Article Per Year (5 Year)
p-Index From 2021 - 2026
24.323
P-Index
This Author published in this journals
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 Muwazah: Jurnal Kajian Gender
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

Published : 235 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 235 Documents
Search

 12   13   14   15   16 
Sentiment and Toxicity Analysis in the Narratives of Wamena's Cultural Heritage: Understanding Community Perspectives and External Influences Yan Dirk Wabiser; Yerik Afrianto Singgalen
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.v5i1.1941

Abstract

This study analyzes digital narratives surrounding Wamena's cultural heritage using the Digital Content Reviews and Analysis Framework, focusing on sentiment, toxicity, and thematic content. The research explores the complex interplay between community perspectives, cultural preservation, modernization, and external influences such as tourism. Toxicity analysis revealed that while most online discourse is supportive, there are instances of harmful language that could disrupt social cohesion, with toxicity scores peaking at 0.50790. These findings highlight the need for continuous moderation to foster a positive digital environment. Sentiment analysis provided a deeper understanding of emotional tones, showing a predominance of positive sentiments and highlighting frustration and dissent related to cultural erosion. The study employed machine learning algorithms for sentiment and toxicity classification, with the Support Vector Machine (SVM) enhanced by Synthetic Minority Over-sampling Technique (SMOTE) demonstrating superior accuracy at 87.29%. Content analysis identified vital themes such as community dynamics, cultural resilience, and the dual impact of tourism as both an economic catalyst and a potential threat to cultural integrity. The findings underscore the importance of maintaining an inclusive digital environment that promotes constructive dialogue and cultural preservation. This framework provides valuable insights for policymakers and community leaders, emphasizing the need for culturally sensitive strategies to manage digital content and support sustainable cultural tourism. Future research should expand this framework to other contexts to enhance the understanding of digital communication dynamics in diverse cultural settings.
Sentiment and Toxicity Score Evaluation of DJI Avata Product Reviews Using Cross-Industry Standard Process for Data Mining Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1946

Abstract

This research employs the CRISP-DM framework to analyze consumer sentiment and preferences regarding DJI Avata drone products, aiming to provide data-driven strategic recommendations for marketing and product development. By systematically exploring business objectives, preparing and cleaning data, and modeling sentiment, the study reveals high consumer engagement and predominantly positive sentiment (51.91% positive, 31.16% neutral, 16.93% negative) towards the DJI Avata. The Support Vector Machine (SVM) algorithm demonstrated superior performance in sentiment classification, achieving an accuracy of 74.69%, with an AUC of 0.839, precision of 77.57%, recall of 69.68%, and F-measure of 73.23%. A comparative analysis between the VADER and TextBlob models, showing a moderate agreement (Cohen’s kappa statistic = 0.413) on 64.84% of the posts, highlighted the value of using multiple sentiment analysis tools. Furthermore, toxicity scores calculated via the Perspective API identified critical areas for improvement in user engagement. Subsequently, the toxicity results reveal the following scores: Toxicity with an average of 0.09461 and a peak of 0.90451, Severe Toxicity with an average of 0.00817 and a peak of 0.45895, Identity Attack with an average of 0.01139 and a peak of 0.58743, Insult with an average of 0.04543 and a peak of 0.70658, Profanity with an average of 0.06133 and a peak of 0.89080, and Threat with an average of 0.02063 and a peak of 0.69437. These detailed metrics provide a comprehensive understanding of the dataset's different dimensions and intensities of negative sentiments. The significant variation between average and peak values indicates the presence of highly negative interactions, which necessitates targeted intervention. Consequently, these findings inform the development of specific strategies to mitigate toxicity and enhance the overall user experience in digital communities. These insights informed strategic recommendations to enhance digital marketing efforts and product features, underscoring the CRISP-DM framework's efficacy in guiding comprehensive consumer sentiment analysis and fostering informed decision-making in the aerial photography and videography market.
Understanding Digital Engagement through Sentiment Analysis of Tourism Destination through Travel Vlog Reviews Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1947

Abstract

This research employs the CRISP-DM framework to analyze digital engagement through travel vlog content, explicitly focusing on vlogs about Gili Trawangan. The study systematically follows the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Utilizing the VADER sentiment analysis model and the SVM algorithm with SMOTE, the research achieves a high level of accuracy in sentiment classification, with the SVM model demonstrating an accuracy of 88.57% +/- 5.11% and a precision of 90.95% +/- 5.09%. Analysis of 442 cleaned and labeled data points reveals a strong dominance of positive sentiments, with 62.61% in the first video and 84.25% in the second video. These findings underscore the effectiveness of travel vlogs in engaging viewers and generating positive interactions as powerful tools for tourism marketing. The study concludes that the CRISP-DM framework is highly effective in facilitating comprehensive sentiment analysis and enhancing strategic tourism marketing initiatives.
Understanding Tourism Destination through Music: Digital Engagement Discourse Based on Sentiment Analysis Approach Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1950

