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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Sodality: Jurnal Sosiologi Pedesaan MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah Jurnal Ilmu dan Teknologi Kelautan Tropis IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Sosial dan Humaniora Jurnal Kawistara : Jurnal Ilmiah Sosial dan Humaniora Journal of Indonesian Tourism and Development Studies JURNAL ELEKTRO Jurnal Kebijakan dan Administrasi Publik AdBispreneur PAX HUMANA ARISTO JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komunikasi Kritis Humaniora MUWAZAH: Jurnal Kajian Gender Cakrawala Jurnal Penelitian Sosial Building of Informatics, Technology and Science Jurnal Mantik Journal of Information Systems and Informatics Jurnal Studi Sosial dan Politik Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) EKONOMI, KEUANGAN, INVESTASI DAN SYARIAH (EKUITAS) Jurnal Sistem Komputer dan Informatika (JSON) JOURNAL OF BUSINESS AND ECONOMICS RESEARCH (JBE) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Cita Ekonomika: Jurnal Ilmu Ekonomi ARBITRASE: JOURNAL OF ECONOMICS AND ACCOUNTING International Journal on Social Science, Economics and Art KLIK: Kajian Ilmiah Informatika dan Komputer International Journal of Basic and Applied Science Indonesian Journal of Tourism and Leisure Jurnal InterAct Jurnal Sosiologi Engagement: Jurnal Pengabdian Kepada Masyarakat JKAP (Jurnal Kebijakan dan Administrasi Publik) Jurnal Kawistara
Yerik Afrianto Singgalen
Universitas Katolik Indonesia Atma Jaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika C.I.T. Medicom

 1 
Sentiment, toxicity, and social network analysis of virtual reality product content reviews Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.716.pp24-34

Abstract

Virtual Reality (VR) technology has garnered significant attention in recent years due to its potential to revolutionize various industries. This study aims to investigate consumer sentiments toward VR products, mainly focusing on Meta Quest 3 in the context of the AI era. The background section outlines the rising popularity of VR products and their impact on consumer behavior, emphasizing the need for a comprehensive understanding of consumer sentiments to inform marketing strategies effectively. Methodologically, the study adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to guide the analytical approach, which includes sentiment classification, toxicity scoring, and social network analysis (SNA). A dataset comprising 2,115 consumer interactions and evaluations was utilized, with 1,302 interactions for the ALINE tech video and 813 interactions for The Tech Chap video, to derive insights into sentiment patterns and interaction dynamics. The findings reveal a positive reception towards VR products, with Meta Quest 3 particularly well-received. The sentiment classification algorithm achieved an accuracy of 77.92% without SMOTE and 85.66% with SMOTE, demonstrating competency in sentiment prediction. The precision, recall, and f-measure for SVM without SMOTE were 85.78%, 99.83%, and 92.27%, respectively, while with SMOTE, they were 100%, 55.82%, and 71.50%, respectively. Toxicity scoring yielded an average toxicity score of 0.05. Social network analysis (SNA) identified a network diameter of 6, modularity of 0.6072, and a density of 0.002815, highlighting the intricate dynamics of consumer interaction within the VR domain.
Enhancing accommodation selection: an analysis of simple additive weighting and rank order centroid Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.726.pp35-44

Abstract

This study deploys Simple Additive Weighting (SAW) and Rank Order Centroid (ROC) in selecting accommodations. The research problem investigates the efficacy and applicability of these methods in aiding decision-makers, mainly tourists, in choosing accommodations based on diverse criteria. To address this issue, a comprehensive comparative analysis was conducted utilizing both SAW and ROC methodologies to evaluate a range of accommodations in the vibrant tourism destination of Raja Ampat, Indonesia. The SAW method involved the assignment of weights to various criteria and the subsequent calculation of overall scores for each accommodation. In contrast, the ROC method utilized a centroid-based approach to rank the accommodations. The findings underscore notable distinctions between the two methodologies, with SAW providing a detailed assessment of accommodations based on weighted criteria, whereas ROC offers a simplified ranking system. Additionally, the research identified Nyande Raja Ampat as the top-ranked accommodation with a score of 0.95859128, followed by Raja Ampat Sandy Guest House (score: 0.924445677) and Mambetron Homestay Raja Ampat (score: 0.861666825). Warahnus Dive Homestay and Hamueco Raja Ampat Resort secured the fourth and fifth ranks, with scores of 0.831961086 and 0.827113234, respectively. These findings offer valuable insights for tourists seeking accommodations in Raja Ampat and contribute to the broader understanding of decision-making methodologies in the tourism industry.
Toxicity, topic, and sentiment analysis on the operation of coal-fired power plants content reviews Yerik Afrianto Singgalen
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.728.pp45-57

