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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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Distribution of Polarity Value between VADER and TextBlob in Sentiment Classification of Tourist Vlog Content Reviews Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5501

Abstract

This research employs sentiment analysis techniques to examine audience perceptions across three videos featuring tourist vlog content. Utilizing the CRISP-DM framework, the study compares the performance of VADER and TextBlob in sentiment classification, analyzing the distribution of polarity values and agreement levels between the two models. The findings reveal varying proportions of negative, neutral, and positive sentiments across the videos, with VADER and TextBlob demonstrating fair agreement levels ranging from 64.97% to 72.60%. These results underscore the importance of employing diverse sentiment analysis tools and language-specific models for accurate sentiment classification. The research contributes valuable insights for content creators, marketers, and analysts in understanding audience sentiments and shaping content strategies effectively.
Project Monitoring and Assessment System Design Using Rapid and Participatory Application Development Framework Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5505

Abstract

This study investigates the implementation of Rapid and Participatory Application Development (RPAD) in educational settings, emphasizing its urgent necessity in enhancing project-based learning environments. Given the rapid pace of technological advancements and the evolving demands of modern education, there is an urgent need for innovative methodologies that can effectively integrate digital tools into the learning process. RPAD's iterative development process and active user involvement ensure the creation of functional and user-centric applications. The findings demonstrate that RPAD facilitates timely feedback, continuous improvement, and the effective integration of digital tools, enriching the learning experience and aligning theoretical knowledge with real-world applications. This methodology drives substantial improvements in teaching methodologies and student engagement and ensures that educational institutions remain adaptive and future-ready. The study concludes that adopting RPAD is crucial for enhancing the effectiveness and relevance of contemporary education, addressing current challenges, and preparing for future demands.
Implementation of Perspective, Vader, and TextBlob in Toxicity and Sentiment Analysis of Food and Tourism Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i4.5632

Abstract

This research investigates the sentiment and toxicity of viewer responses to digital content on food and tourism using the Digital Content Reviews and Analysis Framework. Employing advanced text processing and sentiment analysis models such as Perspective, TextBlob, and Vader, the study analyzed 4,166 comments. The findings reveal a predominantly positive viewer sentiment, with VADER identifying 18.98% negative, 21.33% neutral, and 59.69% positive sentiments, while TextBlob identified 14.42% negative, 33.86% neutral, and 51.72% positive sentiments. The toxicity analysis highlighted various levels, with an average toxicity score of 0.15783 and notable scores for severe toxicity, identity attack, insult, profanity, and threat. The research underscores the importance of comprehensive sentiment analysis in understanding viewer engagement, providing valuable insights for content creators and marketers in the tourism industry. The study concludes with recommendations for further exploration and refinement of sentiment analysis methodologies to enhance the understanding and management of digital content interactions.
Analyzing Social Networks and Topic Clustering in Backpacker Tourism Content Reviews using K-means, Fast HDBScan, and Gaussian Mixture with Communalytic Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5969

Abstract

This research explores the integration of social network analysis and topic clustering techniques to provide novel insights into digital interactions and thematic trends within the context of backpacker tourism. Utilizing a structured framework, 3,575 records across three content IDs (c2ZMFDS_3rU, Sv_yxz7T8rU, and i9t9pbdo-bk) were processed and classified into 10 clusters using k-Means, Fast HDBScan, and Gaussian Mixture algorithms. Social network analysis was performed on 4,224 actor nodes and 395 edges, highlighting the role of key influencers in driving conversations while revealing the participation patterns of a larger, less engaged audience. The topic clustering revealed distinct themes, including budget travel, off-the-beaten-path destinations, and sustainable tourism, with each algorithm offering unique insights into the structure of the data. The novelty of this research lies in applying these computational methods to backpacker tourism, traditionally analyzed through qualitative approaches, to uncover how thematic discussions propagate within digital communities. By integrating these techniques, the study provides a deeper understanding of how key topics resonate with backpackers and how social interactions influence the spread of ideas. The findings offer valuable implications for content creators and tourism marketers seeking to engage this niche travel demographic more effectively. This work contributes a scalable, data-driven methodology for analyzing traveler behavior and preferences in virtual environments, enhancing the field of backpacker tourism research.
Toxicity and Social Network Analysis of Green Marketing Content for Electric Cars through Digital Media Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 13 No. 4 (2024): February: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

