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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 : Building of Informatics, Technology and Science

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Implementation of the GloVe in Topic Analysis based on Vader and TextBlob Sentiment Classification 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.v5i4.5033

Abstract

This research investigates public sentiment towards tourism and gastronomy content through sentiment classification methodologies, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. Leveraging sentiment analysis techniques, including Vader and TextBlob, the study analyzes a dataset of textual content related to tourism and gastronomy to discern prevailing sentiment distributions. The findings reveal a predominant prevalence of positive sentiments (72.19%), followed by neutral (23.33%) and negative sentiments (4.48%). These results shed light on the overall sentiment dynamics surrounding tourism and gastronomy content, indicating a predominantly positive reception among users. The study contributes to the body of knowledge in sentiment analysis research, particularly within tourism and gastronomy studies, offering valuable insights into user perceptions and attitudes. Such findings have implications for content creators, marketers, and policymakers seeking to enhance tourism and gastronomy experiences. Future research could delve deeper into the factors influencing sentiment expressions and explore strategies to leverage positive sentiments for promoting and advancing tourism and gastronomy endeavors within the CRISP-DM framework.
Implementation of Sentiment Classification using k-NN, SVM, and DT for the MukaRakat Official Music Video (IDR and Toki Sloki) 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.v5i4.5044

Abstract

This study presents a comprehensive analysis of sentiment classification algorithms applied to content from the entertainment industry, specifically focusing on hip-hop music videos. Following the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, the research evaluates the performance of three prominent algorithms: k-nearest Neighbors (k-NN), Decision Tree (DT), and Support Vector Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE). The analysis incorporates performance metrics, including accuracy, precision, recall, f-measure, and the area under the curve (AUC) values. The dataset comprises user-generated comments and feedback from two distinct hip-hop music videos. Results indicate that all three algorithms exhibit notable accuracy in classifying sentiments, with SVM with SMOTE achieving the highest accuracy of 83.68%. DT demonstrates balanced performance metrics, particularly in precision and recall, with an accuracy of 79.12%. Meanwhile, k-NN exhibits a lower accuracy of 64.71% but showcases balanced precision and recall rates. These findings suggest the suitability of SVM with SMOTE for sentiment classification tasks in the entertainment industry, offering valuable insights for content creators, marketers, and platform administrators to enhance audience engagement and user experience. Additionally, the study underscores the importance of algorithmic evaluation and selection in content analysis, providing guidance for future research and practical applications in the entertainment domain within the framework of CRISP-DM.
Implementation of Toxicity, Sentiment, and Social Network Analysis (Epic Rap Battles of Presidency 2024) 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.v5i4.5046

Abstract

This research delves into the complex realm of digital political communication, employing a comprehensive approach that integrates toxicity analysis, sentiment classification, and social network analysis within the framework of the CRISP-DM methodology. The study illuminates the multifaceted nature of online discourse through meticulous examination, elucidating the coexistence of harmful content, diverse sentiments, and intricate network structures. Leveraging VADER and TextBlob algorithms, toxicity and sentiment distribution patterns are meticulously identified, with metrics such as Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, and Threat presenting distinct numerical values. For instance, Toxicity measures at 0.09275 with a severe threshold of 0.98622, while sentiment analysis reveals varying proportions of negative, neutral, and positive sentiments across English, French, and German content. Specifically, VADER sentiment analysis for English content shows 25.38% classified as unfavorable, 41.13% as neutral, and 33.49% as positive sentiments, while TextBlob sentiment analysis for English content displays 8.59% negative, 64.12% neutral, and 27.29% positive sentiments. Similarly, TextBlob sentiment analysis for French content indicates 1.75% negative, 96.49% neutral, and 1.75% positive sentiments, and for German content, it illustrates 2.00% negative, 96.52% neutral, and 1.48% positive sentiments. These findings provide crucial insights into public sentiment, information dissemination, and community formation within online political discourse. The implications of this research extend to policymakers, electoral candidates, and digital platform developers, offering evidence-based strategies to cultivate healthier online environments and promote informed civic engagement. Further investigation is warranted to explore emerging trends and adapt analytical frameworks to the evolving landscape of digital communication. Ultimately, this study advances our understanding of digital political communication and underscores the necessity of interdisciplinary approaches in addressing contemporary socio-political challenges in the digital era.
Tourism and Travel Content Analysis for Market Segmentation using Toxicity and Sentiment Classification in Communalytic Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5294

