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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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Journal : Journal of Computer System and Informatics (JoSYC)

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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.
Utilizing Knowledge Discovery in Databases (KDD) for Hotel Guest Feedback Analysis 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.v6i1.6094

Abstract

This research explores the application of Knowledge Discovery in Databases (KDD) to analyze hotel guest feedback and improve service quality at Bintang Flores Hotel in Labuan Bajo. Utilizing KDD methodologies, the study processed 589 guest reviews to identify key factors influencing customer satisfaction, including cleanliness (1.00), location (0.82), and staff service (0.71). The analysis also highlighted issues such as limited breakfast variety (0.59) and inconsistent Wi-Fi connectivity (0.41) as recurring concerns, especially for long-term guests and business travelers. The data revealed that guests staying in the Deluxe Double or Twin Room frequently rated their experience as "Excellent" or "Very Good," with couples and families expressing high satisfaction levels. In contrast, suite categories received fewer and more varied ratings, signaling areas for targeted improvement. Through KDD, the study effectively combined structured numerical ratings and unstructured written feedback to pinpoint areas needing operational enhancement. Addressing challenges related to service consistency during peak periods, infrastructure maintenance, and food variety is essential for boosting guest satisfaction. The findings support implementing targeted strategies to ensure that Bintang Flores Hotel maintains a competitive edge and meets evolving customer expectations in the hospitality market.
IndoBERT-Based Sentiment Analysis for Understanding Hotel Guests’ Preferences Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6864

Abstract

The rapid growth of the hospitality industry and the increasing reliance on online reviews emphasize the need for advanced sentiment analysis tools to understand customer preferences effectively. This study explores the application of IndoBERT, a pre-trained language model tailored for the Indonesian language, in classifying sentiments from hotel guest reviews. Utilizing a dataset of 715 reviews, the study employed the Knowledge Discovery in Databases (KDD) framework for systematic data preprocessing, feature extraction, and machine learning analysis. IndoBERT demonstrated exceptional performance, achieving perfect precision, recall, and F1-scores of 1.00 for both positive (657 reviews) and negative (53 reviews) sentiment classes. The ROC curve analysis also yielded a mean AUC score of 0.86, validating the model's robustness and reliability. The results highlight IndoBERT's capability to accurately capture linguistic nuances and contextual meaning, offering actionable insights into factors influencing guest satisfaction, such as cleanliness, staff behavior, and service quality. This research contributes to advancing natural language processing applications in regional contexts and provides practical implications for enhancing service strategies in the hospitality sector. Future research should expand the model's application to other industries and explore multimodal approaches for a more comprehensive understanding of customer behavior.
Improved Sentiment Classification Using Multilingual BERT with Enhanced Performance Evaluation for Hotel Guest Review Analysis Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 2 (2025): February 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i2.6870

Abstract

Sentiment analysis in hotel guest reviews has become essential for evaluating customer satisfaction and service quality. This study improves sentiment classification accuracy by utilizing the Multilingual BERT model with an improved performance evaluation framework. Using the Knowledge Discovery in Databases (KDD) methodology, this research involves data selection, preprocessing, transformation, sentiment classification, and performance evaluation. A dataset of 715 hotel reviews from Qubika Boutique Hotel, sourced from Agoda, was used to assess the model's effectiveness. The classification results showed high accuracy in identifying positive sentiment, with 98% precision, 97% memory, and 98% F1 score, as observed in 432 correctly classified reviews. However, challenges were identified in the classification of neutral sentiment, which achieved a precision of 87% with 127 correctly classified cases, and negative sentiment, where the accuracy was 92%, with 104 correctly identified reviews. The overlap in confidence scores, especially in the range of 0.4-0.6 between neutral and negative sentiment, highlights the need for improved contextual embedding and hybrid modeling techniques. The sentiment distribution analysis revealed that 60-70% of reviews were positive, 20-30% neutral, and 10-15% indicated dissatisfaction, underscoring the need for targeted service improvement. These findings provide valuable insights for data-driven decision-making in hospitality management, enabling businesses to strengthen service power and address critical areas of concern. Future research should focus on refining model interpretability, expanding multilingual datasets, and integrating real-time sentiment analysis to improve classification performance. Strengthening these aspects will contribute to a more robust and scalable sentiment analysis framework, ensuring greater precision in capturing the guest experience and optimizing service strategies in the hospitality industry.
Sentiment Classification of Over-Tourism Issues in Responsible Tourism Content using Naïve Bayes Classifier Afrianto Singgalen, Yerik
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.v5i2.4904

Abstract

The research problem addressed in this study is the analysis of public sentiment regarding over-tourism issues. Utilizing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology and the Naive Bayes Classifier (NBC) algorithm, the study navigates through stages of business understanding, data processing, modeling, evaluation, and deployment. The central focus lies in understanding and classifying public sentiments surrounding the challenges associated with over-tourism. The findings reveal that the NBC algorithm, particularly when augmented with Synthetic Minority Over-sampling Technique (SMOTE), demonstrates superior performance metrics, showcasing an accuracy of 84.82%, precision of 91.69%, recall of 76.75%, f-measure of 83.47%, and AUC of 0.838. The comparison with NBC without SMOTE, which registers an accuracy of 78.16%, precision of 87.61%, recall of 74.56%, f-measure of 80.51%, and AUC of 0.745, underscores the significance of addressing class imbalance for improved predictive performance. Integrating CRISP-DM with the NBC algorithm and SMOTE proves instrumental in advancing sentiment analysis methodologies, providing nuanced insights into public perceptions and attitudes concerning the critical issue of over-tourism.
Sentiment Classification of Climate Change and Tourism Content Using Support Vector Machine Singgalen, Yerik Afrianto
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 Singgalen, Yerik Afrianto
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.
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