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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 Perkotaan 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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Sentiment and Toxicity Analysis of Digital Content Using Perspective, Vader, and TextBlob: Tourism and Birdwatching Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2091

Abstract

This research investigates the impact of digital content on specialized tourism activities, focusing on birdwatching, using tools such as Communalytic and RapidMiner. By analyzing 1,021 posts, the study reveals an average toxicity score of 0.13839, with VADER identifying 32.78% negative sentiment and TextBlob identifying 17.07% negative sentiment. Despite these negative interactions, over 50% of the posts convey positive sentiment, highlighting the potential for digital content to foster a supportive and engaging community. The findings underscore the urgent need to address toxicity to maintain a positive online environment, crucial for enhancing educational outreach and participant engagement. This research emphasizes the critical and immediate role of digital platforms, analyzed through Communalytic and RapidMiner, in promoting environmental awareness and conservation, thereby driving the growth and sustainability of niche tourism sectors such as birdwatching. Prompt action is essential to leverage these insights to benefit the environment and local economies.
Implementation of Global Vectors for Word Representation (GloVe) Model and Social Network Analysis through Wonderland Indonesia Content Reviews Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7569

Abstract

Integrating the Global Vectors for Word Representation (GloVe) Model with Social Network Analysis presents a promising approach for extracting nuanced semantic relationships from Wonderland Indonesia's content reviews. However, the lack of comprehensive studies exploring the effectiveness of this integration, specifically within the context of Wonderland Indonesia's content reviews, necessitates focused research to uncover its potential impact and applications. This study investigates the reception and impact of the "Wonderland Indonesia" video content by Alffy Rev ft. Novia Bachmid (Chapter 1) within the YouTube community using a comprehensive methodology based on CRoss-Industry Standard Process for Data Mining (CRISP-DM), topic analysis, and Social Network Analysis (SNA). Through topic analysis, the content's main themes and narrative elements were identified, shedding light on its storytelling effectiveness. Furthermore, sentiment analysis using Vader was conducted on 2204 out of 24185 posts, revealing that 1369 (92%) exhibited positive sentiment, 427 (31.19%) had neutral sentiment, and 850 (62.09%) contained negative sentiment. Additionally, sentiment analysis using TextBlob was performed on the same subset of posts, with 1369 (40) posts exhibiting positive sentiment, 599 (43.75%) with neutral sentiment, and 730 (53.32%) expressing negative sentiment. Notably, metrics such as toxicity (highest value: 0.90780) and severe toxicity (highest value: 0.95021) exhibited varying prominence within the analyzed content. These findings enable targeted interventions and content moderation strategies to promote healthier online discourse. The SNA uncovered intricate social dynamics and interaction patterns among viewers, emphasizing the video's ability to foster engagement and community interaction. This study underscores the significance of creative storytelling and community engagement strategies in digital content creation, with implications for audience participation and community development within the digital sphere. Future research could explore the longitudinal effects of such content strategies on audience retention and community engagement.
Sentiment Classification of Food Influencer Content Reviews using Support Vector Machine Model through CRISP-DM Framework Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7509

Abstract

The research problem revolves around the challenges in effectively marketing culinary tourism aligned with tourist preferences in Indonesia, necessitating a substantial exploration of consumer sentiments related to culinary diversity through the lens of food influencer content. Food influencers are crucial in stimulating tourists' interest in gastronomy through culinary tourism in Indonesia. This research reveals challenges in culinary tourism marketing aligned with tourist preferences, necessitating substantial exploration of consumer sentiments related to culinary diversity through food influencer content. The sentiment classification method employed is the Cross-Industry Standard Process for Data Mining (CRISP-DM) using the Support Vector Machine (SVM) algorithm and the SMOTE operator. The data source is derived from a video with the ID PMhfLy_buV8, containing 114,422 comments. This study collects and processes 30,000 comments, resulting in 9,323 data points. The findings highlight the vital performance metrics of SVM models, both with and without SMOTE, showcasing high accuracy, precision, recall, and F-measure values. Specifically, SVM without SMOTE achieves 95.28% accuracy, while SVM with SMOTE achieves 98.67%. Despite some limitations in discerning positive and negative sentiments, indicated by moderate Area Under the Curve (AUC) values (0.608 to 0.658), the overall efficacy of SVM in sentiment analysis for food influencer content is apparent. Drawing from a dataset of 30,000 comments, these insights contribute to advancing sentiment analysis methodologies and offer practical implications for understanding consumer perceptions and behaviors in digital media and influencer marketing. Additionally, the prominence of frequent words such as "bang" (1322), "nonton" (1064), "makan" (921), "yang" (801), "puasa" (711), "tahun" (484), "ngiler" (448), "lagi" (384), "tanboy" (311), and "enak" (315), as extracted from RapidMiner analysis, underscores the significance of language patterns in the realm of food influencer content.
Toxicity Analysis and Sentiment Classification of Wonderland Indonesia by Alffy Rev using Support Vector Machine Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7563

Abstract

The music industry's increasing reliance on digital platforms like YouTube for dissemination raises concerns about the potential impact of music videos on viewer sentiment and well-being. This study seeks to assess the toxicity and sentiment of the Wonderland Indonesia music video by Alffy Rev through Support Vector Machine analysis, contributing to our understanding of the effects of music content on online audiences. This research addresses the challenge of sentiment classification in digital content by leveraging the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The study aims to enhance sentiment classification accuracy by applying a Support Vector Machine (SVM) with a Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues. The research problem revolves around the need for robust sentiment analysis models capable of accurately discerning sentiment polarity within diverse datasets. Through the systematic application of CRISP-DM phases - business understanding, data understanding, data preparation, modeling, evaluation, and deployment - the study examines the efficacy of SVM with SMOTE in sentiment classification tasks. The findings demonstrate notable performance metrics, including accuracy (96.50%), precision (95.75%), recall (99.00%), and F-measure (97.34%). The AUC value substantially increases from 0.642 without SMOTE to 0.997 with SMOTE, highlighting its effectiveness in improving sentiment classification accuracy. In addition, The comparative analysis of toxicity values between the first and second videos demonstrates distinct patterns: the first video showcases a Toxicity score of 0.05290, with notable metrics such as Profanity registering at 0.04815. Conversely, the second video exhibits a slightly lower Toxicity score of 0.04744, with varying metrics such as Severe Toxicity at 0.01386.
Spatio-temporal Analysis through NDVI, NDBI, and SAVI Using Landsat 8/9 OLI Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5735

Abstract

This research underscores the significant role of remote sensing and spatio-temporal analysis in promoting sustainable tourism development on Kakara Island, North Halmahera. Applying NDVI, NDBI, and SAVI models provided valuable insights into vegetation health, urban expansion, and soil-adjusted indices from 2013 to 2024. NDBI values in 2013, 2018, and 2024 revealed changes in urban development with minimum values of -0.8837597, -0.8867515, and -0.7182528, respectively. NDVI values indicated improvements in vegetation health, with mid values increasing from 0.3804683 in 2013 to 0.8090699 in 2024. Similarly, SAVI values demonstrated better vegetation density, with maximum values rising from 0.3782764 in 2013 to 0.6022941 in 2024. These models effectively monitored environmental changes and informed sustainable land management practices. As tourism on Kakara Island grows, with visitor numbers increasing by 25% annually, a balanced approach is essential to preserve its natural and cultural heritage. Integrating remote sensing and spatio-temporal analysis is crucial for identifying areas under environmental stress and implementing sustainable practices to mitigate negative impacts. Future research should include additional models, such as the Enhanced Vegetation Index (EVI) and Normalized Burn Ratio (NBR), and integrate socio-economic data with environmental datasets for a more comprehensive understanding. This approach will foster sustainable development that benefits both the environment and the local community, ensuring the long-term resilience and viability of Kakara Island's tourism industry.
Spatial Data Processing for Mangrove Ecotourism Development: Spatio-temporal Analysis through NDVI, NDBI, and SAVI Using Landsat 8/9 OLI Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5740

Abstract

This study evaluates the ecological trends on Tagalaya Island by analyzing the NDBI, NDVI, and SAVI indices from 2013 to 2024. The NDBI data reveals a notable improvement in vegetation conditions over this period. In 2013, NDBI values ranged from -0.8818104 to -0.3152868, indicating poor vegetation health. Although there was a slight deterioration by 2018, with values ranging from -0.8922318 to -0.2858251, a significant recovery was observed by 2024, with values ranging from -0.7118425 to 0.027627. NDVI values also demonstrate positive changes, with 2013 values ranging from -0.340193 to 0.4773595 and increasing substantially by 2024 to a range of -0.2155555 to 0.9997522, reflecting enhanced vegetation coverage and health. Similarly, SAVI values show improvement, increasing from -0.1651871 to 0.3954751 in 2013 to -0.0731807 to 0.6464996 in 2024. These trends suggest that Tagalaya Island has experienced successful ecological recovery or effective conservation measures. Continued monitoring is essential to sustain and further these positive developments, ensuring ongoing environmental stability and health.
Enhancing Tourism Digital Content Engagement through Sentiment and Toxicity Analysis: Application of Perspective, Vader, and TextBlob Models Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5757