Abstract

This research highlights the significant impact of digital content on shaping tourist perceptions and behaviors, particularly emphasizing the influence of travel vlogs. Utilizing the Tourism and Travel Content Analysis (TTCA) framework, the study analyzed 1,972 review posts out of 2,250, revealing critical insights into viewer engagement and sentiment. Toxicity score calculations indicated prevalent negative interactions, with scores ranging from 0.05542 to 0.86967 for Toxicity, 0.00536 to 0.50704 for Severe Toxicity, 0.01921 to 0.59834 for Identity Attack, 0.03305 to 0.76573 for Insult, 0.03737 to 0.78492 for Profanity, and 0.01075 to 0.48617 for Threat, underscoring the need for compelling content moderation. Sentiment analysis using VADER and TextBlob demonstrated a generally positive reception of travel vlogs, with VADER classifying 3.73% of posts as unfavorable, 19.83% as neutral, and 76.44% as positive. In comparison, TextBlob classified 2.71% of posts as unfavorable, 35.59% as neutral, and 61.69% as positive for English posts. Notably, VADER and TextBlob agreed on sentiment classification for 446 out of 587 posts (75.98%), with a Cohen’s kappa statistic of 0.471, indicating moderate agreement. These findings suggest that well-regulated and thoughtfully designed digital content significantly enhances user engagement and optimizes destination marketing strategies. Future research should incorporate advanced analytical tools and comprehensive data sets to refine these insights further, supporting the development of more targeted and effective marketing efforts in the tourism sector. This study thus contributes to a deeper understanding of digital media's impact on tourism marketing, offering practical recommendations for leveraging content to foster positive and engaging tourist experiences
Travel Content Evaluation through Sentiment and Toxicity Analysis using CRISP-DM Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5397

Abstract

This research, framed by the CRISP-DM methodology, offers a comprehensive analysis of sentiment and toxicity in digital content, focusing on tourism-related videos. Utilizing advanced machine learning models like VADER and TextBlob for sentiment analysis, as well as APIs such as Detoxify and Perspective for toxicity assessment, the study analyzed 25,361 posts, with 23,292 processed for sentiment and 24,171 for toxicity. Various algorithms, including k-NN, DT, NBC, and SVM, were applied with SMOTE to address data imbalance. The SVM algorithm achieved the highest performance with an accuracy of 54.80% and an F-measure of 66.01%, while others showed lower efficacy. The deployment phase integrated these models for real-time analysis, providing actionable insights into user engagement. Findings emphasize the significant impact of sentiments on brand perception and the necessity of managing toxic behavior for a healthier online environment. Despite limitations such as dataset imbalance and model dependency, the study offers valuable recommendations for content creators, advocating for robust moderation and sentiment-based strategies to enhance user interaction. Future research should include diverse datasets and advanced tools to improve the findings' robustness and applicability. This research contributes to understanding digital content dynamics and provides strategic insights for optimizing content creation and user engagement.
Sentiment and Toxicity Analysis of Tourism-Related Video through Vader, Textblob, and Perspective Model in Communalytic Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5416

Abstract

This study leverages the Tourism and Travel Content Analysis (TTCA) framework to explore user sentiment and behavior in response to digital travel content. Utilizing sentiment analysis models such as VADER and TextBlob, the research analyzed 13,162 posts, revealing that 13.92% were negative, 15.02% neutral, and 71.06% positive, according to VADER. At the same time, TextBlob classified 10.47% as unfavorable, 26.51% as neutral, and 63.02% as positive. Additionally, toxicity scores calculated using Detoxify and Perspective models showed a range from low to high levels of toxic content, highlighting issues like identity attacks, insults, profanity, and threats. The findings underscore the effectiveness of well-crafted narratives in digital content for influencing tourist behavior and visit intentions. However, limitations were noted in the model's ability to fully capture emotional and cultural nuances. Future research should incorporate more advanced analytical tools and diverse datasets to overcome these limitations. Ultimately, the TTCA framework provides valuable insights for enhancing digital marketing strategies and improving user engagement in the tourism secto
The Role of Sentiment and Toxicity in Digital Narratives Surrounding Sulawesi's Wildlife Tourism: A Content Analysis for Enhancing Conservation Strategies Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5821

Abstract

This research explores the intersection of wildlife tourism and digital narratives, focusing on Sulawesi's endemic species. Utilizing the Digital Content Reviews and Analysis framework, the study combines content analysis, sentiment classification, and toxicity assessment to uncover critical insights. The findings highlight digital narratives' significant role in shaping public perceptions and behaviors toward conservation and ecotourism. Through systematic content analysis, themes such as biodiversity, conservation, and local community involvement emerged as effectively communicated, resonating with audiences and promoting sustainable tourism practices. The framework's structured approach enabled a thorough examination of digital content's impact on wildlife tourism narratives, identifying critical patterns and themes. The study also employed advanced machine learning techniques, specifically the SVM algorithm enhanced by SMOTE, which achieved a sentiment classification accuracy of 88.76% ± 3.11% and an AUC of 0.977, demonstrating its effectiveness. However, toxicity assessment revealed that while most interactions were civil, specific posts contained significant levels of toxicity, with a peak score of 0.64912, underscoring the need for better moderation and engagement strategies. The research emphasizes integrating conservation-focused elements into digital narratives to foster positive engagement and support for wildlife preservation. The study provides practical recommendations for enhancing the positive influence of digital narratives on conservation and sustainable tourism, offering a foundation for future initiatives to optimize digital communication strategies in ecotourism
Exploring Toxicity and Sentiment in Cultural Heritage Documentation: Content Analysis of Sabu Island's Portrayal in KOMPASTV's Expedition Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5838

Abstract

This study explores the dual role of media in preserving and potentially distorting cultural heritage, focusing on the portrayal of Sabu Island in KOMPASTV's expedition documentary. Utilizing the Digital Content Reviews and Analysis Framework, the research comprehensively dissection the documentary’s content, uncovering critical insights into the intricate relationship between tourism, cultural preservation, and media representation. By integrating sentiment and toxicity analysis, the study identifies the emotional tone and harmful language present within digital narratives, with the toxicity analysis revealing an average score of 0.09886 and a peak score of 0.83647, indicating the potential influence of negative discourse on cultural heritage. The sentiment classification, conducted through a Support Vector Machine (SVM) model enhanced by SMOTE, demonstrated robust performance metrics, including an accuracy of 66.43%, precision of 60.51%, recall of 94.98%, and an F-measure of 73.90%, with an AUC ranging from 0.728 to 0.904. Additionally, content analysis centered on key themes such as Economic Impact, Sacred Rituals, Tourist Experience, and Weaving Traditions, revealing the complex dynamics where cultural preservation must be balanced with economic development and tourism demands. The findings emphasize the need for responsible and authentic media portrayals to safeguard cultural identities, as media holds the power to uphold or undermine cultural narratives' integrity. This research contributes to the broader discourse on cultural heritage documentation by offering a comprehensive framework for evaluating the impact of digital narratives on the preservation of cultural identities, ensuring the accurate and respectful portrayal of cultural heritage.
An Analysis of User Engagement in the Reviews of The Guardian of Nusantara Official Music Video: Toxicity and Sentiment Analysis Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5846

Abstract

This study investigates user engagement within digital environments, explicitly focusing on creative content like music videos, and examines how sentiment and toxicity levels in user interactions influence engagement dynamics. Employing the Digital Content Reviews and Analysis Framework, the study reveals that 95.8% of user interactions exhibit positive or neutral sentiments. In comparison, a notable 4.2% are toxic, reflecting underlying societal tensions and potentially perpetuating negative feedback loops. Analysis of 23,112 posts using the Perspective API shows an average toxicity score of 0.03972, with severe cases reaching up to 0.87787. Scores for severe toxicity, identity attacks, insults, profanity, and threats, although generally low, indicate maximum values of concern, highlighting the need for vigilant monitoring. Sentiment classification results using the VADER model and multiple algorithms demonstrate that the Support Vector Machine (SVM) model achieved the highest accuracy (68.74%) and Area Under Curve (AUC) score (0.686), outperforming other models in distinguishing sentiment. The study's discussion on user engagement suggests that high levels of participation, such as comments, likes, and shares, are indicators of user interest and community identity but are susceptible to being undermined by toxic interactions. These findings emphasize the importance of fostering positive engagement through effective moderation strategies and advanced sentiment analysis tools, ensuring digital platforms remain conducive to constructive dialogue and community building. The research underscores the necessity for sophisticated analytical approaches to navigate the complexities of user behavior in digital spaces, providing critical insights into the interplay between sentiment, engagement, and toxicity in shaping online communities.
A Hybrid CNN-LSTM Model with SMOTE for Enhanced Sentiment Analysis of Hotel Reviews Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6301

Abstract

The growing reliance on online reviews as a critical decision-making tool in the hospitality industry underscores the need for robust sentiment analysis methodologies. Understanding customer feedback is essential for hotels to enhance service quality and maintain a competitive edge in an increasingly digital marketplace. However, traditional sentiment analysis models often encounter difficulties processing unstructured textual data, particularly when faced with class imbalances where positive reviews dominate, overshadowing critical negative feedback. To address these challenges, this study investigates integrating a hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) model with the Synthetic Minority Over-sampling Technique (SMOTE) to improve sentiment classification accuracy. Utilizing a dataset of 665 reviews from THE 1O1 Bandung Dago Hotel, the model leverages CNN’s capability to capture local features and LSTM’s strength in handling sequential dependencies, resulting in a more nuanced analysis of customer sentiments. The application of SMOTE effectively balances the dataset, addressing the class imbalance issue, which often skews sentiment classification. This approach improves predictive accuracy and provides actionable insights to enhance customer satisfaction strategies. The study achieved an overall classification accuracy of 77%, with precision at 78%, recall at 77%, an F1 score of 77.5%, and an AUC score of 0.81, reflecting discriminatory solid capability. Future research could focus on model optimization, multilingual sentiment analysis, aspect-based sentiment insights, and real-time sentiment monitoring to further refine customer feedback analysis and support strategic decision-making in the hospitality sector.
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