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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

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%.

Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (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.v16i1.3652

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.

Cross-Platform Mobile Based Crowdsourcing Application for Sentiment Labeling Using Gamification Method Elaine, Elaine; Putri, Farica Perdana; Suryadibrata, Alethea
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3935

Sentiment analysis is the application of natural language processing which aims to identify the sentiment of texts. To carry out sentiment analysis, data which has been labeled sentiment is needed to be included in the training model. Crowdsourcing is considered as the most optimal method to label data because it has a high level of accuracy at a relatively low cost. However, the use of crowdsourcing platforms has its own challenge, which is to increase user interest and motivation. A solution which can be applied is to design and build a crowdsourcing platform or application using the gamification method. The definition of gamification is an effort to increase one's intrinsic motivation for an activity by applying game elements to it. Therefore, a cross-platform mobile based crowdsourcing application for sentiment labeling using gamification method was carried out. The gamification design process was done based on the 6D framework and the application was developed using the Ionic-React framework. Application was examined through black box testing and the result showed that the application was functioning properly and according to the design requirements. There was also an evaluation carried out by distributing Intrinsic Motivation Inventory questionnaires to users who had used the application for 2 weeks. From a total of 40 respondents, the result showed that the level of user motivation and interest in using the application was high with a percentage of 83.10%.

Cross-Platform Mobile Based Crowdsourcing Application for Sentiment Labeling Using Gamification Method Elaine, Elaine; Putri, Farica Perdana; Suryadibrata, Alethea
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3935

Sentiment analysis is the application of natural language processing which aims to identify the sentiment of texts. To carry out sentiment analysis, data which has been labeled sentiment is needed to be included in the training model. Crowdsourcing is considered as the most optimal method to label data because it has a high level of accuracy at a relatively low cost. However, the use of crowdsourcing platforms has its own challenge, which is to increase user interest and motivation. A solution which can be applied is to design and build a crowdsourcing platform or application using the gamification method. The definition of gamification is an effort to increase one's intrinsic motivation for an activity by applying game elements to it. Therefore, a cross-platform mobile based crowdsourcing application for sentiment labeling using gamification method was carried out. The gamification design process was done based on the 6D framework and the application was developed using the Ionic-React framework. Application was examined through black box testing and the result showed that the application was functioning properly and according to the design requirements. There was also an evaluation carried out by distributing Intrinsic Motivation Inventory questionnaires to users who had used the application for 2 weeks. From a total of 40 respondents, the result showed that the level of user motivation and interest in using the application was high with a percentage of 83.10%.

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

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%.

Comparison of Fine-tuned CNN Architectures for COVID-19 Infection Diagnosis Jonathan, Jonathan; Widjaja, Moeljono; Suryadibrata, Alethea
ULTIMATICS Vol 16 No 1 (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.v16i1.3652

SARS-CoV-2 (COVID-19) virus spread quickly worldwide affects a variety of industries. The government took preventive steps to control the infection, such as diagnosing the human's lung by taking an X-Ray to see if the lungs were infected with COVID-19 or not. Using several pre-trained Convolutional Neural Network models as the basic model, this research deconstructs the comparison of fine-tuned architecture to identify which pre-trained model delivers the best outcomes in diagnosis by applying machine learning. Comparison is conducted using two scenarios that use batch sizes 64 and 32. Accuracy and f1 score are two evaluation metrics used to justify the model's good performance because the images in the real world, especially for positive classes, are scarce. According to the study, EfficientNetB0 outperforms other pre-trained models, namely ResNet50V2 and Xception, which achieved an accuracy of 0.895 and f1 score of 0.8871.

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