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

Abstract

This study aims to investigate the effectiveness of green marketing strategies in influencing consumer interest and purchasing behavior towards electric cars, focusing on media coverage, as exemplified by BBC News. Specifically, it seeks to understand how media portrayals of electric cars through green marketing narratives impact consumer perceptions and preferences in the context of sustainability. The research adopts the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The study culminates in noteworthy findings obtained through Toxicity Analysis and Social Network Analysis (SNA). Toxicity Analysis yielded specific numerical values across categories: Toxicity (0.05645, 0.99613), Severe Toxicity (0.00002, 0.00333), Identity Attack (0.00211, 0.35185), Insult (0.03630, 0.99520), Profanity (0.01584, 0.93590), and Threat (0.00279, 0.43515). These metrics signify varying levels of negative sentiment and potentially harmful language within the examined dataset. Concurrently, SNA provided structural insights with a diameter of 6, low density (0.009484), negligible reciprocity (0.000000), modest centralization (0.038160), and high modularity (0.872000). While the network exhibits centralized influence and limited reciprocity, the high modularity suggests distinct communities or clusters. These findings underscore the importance of considering sentiment dynamics and network structure, emphasizing the need for targeted interventions to mitigate toxicity and cultivate healthier communication environments.
Social network and sentiment analysis of product reviews (case of smartwatch product content) Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 13 No. 4 (2024): February: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

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

Abstract

This study addresses the need to understand the dynamics of sentiment and social network analysis (SNA) in the context of smartwatch product reviews. Leveraging the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, the research aims to analyze sentiments and social networks to glean insights into consumer behavior and interaction patterns. The CRISP-DM framework guides the research through structured phases of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Through sentiment analysis using Support Vector Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) and SNA, the study examines accuracy (91.41% +/- 1.66%), precision (100.00% +/- 0.00%), recall (82.80% +/- 3.36%), f-measure (90.56% +/- 2.01%), Area Under the Curve (AUC), as well as network metrics such as diameter (4), density (0.001036), reciprocity (0.000000), centralization (0.004920), and modularity (0.994200). Findings reveal a robust performance of the SVM algorithm coupled with SMOTE, showcasing high accuracy and effective discrimination between sentiments. Additionally, SNA uncovers valuable insights into network structures, communication patterns, and sentiment propagation dynamics within the online community. These findings contribute to a deeper understanding of consumer sentiments and interactions, guiding strategic marketing, product development, and reputation management decisions.
Selling vegetables through live streaming: sentiment and network analysis Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 13 No. 4 (2024): February: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

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

Abstract

This study addresses the research problem of understanding digital interactions and dynamics in online environments, mainly focusing on sentiment analysis and Social Network Analysis (SNA). The methodology integrates sentiment analysis techniques to discern prevailing attitudes and emotions within digital content, coupled with SNA to unveil intricate network structures and user relationships. Concurrently, SNA unveils intricate network structures and relationships among users, illuminated by numerical metrics such as Diameter (2), Density (0.003982), Reciprocity (0.000000), Centralization (0.027240), and Modularity (0.978600). Additionally, the performance vector further enhances the evaluation with metrics including accuracy (97.68% +/- 2.44%), AUC (0.429 +/- 0.477), precision (97.68% +/- 2.44%), recall (100.00% +/- 0.00%), and f-measure (98.81% +/- 1.25%). The study utilizes a dataset of digital content and user interactions, applying sentiment analysis to quantify sentiments and SNA to map network connections. Findings reveal nuanced insights into audience perceptions, engagement patterns, and network dynamics within digital ecosystems. Moreover, the study employs numerical metrics to evaluate the performance of sentiment analysis and SNA methodologies. The results underscore the importance of integrating sentiment analysis and SNA in comprehensively understanding online behavior and communication dynamics, offering valuable insights for content creation, engagement optimization, and community management strategies in digital environments.
Digital marketing of smartphone manufacturing product: toxicity, social network, and sentiment classification Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 14 No. 1 (2024): May: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijosea.v14i1.442