Abstract

This research examines the engagement with tourism digital content for Sumba Island through sentiment and toxicity analysis. The study uses advanced models such as Perspective, Vader, and TextBlob to reveal an average toxicity score of 0.04066, indicating minimal harmful language. Sentiment classification shows a predominantly positive reception, with VADER identifying 81.69% positive, 12.96% neutral, and 5.35% negative sentiments. TextBlob analysis supports these findings, confirming the robustness of the sentiment evaluation. The research underscores the effectiveness of well-crafted digital content in promoting positive user engagement while maintaining low toxicity. The urgency of this research is emphasized by the increasing reliance on digital platforms for tourism marketing, where understanding audience perception is crucial for effective strategy development. The study employs the Digital Content Reviews and Analysis Framework, which ensures systematic data processing and comprehensive evaluation. This framework includes data cleansing, sentiment, toxicity scoring, and rigorous evaluation using multiple analytical models to enhance the reliability and applicability of the findings. Future recommendations include expanding the analysis to encompass visual content and non-English comments and incorporating advanced multimodal techniques to capture a holistic view of digital content engagement. Addressing these areas will further enrich the understanding and impact of tourism digital content, driving more effective and engaging marketing strategies in the competitive digital landscape.
Prototype Design for Education and Heritage Tourism through Rapid Application Development Pinia, Nyoman Agus Perdanaputra; Gintu, Agung Rimayanto; Wabiser, Yan Dirk; Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 4 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i4.5776

Abstract

This research explores the development of a prototype for the Sangiran Information System, utilizing the Rapid Application Development (RAD) framework to meet the specific needs of researchers, destination managers, and tourists. The study emphasizes the importance of user-centric design, facilitated by iterative refinement, which ensures the system effectively supports data management and access related to the Sangiran heritage site. The coding results from content analysis were instrumental in shaping the system, particularly in digital technology integration, educational roles, museum management, and tourism impact. Despite these advancements, the research identifies a critical limitation: the lack of integration with the museum's internal systems and databases. This gap highlights the necessity for further development to achieve a more cohesive and comprehensive information system. The findings underscore the significant progress made in enhancing the educational and management functions of the Sangiran site while also pointing to the need for ongoing improvements to fully support heritage preservation and tourism objectives.
Inclusive Digital Narratives: Analyzing Toxicity, Topics, and Social Networks in Indigenous Documentaries Content Reviews Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 14 No. 2 (2024): August: Social Science, And Economics
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The research delves into the livelihood and coping strategies of indigenous communities in the digital era, focusing on the analysis of digital content. Utilizing the CRISP-DM framework, the study investigates toxicity scores, topics, and social networks within digital content, particularly examining video documentaries portraying indigenous communities' ways of life. Through data understanding, scraping, and modeling, the research unveils insights into the toxicity levels of online discussions and identifies topics resonating with viewers. The findings underscore the significance of preserving indigenous cultures, promoting community well-being, and fostering inclusive digital content. Moreover, the analysis reveals 8,776 actors and 498 edges within social networks, with an average degree of connectivity of 0.051 and an average weighted degree of 0.057. Notably, the toxicity analysis shows relatively low toxicity levels, with a toxicity score of 0.04992 and severe toxicity at 0.00609. The study concludes by recommending strategies to enhance the quality and sensitivity of digital content, contributing to broader societal understanding and appreciation of indigenous communities.
Investigating Viewer Engagement Dynamics Through Toxicity and Social Network Analysis in the Muara Enggelam Documentary Video Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 14 No. 2 (2024): August: Social Science, And Economics
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijosea.v14i2.473

Abstract

his study delves into the dynamics of viewer engagement and sentiment surrounding the Muara Enggelam documentary video, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology across six distinct stages. Rooted in the imperative of comprehending audience perceptions and interactions within digital media contexts, particularly in exploring community livelihood and socio-economic dynamics depicted in documentary content, the research traverses through stages encompassing Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Leveraging toxicity analysis utilizing the Perspective API, the study reveals profound insights into the prevalence of negative language within comments, unveiling a low overall toxicity score of 0.04307, with nuanced breakdowns across categories such as Severe Toxicity (0.00476), Identity Attack (0.00978), Insult (0.02652), Profanity (0.03234), and Threat (0.01468). Additionally, Social Network Analysis (SNA) unveils the intricate network structure and interaction patterns among viewers, identifying 1754 nodes and 126 edges in the directed graph, with Modularity at 0.376, Number of Communities at 1653, and an Average Path length of 1.9559594547361063. This analysis highlights the significant role of content creators, exemplified by Kacong Explorer, in shaping discourse and fostering community engagement.
Co-Authors A.Y. Agung Nugroho Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel 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 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 Heru Prasadja Heru Prasadja, Heru 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 Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno, Stephen Aprius Suharsono SUHARSONO Suni, Eugenius Kau Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani