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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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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.
Analyzing Hotel Customer Satisfaction Using Review Dataset: Insights and Implications for Service Improvement Singgalen, Yerik Afrianto
Journal of Business and Economics Research (JBE) Vol 5 No 3 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jbe.v5i3.6015

Abstract

This research investigates customer satisfaction at Meruorah Komodo Labuan Bajo through a comprehensive analysis of review data extracted from the Agoda platform. By examining 1,340 reviews, including 527 verified accounts, the study identifies key factors influencing guest experiences, such as service quality, room features, and location. The methodology comprised four stages: hotel selection, data scraping, data processing, and data interpretation. Findings indicate that premium room types, such as “The Signature Sea View Room,” consistently receive high satisfaction ratings, with 414 mentions (2.99%), highlighting the value placed on scenic views and superior amenities. Seasonal fluctuations and guest origins also impact satisfaction, with Indonesian guests strongly preferring familiarity, while international travelers prioritize diverse amenities. The data shows that 37 out of 203 accounts were domestic, while 17 were from the United States and Australia combined. The study reveals that 89% of domestic guests reported satisfaction, compared to varied expectations among international visitors. These insights suggest that tailored service strategies and enhancements in service consistency can further improve overall guest satisfaction. The research underscores the necessity of aligning service offerings with guest expectations to maintain a competitive edge in the dynamic hospitality industry.
Hotel Customer Satisfaction: A Comprehensive Analysis of Perceived Cleanliness, Location, Service, and Value Singgalen, Yerik Afrianto
Journal of Business and Economics Research (JBE) Vol 5 No 3 (2024): October 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jbe.v5i3.6016

Abstract

This research investigates the key determinants of customer satisfaction in the hospitality industry, focusing on cleanliness, service quality, location, and value. Analyzing guest reviews, the study reveals that 85% of guests consider cleanliness a primary factor influencing their overall experience, while 78% highlight service quality, particularly staff responsiveness and professionalism, as crucial components. Location is identified as a significant contributor by 65% of guests, emphasizing convenience and accessibility to local attractions, and 72% of guests evaluate their satisfaction based on the perceived value of the stay, which balances price and service quality. Additionally, digital engagement, health and safety perceptions, and sustainability practices play an increasing role in shaping guest satisfaction. Specifically, 60% of guests appreciate digital features such as contactless check-in and personalized communication. Meanwhile, 70% note that visible health and safety measures, including enhanced cleaning protocols, positively impact their comfort and trust. Furthermore, 58% of guests prefer hotels adopting sustainability practices, such as reducing plastic use and promoting eco-friendly amenities. The study concludes that 90% of guests rated cleanliness, service quality, and value highly were more likely to recommend the property and return in the future. In contrast, properties lacking in these areas saw a 45% decline in repeat visit intentions. These findings underscore hospitality providers' need to prioritize these factors and integrate digital, health, and sustainability considerations to optimize service delivery, enhance guest satisfaction, and establish a sustainable competitive advantage.
Sentiment Analysis and Trend Mapping of Hotel Reviews Using LSTM and GRU Singgalen, Yerik Afrianto
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.926

Abstract

This study explores applying Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for sentiment analysis and trend mapping of hotel reviews, specifically focusing on customer feedback from Hotel Vila Ombak in Lombok, Indonesia. The primary objective was to leverage these advanced deep learning models to capture nuanced sentiment patterns in unstructured textual data, enhancing insights into guest satisfaction. The analysis was conducted on a dataset of 326 reviews, achieving an overall model accuracy of 91% (0.91). The results showed that while the models excelled in identifying positive sentiments, with a precision of 0.94, recall of 0.98, and F1-score of 0.96, they struggled with minority classes. Both negative and neutral sentiments exhibited 0% accuracy, primarily due to the dataset’s imbalance, where positive reviews constituted 92.3% of the total entries. The macro average metrics (precision 0.31, recall 0.33, F1-score 0.32) highlighted the model's limitations in classifying sentiments less frequently despite high weighted averages driven by the dominant positive class. This research underscores the need to address data imbalance and suggests that future studies incorporate techniques like data augmentation or hybrid models to improve performance across all sentiment categories. By optimizing sentiment analysis models, hospitality businesses can gain deeper insights into customer feedback, ultimately enhancing service quality and customer satisfaction.
Hotel Guest Length of Stay Prediction Using Random Forest Regressor Singgalen, Yerik Afrianto
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.959