Abstract

This research explores digital interactions, analyzing toxicity, sentiment, and network dynamics using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Understanding and managing these elements are crucial for effective digital strategies with the rise of user-generated content. Leveraging machine learning, including Support Vector Machines (SVM) and Synthetic Minority Over-sampling Technique (SMOTE), toxicity analysis and sentiment classification are conducted. Data preprocessing involves text cleaning and feature engineering, aligning with the CRISP-DM data preparation phase. Toxicity levels are measured using various toxicity metrics, including Toxicity, Severe Toxicity, Identity Attack, Insult, Profanity, and Threat. Sentiment analysis employs SVM to classify sentiment polarity, while SMOTE addresses class imbalance as part of the CRISP-DM modeling phase. Social Network Analysis (SNA) techniques are also applied to study network structures following the CRISP-DM modeling phase. Network data are processed to compute key SNA metrics such as Diameter, Density, Reciprocity, Centralization, and Modularity. Findings reveal a toxicity level of 0.06194 and severe toxicity at 0.00730. Identity Attack stands at 0.01107, while insults and profanity are at 0.03803 and 0.04905, respectively. The threat is observed at 0.01359. The sentiment analysis indicates an accuracy of 97.94%, with a precision and recall of 98.07% and 99.86%, respectively, for the positive class. The f-measure for the positive class is 98.96%. The SNA metrics show a diameter of 4, a density of 0.000266, and a reciprocity of 0.000000. Centralization is calculated at 0.001468, while modularity stands at 0.999400.
Comprehensive analysis of tempel hamlet digital content reviews: Toxicity, sentiment, and social network Singgalen, Yerik Afrianto
International Journal on Social Science, Economics and Art Vol. 14 No. 1 (2024): May: Social Science, Economics
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijosea.v14i1.468

Abstract

This research addresses the complexities of digital content analysis, focusing on toxicity, sentiment, and social network dynamics, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) as the overarching framework. The research problem centers on understanding the prevalence of toxicity, discerning sentiment nuances, and unraveling viewer interactions within social networks. Comprehensive toxicity analysis was conducted, revealing specific scores for toxicity attributes and a prevalence of positive sentiment (72.5%). Sentiment classification utilizing the k-NN algorithm achieved exceptional accuracy (98.06%), showcasing its efficacy in sentiment discernment. Social network dynamics were examined, uncovering key metrics such as Diameter (3), Density (0.002140), Reciprocity (0.000000), Centralization (0.393200), and Modularity (0.552200), shedding light on network structures and interactions. Findings underscore the need for nuanced content moderation strategies and highlight the importance of fostering positive interactions in digital spaces. Recommendations include implementing targeted moderation policies, leveraging sentiment analysis for audience engagement, and fostering community-building initiatives to promote healthier online environments.
Penerapan CRISP-DM dalam Klasifikasi Sentimen dan Analisis Perilaku Pembelian Layanan Akomodasi Hotel Berbasis Algoritma Decision Tree (DT) Singgalen, Yerik Afrianto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7081

Abstract

The Cross-Industry Standard Process for Data Mining (CRISP-DM) approach is very relevant in identifying business challenges and producing recommendations in the form of appropriate models to face various business challenges. Sentiment classification is needed to identify and analyze consumer trends and preferences in order to plan risk mitigation strategies related to business sustainability. This study adopts the CRISP-DM method in classifying hotel guest sentiment through review data on the Agoda platform and analyzing sentiment data based on the purchase behavior of related products and services. Meanwhile, the stages in the CRISP-DM method are as follows: the stage of understanding the business context (business understanding), the stage of understanding data characteristics (data understanding), the modeling stage (modeling), the evaluation stage, and the implementation stage (deployment). The results of this study show that ten words are the attention of hotel guests and are dominated by positive sentiment, namely shopping, great, stay staff, clean, location, room, good, mall, and hotel. The classification results using the DT algorithm showed good performance with an accuracy value of 93.91%, a precision value of 90.98%, and a recall value of 97.77%.  In addition, the AUC value is 0.943 or 94.3%, and the f-measure value is 94.18%. Furthermore, sentiment analysis data can be developed into a Customer Relationship Management (CRM)  supporting application to analyze guest purchase history data related to sentiment, country of origin, guest type, room type, and length of stay by day, month, and year. Thus, the marketing strategy of hotel accommodation services can be optimized for personalization and increase interest and intention of returning stays.
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