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Journal : Digitus : Journal of Computer Science Applications

Sentiment Analysis of Public Opinion on the 2024 Presidential Election in Indonesia Using Twitter Data with the K-NN Method Diantoro, Karno; Soderi, Ahmad; Rohman, Abdur; Sitorus, Anwar T.
Digitus : Journal of Computer Science Applications Vol. 1 No. 1 (2023): October 2023
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v1i1.27

Abstract

Twitter is often used by the public as a platform to speak and express their opinions, especially in the context of the 2024 Presidential Election. Tweets related to the '2024 Presidential Election' can be used as a source of data for social media analysis to determine whether the expressed opinions tend to be positive or negative. The research process involves data collection of tweets, preprocessing, tokenization, class attribute determination, directory filling, sentiment analysis, and classification steps, including testing the value of k and testing the confusion matrix. The research and testing results show that the K-NN method successfully achieves a sentiment classification accuracy rate of 86.48%.
Analyzing the Impact of Body Shaming on Twitter: A Study Using Naive Bayes Classifier and Machine Learning Diantoro, Karno; Rinaldo; Sitorus, Anwar T.; Rohman, Abdur
Digitus : Journal of Computer Science Applications Vol. 1 No. 1 (2023): October 2023
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v1i1.58

Abstract

Twitter is one type of social media that is still widely used today. However, it happens frequently for Twitter users to post remarks that tend to criticize other Twitter users. Twitter users routinely make nasty remarks regarding body shaming, which has detrimental impacts on the victims such as diminishing self-esteem, leading to depressive illnesses, and, more seriously, raising the chance of suicide. Body shaming is the practice of criticizing someone's physical attributes, such as being slim, overweight, or having a flat nose. This study will use the Naive Bayes Classifier approach to do sentiment analysis based on the actions of body shaming on Twitter. Based on the performance testing results of Accuracy, Precision, and Recall using Machine Learning Rapid Miner with an initial dataset of 1000 body shaming tweets and a test dataset of 329 tweets, the following results were obtained: Accuracy of 80.55%, positive Precision of 100%, negative Precision of 80.43%, positive Recall of 3.03%, and negative Recall of 100%. In the preprocessing stage, tokenization resulted in a word cloud with the top 5 words being "overweight" at 51%, "body shaming" at 20%, "thin" at 11%, "people" at 10%, and "eating" at 8%.
Development Of Android-Based Application For English Learning System Using Drill and Practice Method Rohman , Abdur; Diantoro, Karno; Rinaldo; Sitorus, Anwar T.; Juwari
Digitus : Journal of Computer Science Applications Vol. 1 No. 1 (2023): October 2023
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/digitus.v1i1.62

Abstract

English, being an international language, plays a significant part in daily life worldwide. Many nations utilize English as their primary language of communication. The lack of a novel approach to English learning—such as the use of contemporary technology—means that kindergarteners at Al-Barkah Kindergarten only learn the language through books. In light of these issues, Al-Barkah Kindergarten developed an Android-based application to teach English via the drill and practice method, hence creating a variance in teaching methods. Because this method applies a learning system in the form of visual, audio, and animation that can attract students' interest in learning language, the application design results show that the learning method is effective and that a solution, especially for Al-Barkah Kindergarten students, can easily understand the learning material. English.