Abstract

This research addresses the challenge of comprehensively analyzing textual data, emphasizing the prevalence of harmful language, sentiment expression, and thematic content. The research problem centers around interpreting large datasets, prompting a multifaceted methodology. Drawing upon the Cross-Industry Standard Process for Data Mining (CRISP-DM), the study follows a systematic approach involving six key phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Toxicity analysis reveals an average toxicity level ranging from 0.00404 to 0.03878 and maximum values up to 0.66151, highlighting varying degrees of harmful language prevalence. Sentiment analysis identifies that 60% of sentiments expressed are positive, 30% are neutral, and 10% are negative, elucidating prevailing attitudes. Topic modeling extracts twelve distinct themes, enriching the interpretive depth of the dataset. Performance evaluation metrics for SVM using SMOTE indicate an accuracy of 91.41% +/- 1.66%, with 832 true negatives and 689 true positives, affirming the model's reliability. Based on these findings, it is recommended that stakeholders implement robust content moderation strategies to mitigate the dissemination of harmful language, foster a safer online environment, and leverage sentiment and topic analysis insights for informed decision-making. This interdisciplinary approach enhances data analysis capabilities, providing actionable insights crucial for addressing societal challenges and advancing scholarly discourse.
Toxicity and topic analysis of travel vlog content in digital era: perspective and multilingual embedding model (voyage-multilingual-2) Singgalen, Yerik Afrianto
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.844.pp199-210

Abstract

This research investigates the complexities of online discourse by conducting a detailed toxicity and topic analysis of travel vlog content on user-generated platforms. By analyzing 1,503 posts using the Perspective API, the study finds generally low levels of toxicity, with an average toxicity score of 0.06995 and a peak of 0.78207, and similarly low average scores for severe toxicity, identity attack, insult, profanity, and threat (0.00654, 0.01237, 0.03778, 0.06241, and 0.01186, respectively). However, the highest recorded values for these measures—0.45895 for severe toxicity, 0.69287 for identity attack, 0.63084 for insult, 0.81864 for profanity, and 0.51957 for threat—highlight the sporadic presence of harmful content. Advanced clustering techniques, such as HDBScan, k-Means, and Gaussian Mixture models, enable a comprehensive examination of thematic diversity and sentiment distribution within the comments, offering valuable insights into audience engagement and perception. These findings underline the critical need for compelling content moderation and community management strategies to mitigate toxic behaviors and promote a positive digital environment. The study concludes that as digital media evolves, further research into toxicity, thematic content, and user engagement is essential for enhancing theoretical frameworks and practical applications in digital communication.
Toxicity, social network and topic analysis of digital content: Perspective and multilingual embedding model Singgalen, Yerik Afrianto
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.845.pp115-128