Abstract

This research investigates the effectiveness of music-video content as a tool for tourism destination marketing, employing the CRISP-DM framework to approach data collection, analysis, and interpretation systematically. Focusing on the music video "Welcome to Sumba Island" by Marapu Reggae Official, the study analyzes public sentiment and toxicity scores to gauge audience engagement. The findings reveal a predominance of positive sentiments and minimal toxicity, with scores such as 0.02117 for general toxicity and 0.00189 for severe toxicity, indicating a respectful and appreciative audience. The Decision Tree (DT) algorithm, enhanced by the SMOTE operator, demonstrated superior performance in sentiment classification, achieving an accuracy of 95.50% and an AUC of 0.979. While the study's focus on a single music genre and location limits generalizability, it highlights the potential of music videos in tourism marketing. Future research should expand to diverse music genres and destinations and integrate mixed-method approaches for deeper insights. The CRISP-DM framework's effectiveness in this study underscores its value in guiding sentiment analysis and developing impactful tourism marketing strategies
Integrating Remote Sensing and Spatial Data for Ecological Sustainability through Spatio-temporal Analysis 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.v5i1.2082

Abstract

This research underscores the pivotal role of integrating spatial data and remote sensing technologies within a spatio-temporal analysis framework for regional development planning. Analyzing NDVI, NDBI, and SAVI values from 2013, 2018, and 2024 provided significant insights into vegetation health, urbanization, and soil conditions on Kumo Island. The NDVI values exhibited changes from a minimum of -0.0549, a mid-value of 0.1782, and a maximum of 0.4690 in 2013 to a minimum of 0.2456, a mid-value of 0.8296, and a maximum of 0.9416 in 2024. Similarly, the NDBI values shifted from a minimum of -0.8734, a mid-value of -0.5779, and a maximum of 0.0009 in 2013 to a minimum of -0.6561, a mid-value of -0.4304, and a maximum of 0.0247 in 2024. The SAVI values showed notable changes from a minimum of -0.0365, a mid-value of 0.1245, and a maximum of 0.3814 in 2013 to a minimum of 0.1138, a mid-value of 0.4953, and a maximum of 0.6160 in 2024. These findings highlight the importance of ecological sustainability in decision-making processes, demonstrating how advanced spatial analysis within a spatio-temporal framework can effectively monitor and manage land use changes. The urgency of this research lies in addressing rapid environmental changes and escalating human activities, necessitating timely and accurate monitoring techniques. The study reveals the utility of the NDVI, NDBI, and SAVI indices in assessing vegetation health, urbanization, and soil conditions, which are instrumental in identifying trends and informing sustainable development strategies. The research advocates for the continued use of remote sensing and spatial data to ensure balanced and informed regional development, emphasizing the necessity of sustainable practices to preserve ecological integrity while supporting socio-economic growth. Integrating remote sensing into the decision-making process enhances the accuracy and reliability of spatial data, leading to more effective and responsible regional development
Comparative Spatio-temporal Analysis Using NDVI, NDBI, and SAVI based on Landsat 8/9 OLI (2013, 2018 and 2024) 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.v5i1.2088

Abstract

The research on the comparative spatio-temporal analysis of NDVI, NDBI, and SAVI values for Kumo Island, Kakara Island, and Tagalaya Island from 2013, 2018, and 2024 offers insights into environmental dynamics influenced by human activities, particularly tourism. Key findings indicate that NDVI values, reflecting vegetation health, improved across all three islands, with Kumo Island showing the most significant increase from -0.0549 in 2013 to 0.2456 in 2024. NDBI values, indicative of urban development, also rose on all islands. Kumo Island's NDBI increased from -0.8734 in 2013 to -0.6561 in 2024, and Kakara Island's NDBI rose from -0.8838 in 2013 to -0.7183 in 2024. Tagalaya Island saw a more moderate rise in NDBI values from -0.8818 in 2013 to -0.7118 in 2024, suggesting controlled urban expansion. SAVI values, reflecting soil and vegetation conditions, also improved. Kumo Island's SAVI increased from -0.0365 in 2013 to 0.1138 in 2024, Kakara Island's from -0.1161 to -0.0319, and Tagalaya Island's from -0.1652 to -0.0732. These trends indicate effective soil conservation and sustainable land use practices. The findings highlight the dual impact of urbanization and environmental conservation, suggesting that while urbanization progresses, vegetation health and soil conditions are concurrently improving. This underscores the potential for balancing development with ecological sustainability through targeted conservation efforts. Future research should identify specific practices and policies contributing to these positive trends, ensuring economic development and environmental preservation can continue to coexist harmoniously on these islands
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.
Co-Authors A.Y. Agung Nugroho Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Astuti Kusumawicitra 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 Timisela, Marthen Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani