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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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Brewing Knowledge and Revenue: Coffeeshop Management in Higher Education Settings through Business Model Canvas Fang, Liem Shiao; Singgalen, Yerik Afrianto
ARBITRASE: Journal of Economics and Accounting Vol. 5 No. 3 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/arbitrase.v5i3.1558

Abstract

This research examines the integration of coffeeshop management into higher education curricula through Business Model Canvas methodologies, utilizing Le Café at Atma Jaya Catholic University of Indonesia as a case study. The investigation employs qualitative methodology, incorporating document analysis and participant observation, to evaluate the dual-purpose paradigm of campus-based coffee establishments functioning simultaneously as commercial enterprises and experiential learning laboratories. Findings reveal a bifurcated operational framework with distinct yet complementary value propositions: commercial hospitality services for campus stakeholders and authentic skill development opportunities for tourism students. Financial analysis demonstrates a significant maturation trajectory, with operations transitioning from consistent deficits in 2022-2023 to substantial surpluses exceeding Rp 20 million in late 2024, achieved through strategic revenue diversification across café operations (60%), consignment arrangements (20%), and event services (15%). The research identifies unique pedagogical advantages emerging from this educational-commercial integration, including enhanced practical competency development in areas traditionally challenging to address through conventional instruction. The study further establishes critical success factors for governance structures, including proportional resource allocation, integrated quality assurance protocols, and cross-functional oversight mechanisms. This comprehensive analytical framework contributes substantively to educational enterprise management literature while providing higher education institutions with implementable models for balancing academic rigor with commercial sustainability amid increasing financial constraints in contemporary educational environments.
Understanding Guest Experiences in Remote Luxury Resort: A SERVQUAL Analysis Seingo, Martha Maraka; Pontolawokang, Theresya Ellen; Singgalen, Yerik Afrianto
ARBITRASE: Journal of Economics and Accounting Vol. 5 No. 3 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/arbitrase.v5i3.2078

Abstract

This study examines guest experiences at Nihi Resort in Sumba, Indonesia, utilizing an adapted SERVQUAL framework to analyze service quality dimensions in a remote luxury hospitality context. Through systematic analysis of guest reviews collected from TripAdvisor, the research employs a qualitative methodology incorporating open, axial, and selective coding techniques to identify patterns in service quality perception. Findings reveal that Assurance Trust is the dominant SERVQUAL dimension in this isolated luxury setting, fundamentally reconfiguring traditional service quality hierarchies. Network visualization analysis demonstrates how Assurance functions as both a direct satisfaction determinant and a mediating variable influencing perceptions across reliability, responsiveness, and empathy domains. This dimensional prioritization reflects the psychological mechanisms activated when guests engage with service providers in unfamiliar, isolated environments characterized by substantial financial investments and limited alternatives. The study contributes to hospitality literature by establishing a contextually sensitive analytical model for understanding service quality in geographically isolated luxury resorts. Strategic implications for management include prioritizing investments in staff competency development, implementing transparent safety communication protocols, and developing specialized training programs addressing cultural sensitivity and interpersonal trust development. These findings advance theoretical understanding of how geographic isolation modulates conventional service quality paradigms while providing luxury hospitality practitioners with evidence-based strategic guidance for resource allocation prioritization.
Geospatial-Based Information System for Visitor Management in the Baduy Region Rahmadini, Asyifa Catur; Tharsini, Priya; Singgalen, Yerik Afrianto
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7186

Abstract

This research presents the development of a Geospatial-Based Information System prototype for visitor management in Indonesia's culturally sensitive Baduy indigenous region. The study addresses the critical challenge of balancing cultural preservation with sustainable tourism development through an innovative technological framework that respects indigenous sovereignty. Utilizing Rapid Application Development (RAD) methodology, the research integrates traditional knowledge systems with modern geospatial technologies to create a governance tool that enhances rather than displaces traditional decision-making structures. The prototype system architecture incorporates permit management workflows, GPS-enabled check-in protocols, and spatial monitoring capabilities that enable Jaro authorities to regulate visitor access, monitor distribution patterns, and enforce culturally appropriate boundaries. Black box testing validated the prototype's functionality across multiple operational scenarios, confirming its feasibility as a protective mechanism against unregulated tourism activities. This methodological approach to cross-cultural information system design establishes a foundational framework demonstrating how thoughtfully implemented geospatial technologies amplify Indigenous governance capabilities while creating sustainable economic opportunities aligned with traditional values and cultural preservation imperatives. The research contributes significantly to the discourse on Indigenous digital sovereignty and culturally appropriate technological interventions in heritage management.
Performance Evaluation of SVM Algorithm in Sentiment Classification: A Visual Journey of Wonderful Indonesia Content Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1709