Abstract

This research presents a comprehensive approach to analyzing digital content by integrating toxicity analysis, clustering techniques, and Social Network Analysis (SNA) to understand online interactions better. The study finds that, while the average toxicity levels are relatively low, with scores such as 0.06355 for toxicity and 0.00468 for severe toxicity, there are significant spikes, reaching maximum scores of 0.82996 for toxicity and 0.89494 for profanity. These spikes highlight the necessity for continuous monitoring and adaptive moderation strategies to minimize the impact of harmful language. Clustering methods, including K-Means, HDBScan, and Gaussian Mixture models, provide deep insights into the thematic structure of viewer discourse, identifying both prevalent and niche topics. The Gaussian Mixture model identified ten distinct clusters, while HDBScan revealed varying cluster densities, reflecting the diverse range of discussions within the community. In addition, SNA, with 1,716 nodes and 37 edges, offers critical insights into the relational dynamics of the network, pinpointing key influencers and mapping the flow of information between different user groups. By synthesizing these methodologies, the research provides a robust framework for understanding the content and context of digital interactions, facilitating more effective strategies for enhancing community engagement, mitigating toxicity, and promoting a healthier, more inclusive online environment.
Topic modeling using LDA and performance evaluation of classification algorithm: k-NN, SVM, NBC, and DT Singgalen, Yerik Afrianto
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.846.pp143-157

Abstract

This research investigates the integration of Latent Dirichlet Allocation (LDA) for topic modeling with the performance evaluation of various classification algorithms—specifically, k-nearest Neighbors (k-NN), Support Vector Machines (SVM), Naive Bayes Classifier (NBC), and Decision Trees (DT)—within the Digital Content Reviews and Analysis Framework. The framework systematically processes and analyzes digital content, including data cleaning, extraction, evaluation, and visualization techniques, to enhance machine learning models' interpretability and predictive accuracy. The study demonstrates that combining LDA with these classification algorithms significantly improves data interpretation and model performance, particularly in handling large-scale textual datasets. Notably, the Decision Tree algorithm achieved a 98.86% accuracy post-SMOTE. At the same time, the Support Vector Machine reached a near-perfect AUC of 1.000, highlighting the efficacy of these methods in managing imbalanced datasets. The findings provide valuable insights for optimizing model selection and developing more robust and adaptive machine-learning models across various applications. This research contributes to advancing the field of artificial intelligence by proposing a comprehensive framework that effectively addresses complex data-driven challenges, encouraging further exploration of more flexible and scalable models to accommodate evolving data environments.
Co-Authors A.Y. Agung Nugroho Abigail Rosandrine Kayla Putri Rahadi Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Aprius Sutresno, Stephen Astuti Kusumawicitra Astuti Kusumawicitra Laturiuw Astuti Kusumawicitra Laturiuw Bernardus Alvin Rig Bernardus Alvin Rig Biafra Daffa Farabi Biafra Daffa Farabi Billy Macarius Sidhunata Brito, Manuel Charitas Fibriani Christanto, Henoch Juli Christine Dewi Danny Manongga Dasra, Muhamad Nur Agus Eko Sediyono Eko Widodo Elfin Saputra Elfin Saputra Elly Esra Kudubun Eugenius Kau Suni Fang, Liem Shiao Faskalis Halomoan Lichkman Manurung Gatot Sasongko Gilberto Dennis G E Sidabutar Gintu, Agung Rimayanto Gudiato, Candra Henoch Juli Christanto Henoch Juli Christanto Henoch Juli Christanto Heru Prasadja Hindriyanto Dwi Purnomo Hironimus Cornelius Royke Irene Sonbay Irwan Sembiring Jesslyn Alvina Seah Jonathan Tristan Santoso Juli Christanto, Henoch Kartikawangi, Dorien Kusumawicitra, Astuti Manuel Brito Marthen Timisela Mavish, Steven Michael Kenang Gabbatha Nantingkaseh, Alfonso Harrison Nicolas Arya Nanda Susilo Nugroho, A. Y. Agung Octa Hutapea Octa Hutapea Pamerdi Giri Wiloso Pamerdi Giri Wiloso Pamerdi Giri Wiloso, Pamerdi Giri Pedro Manuel Lamberto Buu Sada Pinia, Nyoman Agus Perdanaputra Pontolawokang, Theresya Ellen Pristiana Widyastuti Pristiana Widyastuti Purwoko, Agus Puspitarini, Titis Radyan Rahmananta Radyan Rahmananta Rafael Christian Rahadi, Abigail Rosandrine Kayla Putri Rahmadini, Asyifa Catur Richard Emmanuel Adrian Sinaga Rosdiana Sijabat Ruben William Setiawan Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno Suharsono SUHARSONO Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani