature were used in extracting and synthesizing data. Analysis of the data shows that out of 50 pieces of literature, there are 36 works of literature that use public datasets while 14 kinds of literature use private datasets. In the method of developing models for classifying, the SVM and Naive Bayes models are the most popular among the other models. In evaluating the model, the F-measure or F1-score metric is the most widely used metric compared to other metrics. There are three main contributions identified in this study, namely methods, models, and evaluation
Co-Authors Cokorda Pramartha