Abstract

This study addresses the research problem of understanding public sentiment towards tourism-themed content on YouTube, with a specific focus on "A Visual Journey of Wonderful Indonesia." The primary aim is to explore how viewers perceive and depict Indonesia as a tourism destination through their comments on YouTube videos. Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, sentence analysis is conducted using the Support Vector Machine (SVM) algorithm with the Synthetic Minority Over-sampling Technique (SMOTE) to classify sentiments within a dataset of YouTube comments as positive, negative, or neutral. The analysis of frequently used words in the comments provides valuable insights into Indonesia's perception, revealing positive sentiments reflected in terms such as "beautiful," "wonderful," and "amazing," emphasizing the country's aesthetic appeal. Notably, terms like "orang" and "Indonesian" indicate appreciation for Indonesia's rich cultural heritage and its people. These findings highlight the pivotal role of destination branding efforts in shaping positive perceptions and emotions toward Indonesia. The results indicate the efficacy of the SVM-SMOTE model, achieving high accuracy (84.26%), precision (100.00%), recall (68.51%), f-measure (81.25%), and AUC (0.996) in accurately classifying sentiment patterns within analyzed YouTube content. This offers practical implications for destination managers and marketers. Conversely, the SVM algorithm without SMOTE demonstrates impressive accuracy, precision, and recall scores of 97.08%, but its AUC value of 0.607 suggests potential challenges in discriminating between positive and negative sentiment instances. These findings provide valuable insights into the role of digital media platforms in shaping destination perceptions and offer practical implications for destination marketers and managers
Social Network Analysis and Sentiment Classification of Robotic Restaurant Content using Naïve Bayes Classifier Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1710

Abstract

Sentiment analysis is crucial in understanding public opinion, particularly in emerging technologies such as automation AI and robotic restaurant services. However, achieving accurate sentiment classification in sentiment analysis tasks poses challenges, especially when dealing with imbalanced data. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) through the Naive Bayes Classifier (NBC) algorithm and Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced data challenges in sentiment analysis. Social network analysis (SNA) collects and analyzes user-generated content related to automation AI and robotic restaurant services, providing insights into public sentiment. Additionally, the occurrence of frequently used words such as "people" (182), "food" (158), "jobs" (135), "robots" (137), "wage" (102), "work" (78), "robot" (79), "minimum" (78), "fast" (70), and "workers" (65) is examined. The performance of the NBC algorithm with and without SMOTE integration is compared. With SMOTE, the algorithm exhibits an accuracy of 70.11% +/- 3.52%, precision of 88.82% +/- 5.06%, recall of 46.06% +/- 6.13%, AUC of 0.967 +/- 0.016, and F-measure of 60.46% +/- 6.02%. Without SMOTE, the algorithm yields an accuracy of 48.90% +/- 4.36%, precision of 72.15% +/- 5.25%, recall of 44.32% +/- 7.15%, AUC of 0.777 +/- 0.051, and F-measure of 54.57% +/- 5.78%.  Recommendations to further enhance the algorithm's performance include exploring additional optimization techniques, such as feature engineering and ensemble methods, and continuing data collection and augmentation efforts to improve dataset representativeness. Regular monitoring and evaluation and iterative refinement based on evolving data patterns are also recommended to ensure sustained effectiveness in sentiment analysis tasks.
Social Network Analysis and Sentiment Classification of Extended Reality Product Content Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1712

Abstract

This study explores Extended Reality (XR) products, specifically focusing on the Apple Vision Pro, to elucidate consumer perceptions and the underlying social dynamics of these innovative technologies. This research delves into Extended Reality (XR) products, specifically focusing on the Apple Vision Pro, aiming to understand consumer perceptions and social dynamics surrounding these innovative technologies. By leveraging sentiment analysis and Social Network Analysis (SNA) alongside CRISP-DM and SVM algorithms, this study provides a comprehensive insight into sentiment patterns, network structures, and influential factors within the XR community. A multi-faceted approach is adopted to achieve the research objectives. Sentiment analysis and SNA dissect sentiment patterns and uncover network structures within the XR community. The CRISP-DM framework guides the research process, ensuring systematic data analysis and interpretation. SVM algorithms classify sentiments, providing a robust analytical framework for understanding consumer sentiments towards XR products. The analysis yields significant insights into XR consumer perceptions and social dynamics. The calculated network metrics, including a density of 0.000124, absence of reciprocity, centralization value of 0.001331, and modularity value of 0.999000, shed light on crucial network dynamics within the XR community. Examining frequently used words reveals prevalent topics within the XR discourse, providing valuable context for understanding consumer sentiments. Furthermore, the evaluation of SVM algorithms demonstrates commendable performance metrics, with the SVM without SMOTE achieving an accuracy rate of 84.33%, precision of 84.67%, recall of 99.28%, and f_measure of 91.39%. In comparison, the SVM with SMOTE exhibits an accuracy of 81.82% and a precision of 97.58%. This research contributes valuable insights into the consumer landscape of XR products, mainly focusing on the Apple Vision Pro. By combining sentiment analysis, SNA, and established methodologies, the study offers a nuanced understanding of consumer perceptions and social dynamics within the XR community. These findings inform strategic decisions and contribute to advancements in XR technologies, offering valuable insights into the efficacy of sentiment analysis techniques in understanding consumer sentiments
Implementation of SVM and DT for Sentiment Classification: Tempel Hamlet Content Reviews Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1826

Abstract

The study aims to investigate the effectiveness of sentiment analysis algorithms, specifically Support Vector Machine (SVM) and Decision Tree (DT), integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance issues. Guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, the research involves several stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The process begins with understanding the business objectives of sentiment analysis and proceeds to explore and prepare the dataset for analysis. SVM and DT algorithms, enhanced with SMOTE, are then implemented for sentiment classification. The study reveals promising results in sentiment analysis tasks. When integrated with SMOTE, SVM achieves an accuracy of 99.21%, while DT attains an accuracy of 98.33%. The Area Under the Curve (AUC) metrics indicate high confidence in classifying positive instances, with SVM and DT demonstrating AUC scores of 1.000 and 0.996, respectively. These findings underscore the efficacy of SVM and DT algorithms, enhanced with SMOTE, in accurately classifying sentiment within text data, thereby addressing class imbalance issues effectively
Performance Evaluation of Sentiment Classification Models: A Comparative Study of NBC, SVM, and DT with SMOTE Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1827

Abstract

This research explores the performance of sentiment classification models, namely Naive Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM), using the CRISP-DM methodology in the context of digital content analysis and data mining. The analysis was conducted on a SMOTE dataset in Rapidminer, yielding significant performance metrics. The NBC model achieved an accuracy of 86.98% +/- 0.96%, precision of 100.00% +/- 0.00%, recall of 78.82% +/- 1.55%, and f-measure of 88.15% +/- 0.97%, with an AUC of 0.657 +/- 0.203. Similarly, the DT model exhibited an accuracy of 93.20% +/- 0.42%, precision of 90.87% +/- 0.64%, recall of 98.88% +/- 0.31%, and f-measure of 94.70% +/- 0.31%, with an AUC of 0.918 +/- 0.006. Furthermore, the SVM model demonstrated an accuracy of 96.80% +/- 0.65%, precision of 98.99% +/- 0.28%, recall of 95.77% +/- 1.03%, and f-measure of 97.35% +/- 0.55%, with an AUC of 0.994. These findings highlight the efficacy of these models in accurately classifying sentiments within digital content, suggesting their suitability for various data mining applications. Recommendations for future research include exploring ensemble methods, continuous model updating, alternative sampling techniques, feature engineering approaches, and collaboration with domain experts to enhance real-world applicability
Comparative Analysis of DT and SVM Model Performance with SMOTE in Sentiment Classification Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1828

Abstract

This research investigates the efficacy of employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to analyze sentiment classification models. The study focuses on evaluating the performance of Decision Trees (DT) and Support Vector Machine (SVM) models integrated with the Synthetic Minority Over-sampling Technique (SMOTE) across various performance metrics, including accuracy, precision, recall, f-measure, and Area Under the Curve (AUC). Using CRISP-DM, the research ensures a systematic data preprocessing, modeling, and evaluation approach. The findings reveal that both DT and SVM models with SMOTE achieve high accuracy rates, with DT yielding an accuracy of 98.37% +/- 0.48% and SVM achieving 98.91% +/- 0.59%. These models effectively distinguish between positive and negative sentiments, as precision, recall, and f-measure scores indicate. Additionally, the AUC scores underscore the robustness of the models in sentiment analysis tasks. These results highlight the potential of CRISP-DM as a structured methodology for sentiment classification research, providing insights into the performance of different machine learning algorithms in handling imbalanced datasets. Based on these findings, it is recommended that future studies further explore the application of CRISP-DM in sentiment analysis tasks and investigate the scalability of DT and SVM models with SMOTE in larger datasets.
Sentiment classification of coral reef 101 content using decision tree algorithm through CRISP-DM Singgalen, Yerik Afrianto
International Journal of Basic and Applied Science Vol. 12 No. 3 (2023): December: Basic and Applied Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v12i3.297

Abstract

This research aims to classify public sentiment regarding the content of "Coral Reef 101," published by National Geographic. The methodology employed is the Cross-Industry Standard Process for Data Mining (CRISP-DM), encompassing stages such as business understanding, data understanding, modeling, evaluation, and deployment. The Decision Tree algorithm is utilized in conjunction with the SMOTE operator. This comprehensive approach enables the systematic analysis of public sentiment towards coral reef content, facilitating a deeper understanding of public perception and attitudes. The results of this study indicate that the DT algorithm with SMOTE demonstrates an accuracy of 87.51% +/- 4.28% (micro average: 87.50%), a precision of 80.35% +/- 5.10% (micro average: 80.00%) (positive class: Positive), recall of 100.00% +/- 0.00% (micro average: 100.00%) (positive class: Positive), f-measure of 89.02% +/- 3.22% (micro average: 88.89%) (positive class: Positive), and an AUC of 0.875 +/- 0.044 (micro average: 0.875) (positive class: Positive). These metrics demonstrate the effectiveness of the DT algorithm with SMOTE in accurately classifying public sentiment towards coral reef-related content, particularly in correctly identifying positive sentiment instances. The high accuracy, precision, recall, f-measure, and AUC values underscore the robustness and reliability of the model in sentiment analysis tasks.
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