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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of New Media Technology Jurnal ULTIMATICS
Putri, Farica Perdana
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Jurnal ULTIMATICS

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Sentiment Analysis on Song Lyrics for Song Popularity Prediction Using BERT Agatha, Hana; Putri, Farica Perdana; Suryadibrata, Alethea
ULTIMATICS Vol 15 No 2 (2023): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v15i2.3420

Abstract

The increasingly competitiveness in music industry is giving some musicians disadvantages. Musicians need to pay more attention to the factors that influence the popularity of a song, so their song can be popular and they can gain a lot of profit. One of the various factors that can affect a song popularity is the lyrics. The influence of the lyrics can be explored through sentiment analysis. Sentiment analysis is a computing study that identify sentiments or emotions in a text. By conducting sentiment analysis on the lyrics, song popularity can be predicted. Based on the prediction result, songwriters can evaluate their lyrics, so their song can be popular. Bidirectional Encoder Representations from Transformers (BERT) is an excellent algorithm in terms of sentiment analysis. In this study, a BERT model was developed to predict the song popularity, based on the sentiment analysis of the song lyrics. The popularity class of a song will be predicted, based on the results of lyrics sentiment analysis. The developed model is a model that has been trained with English songs. Based on the experiment, the model that used oversampling method achieved accuracy by 87%, precision by 88%, recall by 87%, and f1-score by 87%.
Improved SVM for Website Phishing Detection Through Recursive Feature Elimination Putri, Farica Perdana; Marcello, Feliciano Surya
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3744

Abstract

Technology is developing faster every day, particularly in the information technology field. A website is one of the many information access points people use to do business activities, get information, and other purposes. Sophisticated websites are being developed and used, encouraging many naive individuals to commit crimes for financial gain. Phishing websites are a common method of using information technology to conduct fraud. One way to conduct phishing is by using the features on the website. One technique for identifying phishing websites is to use the Support Vector Machine (SVM) algorithm, which classifies websites based on features. However, the SVM algorithm is not able to detect many features so that the resulting accuracy and optimization level is also not good. Based on datasets, the SVM algorithm only gets around 60% to 70% accuracy. The use of Recursive Feature Elimination (RFE) feature selection is one way that can be done to cover the shortcomings of SVM. By eliminating features that irrelevant and redundance, RFE makes the SVM algorithm get a higher accuracy rate on the available dataset with an accuracy of 96.09%.
Sentiment Analysis on Song Lyrics for Song Popularity Prediction Using BERT Agatha, Hana; Putri, Farica Perdana; Suryadibrata, Alethea
ULTIMATICS Vol 15 No 2 (2023): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v15i2.3420

Abstract

The increasingly competitiveness in music industry is giving some musicians disadvantages. Musicians need to pay more attention to the factors that influence the popularity of a song, so their song can be popular and they can gain a lot of profit. One of the various factors that can affect a song popularity is the lyrics. The influence of the lyrics can be explored through sentiment analysis. Sentiment analysis is a computing study that identify sentiments or emotions in a text. By conducting sentiment analysis on the lyrics, song popularity can be predicted. Based on the prediction result, songwriters can evaluate their lyrics, so their song can be popular. Bidirectional Encoder Representations from Transformers (BERT) is an excellent algorithm in terms of sentiment analysis. In this study, a BERT model was developed to predict the song popularity, based on the sentiment analysis of the song lyrics. The popularity class of a song will be predicted, based on the results of lyrics sentiment analysis. The developed model is a model that has been trained with English songs. Based on the experiment, the model that used oversampling method achieved accuracy by 87%, precision by 88%, recall by 87%, and f1-score by 87%.
Improved SVM for Website Phishing Detection Through Recursive Feature Elimination Putri, Farica Perdana; Marcello, Feliciano Surya
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3744

Abstract

Technology is developing faster every day, particularly in the information technology field. A website is one of the many information access points people use to do business activities, get information, and other purposes. Sophisticated websites are being developed and used, encouraging many naive individuals to commit crimes for financial gain. Phishing websites are a common method of using information technology to conduct fraud. One way to conduct phishing is by using the features on the website. One technique for identifying phishing websites is to use the Support Vector Machine (SVM) algorithm, which classifies websites based on features. However, the SVM algorithm is not able to detect many features so that the resulting accuracy and optimization level is also not good. Based on datasets, the SVM algorithm only gets around 60% to 70% accuracy. The use of Recursive Feature Elimination (RFE) feature selection is one way that can be done to cover the shortcomings of SVM. By eliminating features that irrelevant and redundance, RFE makes the SVM algorithm get a higher accuracy rate on the available dataset with an accuracy of 96.09%.
Co-Authors Agatha, Hana Elaine, Elaine Marcello, Feliciano Surya Supriyatna, Bryan Leonardo Suryadibrata, Alethea