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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 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

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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)

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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)

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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)

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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.
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 : Universitas 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 : Universitas 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.
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 : Universitas 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.
MONITORING MANGROVE MENGGUNAKAN MODEL NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI): STUDI KASUS DI HALMAHERA UTARA, INDONESIA Singgalen, Yerik Afrianto; Gudiato, Candra; Prasetyo, Sri Yulianto Joko; Fibriani, Charitas
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 13 No. 2 (2021): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v13i2.34771

Abstract

Ekowisata berbasis masyarakat menjadi salah satu pendekatan yang efektif dalam menjaga kelestarian hutan mangrove. Strategi untuk menetapkan prioritas pengembangan kawasan mangrove, dapat dilakukan dengan menganalisis kerapatan hutan mangrove. Kawasan mangrove dengan nilai kerapatan paling rendah perlu diprioritaskan sebagai strategi preservasi dan konservasi melalui konsep ekowisata berbasis masyarakat. Artikel ini bertujuan mengidentifikasi sebaran mangrove menggunakan model normalized difference vegetation index (NDVI) di Kabupaten Halmahera Utara, Indonesia. Perspektif penghidupan berkelanjutan digunakan untuk mendiskusikan konteks sosio-kultural masyarakat lokal. Penelitian ini mengadopsi metode campuran. Pengolahan data terbagi menjadi dua tahap yakni: tahap pertama, pemetaan sebaran hutan mangrove berdasarkan tingkat kerapatan; tahap kedua, trianggulasi. Pemetaan sebaran hutan mangrove menggunakan citra satelit Landsat 8 operational land imager (OLI) tahun 2013 dan 2021 serta model NDVI di Tanjung Pilawang, Pulau Kumo, Pulau Kakara, Pulau Maiti, dan Pulau Tagalaya. Hasil penelitian ini menunjukkan bahwa Tanjung Pilawang pada zona 1 dan 2 memiliki Nilai NDVI paling rendah di tahun 2021 yakni 0,22 dengan kategori jarang, sehingga perlu diprioritaskan dalam pengembangan ekowisata mangrove berbasis komunitas sebagai strategi perlindungan kawasan hutan mangrove.
MONITORING KAWASAN EKOWISATA MANGROVE MENGGUNAKAN NDVI, NDWI, DAN CMRI DI PULAU DODOLA, KABUPATEN PULAU MOROTAI, INDONESIA Singgalen, Yerik Afrianto; Manongga, Danny
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 14 No. 1 (2022): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v14i1.37605

Abstract

Pembangunan infrastruktur pariwisata menyebabkan alih fungsi lahan atau konversi lahan dari ruang terbuka hijau menjadi kawasan ekonomi pariwisata. Pemanfaatan kawasan mangrove sebagai daya tarik ekowisata perlu dimonitoring secara berkala agar pembangunan sarana dan prasarana tidak mengancam keberlanjutan vegetasi mangrove. Artikel ini bertujuan mengidentifikasi sebaran mangrove menggunakan model normalized difference vegetation index (NDVI), normalized difference water index (NDWI), combined mangrove recognize index (CMRI) di Kabupaten Pulau Morotai, Provinsi Maluku Utara, Indonesia. Perspektif ekowisata berkelanjutan digunakan untuk mendiskusikan konteks sosio-kultural masyarakat Morotai khususnya masyarakat Pulau Kolorai. Penelitian ini mengadopsi metode campuran. Pengolahan data terbagi menjadi dua tahap yakni: tahap pertama, pemetaan sebaran mangrove Pulau Dodola menggunakan citra satelit Landsat 8 Operational Land Imager (OLI) dari tahun 2013-2021 berdasarkan kalkulasi NDVI, NDWI, dan CMRI; tahap kedua, trianggulasi. Hasil penelitian ini menunjukkan bahwa pada tahun 2017, terjadi penurunan nilai NDVI dan CMRI di Zona 1, Zona, 2, dan Zona 3 sebagai kawasan ekowisata mangrove Pulau Dodola.. Hal ini menunjukkan adanya ancaman ekosistem mangrove apabila pembangunan infrastruktur menyebabkan penurunan nilai indeks vegetasi secara signifikan dari tahun ke tahun. Dengan demikian, diperlukan program pengendalian terhadap program pembangunan infrastruktur dengan melibatkan masyarakat lokal dalam pemeliharaan ekosistem mangrove.
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