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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 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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Journal : Journal of Computer System and Informatics (JoSYC)

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Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine Yerik Afrianto Singgalen
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4908

Abstract

This research aims to classify public sentiment regarding the issue of climate change and tourism. The research problem addressed in this study pertains to the classification of public sentiment concerning climate change within the tourism sector. Specifically, the study aims to explore and classify the public's sentiments regarding the impact of climate change on tourism activities.The methodology employed is CRISP-DM, which encompasses stages of business understanding, data understanding, modeling, evaluation, and deployment. Specifically, the SVM and SMOTE algorithms are utilized in the modeling stage to achieve optimal results. By leveraging this systematic approach and advanced algorithms, the study seeks to comprehensively analyze public sentiment towards climate change within the context of tourism, thus contributing valuable insights to academia and industry practitioners. Applying CRISP-DM methodology coupled with SVM and SMOTE algorithms enhances the rigor and effectiveness of sentiment analysis in addressing the complexities of climate change discourse in the tourism sector. The findings of this research demonstrate that the SVM and SMOTE algorithms yield promising results in sentiment classification, with an accuracy of 86.15% +/- 1.68% (micro average: 86.15%), precision of 85.17% +/- 2.16% (micro average: 85.11%) (positive class: Positive), recall of 87.64% +/- 3.39% (micro average: 87.64%) (positive class: Positive), f_measure of 86.34% +/- 1.79% (micro average: 86.35%) (positive class: Positive), and AUC of 0.923 +/- 0.012 (micro average: 0.923) (positive class: Positive). These metrics indicate the effectiveness and reliability of the SVM and SMOTE algorithms in accurately classifying sentiment toward climate change in the context of tourism. The high accuracy, precision, recall, f_measure, and AUC scores suggest that the models produced by these algorithms are robust and capable of capturing nuanced sentiment patterns, thereby contributing to the advancement of sentiment analysis techniques in climate change research within the tourism domain.
Sentiment Classification of Robot Hotel Content using NBC and SVM Algorithm Yerik Afrianto Singgalen
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4924

Abstract

Sentiment analysis plays a pivotal role in comprehending public sentiment, notably within digital communication, where copious amounts of textual data are generated daily. This study delves into the efficacy of sentiment classification models, namely the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), within the imbalanced datasets commonly encountered in sentiment analysis tasks. Employing a comparative analysis methodology, a dataset comprising robot hotel reviews from online platforms is the basis for evaluation. Both NBC and SVM models undergo training and assessment, with and without the Synthetic Minority Over-sampling Technique (SMOTE), to rectify the class imbalance. Performance evaluation relies on critical metrics, including accuracy, recall, precision, f-measure, and Area Under Curve (AUC) to gauge model effectiveness. Findings demonstrate SVM's superiority over NBC in terms of accuracy (SVM: 76.88%, NBC: 67.43%), precision (SVM: 92.03%, NBC: 86.87%), recall (SVM: 58.88%, NBC: 41.00%), f-measure (SVM: 71.78%, NBC: 55.63%), and AUC (SVM: 0.907, NBC: 0.961). Incorporating SMOTE significantly enhances both models' performance, particularly in addressing class imbalance concerns. Although NBC exhibits a more balanced performance across precision and recall metrics, SVM demonstrates heightened accuracy and predictive capability in sentiment classification tasks. These findings underscore the pivotal role of algorithm selection and preprocessing techniques in optimizing sentiment analysis performance, thereby providing invaluable insights for practitioners and researchers alike.
Comprehensive Analysis of Sentiment Classification and Toxicity Assessment in Cultural Documentary Videos Yerik Afrianto Singgalen
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 Yerik Afrianto Singgalen
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 Yerik Afrianto Singgalen
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.
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.
Understanding Hotel Customer Experience through User-Generated Reviews using Knowledge Discovery in Databases (KDD) Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6014

Abstract

This research explores the analysis of 388 hotel customer reviews to understand guest experiences, employing advanced analytical methodologies to uncover valuable insights for service quality enhancement. Utilizing the Knowledge Discovery in Databases (KDD) framework, the study applies Latent Dirichlet Allocation (LDA) for topic clustering and k-nearest Neighbors (k-NN), enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) for sentiment classification. The integration of these techniques allows for the extraction of coherent thematic patterns and the accurate differentiation of sentiment categories within the reviews. The findings reveal that LDA, evaluated through metrics such as log-likelihood (-54,886.092) and coherence scores (-14.949), effectively captures the underlying themes discussed by guests, providing a clear representation of customer priorities and concerns. Additionally, applying SMOTE significantly improves the k-NN model's performance, achieving an accuracy of 91.43% and a precision of 97.26% by balancing class distributions and enhancing classification accuracy. This approach demonstrates the potential of combining topic modeling and sentiment analysis to derive actionable insights, which can be strategically utilized to optimize service delivery and elevate the overall customer experience in the hospitality industry. The study concludes that leveraging such data-driven methodologies facilitates a deeper understanding of customer feedback, ultimately supporting informed decision-making and continuous service improvement.
Co-Authors A.Y. Agung Nugroho Abigail Rosandrine Kayla Putri Rahadi Agnes Harnadi Agnes Harnadi Agung Mulyadi Purba Alfonso Harrison Aloisius Gita Nathaniel Aprius Sutresno, Stephen 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 Eugenius Kau Suni 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 Henoch Juli Christanto Heru Prasadja 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 Ruben William Setiawan Samuel Piolo Seingo, Martha Maraka Setiawan, Ruben William Siemens Benyamin Tjhang Sri Yulianto Joko Prasetyo Stephen Aprius Sutresno Suharsono SUHARSONO Tabuni, Gasper Tharsini, Priya Titi Susilowati Prabawa Titis Puspitarini Widodo, Eko Winayu, Birgitta Narindri Rara Yan Dirk Wabiser Yoel Kristian Zsarin Astri Puji Insani