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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
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Journal : Journal of Computer System and Informatics (JoSYC)

Comprehensive Analysis of Sentiment Classification and Toxicity Assessment in Cultural Documentary Videos Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5150

Abstract

This research explores sentiment classification and toxicity assessment in cultural documentary videos through a systematic analysis framework based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The study evaluates the sentiment polarity of viewer comments by utilizing a diverse array of machine-learning algorithms, including k-NN, DT, NBC, and SVM. It identifies toxic language patterns across multiple videos. Additionally, the research employs SMOTE to address class imbalance issues and enhance model performance. The results reveal high accuracy rates ranging from 72.24% to 96.79% in sentiment classification, indicating the effectiveness of the proposed methodology. Moreover, toxicity analysis unveils varying degrees of toxic language prevalence, with toxicity scores ranging from 0.01270 to 0.09334 across different videos. Despite these achievements, the study acknowledges the inherent limitations of toxicity scoring algorithms in capturing contextual nuances. Overall, this research contributes to understanding sentiment dynamics and toxicity trends in cultural documentary content and underscores the importance of employing advanced machine learning techniques within a structured analytical framework for insightful data interpretation and decision-making.
Comprehensive Analysis of Sentiment and Toxicity Dynamics in Tourist Vlog Reviews: A CRISP-DM Approach Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5154

Abstract

This research employs the CRISP-DM framework to analyze sentiment and toxicity dynamics in tourist vlog reviews thoroughly. The study delves into sentiment classification and toxicity identification nuances by leveraging machine learning algorithms such as k-NN, SVM, NBC, and DT with SMOTE. Utilizing a dataset comprising a substantial number of posts, the analysis reveals varying levels of accuracy across different algorithms. For instance, k-NN and SVM showcase promising accuracy rates of 85.90% and 86.27% in sentiment classification, while NBC and DT with SMOTE yield 72.52% and 71.14%, respectively. Furthermore, the research elucidates the limitations of toxicity analysis, with NBC demonstrating a precision of 64.96% and DT exhibiting lower recall rates. These findings highlight the importance of robust methodologies for understanding sentiment and toxicity dynamics in online content, particularly in tourist vlog reviews.
Sentiment and Toxicity Analysis of Biometric Authentication and Facial Recognition Technology Content Reviews using Cross-Industry Standard Process for Data-Mining Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5167

Abstract

This study investigates sentiment analysis methodologies within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining), aiming to discern the efficacy of various algorithms in sentiment classification tasks. The research uses a structured approach to evaluate SVM, NBC, DT, and K-NN algorithms with the SMOTE oversampling technique, uncovering distinct performance metrics and limitations. Results indicate SVM achieving 59.88% accuracy, NBC at 59.25%, DT with 52.09%, and K-NN obtaining 54.80%, highlighting the differential precision, recall, and f-measure. Additionally, content analysis identifies pertinent themes such as Biometric security, Cloud storage, and Emotion Analysis, enriching sentiment dynamics comprehension. The toxicity scores of analyzed videos reveal nuanced sentiment nuances, with the first video exhibiting Toxicity: 0.13227 and the second scoring Toxicity: 0.12794. This study underscores the significance of informed algorithm selection and evaluation methodologies within CRISP-DM, fostering optimized sentiment analysis outcomes while acknowledging diverse topical nuances.
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 Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani