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All Journal Building of Informatics, Technology and Science Journal of Information Systems and Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Setiawan, Ruben William
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementation of Toxicity, Social Network, and Sentiment Classification: Alffy Rev Live in World E-sport Championship 2022 Rahadi, Abigail Rosandrine Kayla Putri; Setiawan, Ruben William; Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.5032

Abstract

This academic study investigates sentiment, toxicity, and social network dynamics within esports, focusing on the Esport World Championship 2022 featuring Alffy Rev's music performance. The research problem centers on discerning sentiment perceptions among esports enthusiasts and music fans while evaluating toxicity levels in online interactions during the event. Following the CRISP-DM methodology, the study systematically employs sentiment classification using Rapidminer, SVM with SMOTE for toxicity analysis, and Social Network Analysis (SNA). The findings reveal significant insights, including a sentiment classification accuracy of 98.73% using SVM with SMOTE, toxicity metrics such as Toxicity (0.04690) and Severe Toxicity (0.01203), alongside crucial SNA metrics like Diameter (2) and Density (0.001009). Additionally, frequently used words in the dataset include "keren" (94 occurrences), "Indonesia" (88 occurrences), "karya" (84 occurrences), and "Alffy" (59 occurrences). These findings offer valuable contributions to the esports community, informing community management strategies, event organization, and online engagement approaches. As a recommendation, deploying these analytical approaches could enhance community engagement and mitigate toxic interactions
Enhancing Website Management Through Expertise and Rapid Application Development Frameworks Widodo, Eko; Setiawan, Ruben William; Dasra, Muhamad Nur Agus; Singgalen, Yerik Afrianto
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.725

Abstract

Effective website management is crucial for organizations seeking to engage users and communicate effectively with stakeholders. This research explores the role of specialized expertise in typography, audio and visual design, copywriting, and the implementation of Rapid Application Development (RAD) frameworks in optimizing website management practices. By leveraging the skills of typography, design, and copywriting specialists, organizations create visually appealing and engaging online experiences that effectively convey messages and drive user interaction. Additionally, adopting RAD methodologies enables agile and iterative website development processes, allowing for quick prototyping, feedback integration, and rapid deployment of updates. Through synthesizing expert knowledge and RAD principles, organizations enhance their online presence, meet the evolving needs of users and stakeholders, and achieve their strategic objectives in today's dynamic digital landscape.
Sentiment and Topic Analysis of Aftermovie Piala Presiden eSports 2019 (Onic Esport) Setiawan, Ruben William; Rahadi, Abigail Rosandrine Kayla Putri; Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5036

Abstract

This research addresses the research problem of sentiment classification and topic analysis in the context of the Aftermovie Piala Presiden Esports 2019 (Onic Esport), employing the CRISP-DM methodology. Leveraging a dataset comprising 1830 posts, sentiment analysis was conducted on 191 posts using Vader and TextBlob algorithms, revealing the distribution of polarity values: 7.41% negative, 58.02% neutral, and 34.57% positive sentiments. Furthermore, the study evaluates the performance of classification algorithms, such as k-nearest Neighbors (k-NN), Support Vector Machine (SVM), and Decision Tree (DT), utilizing SMOTE. Notably, SVM demonstrated an accuracy of 90.34%, AUC of 0.995, precision of 99.91%, recall of 80.77%, and F-measure of 89.29%. Similarly, DT exhibited an accuracy of 94.83%, AUC of 0.950, precision of 92.12%, recall of 98.12%, and F-measure of 95.00%. K-NN displayed an accuracy of 92.83%, AUC of 0.972, precision of 98.01%, recall of 87.46%, and F-measure of 92.42%. The analysis also revealed frequently used words in the dataset, including 'onic' (127 occurrences), 'udil' (95 occurrences), and 'piala' (28 occurrences). This study sheds light on sentiment patterns and prevalent topics among esports enthusiasts, offering insights for content optimization and event management strategies.
Co-Authors Dasra, Muhamad Nur Agus Eko Widodo Rahadi, Abigail Rosandrine Kayla Putri Yerik Afrianto Singgalen