Abstract

This research offers a robust framework for integrating predictive analytics into hospitality operations, contributing to sustainable growth and competitive advantage in the industry. This research investigates the application of the Random Forest Regression model to predict the Length of Stay (LoS) of hotel guests, leveraging key features such as country, guest type, room type, and rating. The study addresses the need for precise forecasting to optimize resource allocation, improve operational efficiency, and support data-driven decision-making in the hospitality sector. The methodology involves data collection from a structured dataset of guest reviews, preprocessing through encoding categorical variables, converting target values into numeric forms, and standardizing features to ensure consistency and uniformity. The dataset is split into training (80%) and testing (20%) subsets, with hyperparameters such as n_estimators=100 and random_state=42 set to ensure stability and reproducibility during model training. The Random Forest Regression model demonstrated strong predictive performance, achieving an R-squared value of 0.85 and a Mean Absolute Error (MAE) of 1.06. Feature importance analysis identified "country" as the most significant variable (importance score: 0.5), followed by guest type (0.2), room type (0.15), and rating (0.15). The Predicted vs. Actual Plot and Error Distribution evaluation reveals that most errors cluster near zero, indicating high accuracy with minor deviations in extreme cases. These findings emphasize the model’s potential to enhance marketing strategies, optimize resource allocation, and improve guest satisfaction. This research offers a robust framework for integrating predictive analytics into hospitality operations, contributing to sustainable growth and competitive advantage in the industry.
Exploring the Shariah-Compliant Hotel Market: Meeting the Needs of Muslim Travelers Singgalen, Yerik Afrianto
Ekonomi, Keuangan, Investasi dan Syariah (EKUITAS) Vol 6 No 1 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/ekuitas.v6i1.5525

Abstract

This study explores the dynamics of guest preferences and satisfaction within the context of Sharia-compliant hospitality, using data from 445 verified reviews at The Sahira Hotel. Employing a descriptive-analytic methodology, the research utilizes visitor data extracted from Agoda’s review platform, focusing on room preferences, stay duration, and satisfaction ratings. The dataset was systematically filtered, cleaned, and categorized to ensure relevance and accuracy before conducting qualitative and quantitative analyses. Key findings reveal significant trends in visitor demographics and preferences. Families with young children and teenagers strongly preferred spacious accommodations, particularly the Family Deluxe Triple, accounting for over 35% of such bookings. In contrast, solo travelers, representing 20% of the dataset, favored functional and affordable options like the Deluxe Twin. Ratings analysis revealed the Deluxe Twin as the most highly rated room type, receiving over 40% "Exceptional" ratings. Short stays dominated the data, with 60% of guests staying for one night, emphasizing the importance of high-quality service for transient visitors. The study also underscores the significance of cultural and religious considerations, such as Halal-certified amenities and gender-sensitive spaces, in enhancing guest satisfaction. By aligning operational strategies with these insights, the findings provide actionable recommendations for optimizing marketing efforts, improving service delivery, and ensuring consistent guest satisfaction. This research contributes to the broader discourse on integrating cultural sensitivity with modern hospitality practices, offering a pathway for sustainable growth in the competitive halal tourism market.
An Analysis of Visitor Perception Toward Shariah-Compliant Hotels in Contemporary Hospitality Singgalen, Yerik Afrianto
Ekonomi, Keuangan, Investasi dan Syariah (EKUITAS) Vol 6 No 2 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/ekuitas.v6i2.6184

Abstract

This research examines visitor perceptions of Shariah-compliant hotels within the context of contemporary hospitality, focusing on how these establishments meet guest expectations and foster satisfaction in a competitive global market. The study analyzes 445 customer reviews using a descriptive-analytical methodology to explore integrating Islamic principles with modern service standards. Key themes include cleanliness, staff behavior, Halal compliance, and family-oriented facilities. Cleanliness, mentioned in over 30% of reviews, emerges as the most critical factor, reflecting its alignment with Islamic values and universal hospitality standards. Staff behavior, noted in 25% of reviews, highlights the importance of attentiveness and politeness in shaping guest perceptions. Halal compliance, cited in 20% of feedback, underscores the significance of providing prayer facilities and Halal-certified dining options for Muslim travelers. Family-friendly and worship-related amenities, accounting for 15% of mentions, illustrate the need for culturally and spiritually inclusive environments. Sentiment analysis reveals a weak positive correlation (0.165) between ratings and sentiment polarity, while dissatisfaction, noted in 10% of reviews, primarily relates to tranquility, maintenance, and service inconsistencies. The findings emphasize that the success of Shariah-compliant hotels relies on the seamless integration of faith-based principles with operational excellence. Addressing guest concerns and leveraging positive feedback enhances satisfaction and strengthens loyalty and competitiveness. This study contributes to the broader discourse on inclusive hospitality management, offering practical insights for operational improvement and theoretical advancements in understanding niche markets.
Toxicity and topic analysis of travel vlog content in digital era: perspective and multilingual embedding model (voyage-multilingual-2) Singgalen, Yerik Afrianto
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.844.pp199-210

Abstract

This research investigates the complexities of online discourse by conducting a detailed toxicity and topic analysis of travel vlog content on user-generated platforms. By analyzing 1,503 posts using the Perspective API, the study finds generally low levels of toxicity, with an average toxicity score of 0.06995 and a peak of 0.78207, and similarly low average scores for severe toxicity, identity attack, insult, profanity, and threat (0.00654, 0.01237, 0.03778, 0.06241, and 0.01186, respectively). However, the highest recorded values for these measures—0.45895 for severe toxicity, 0.69287 for identity attack, 0.63084 for insult, 0.81864 for profanity, and 0.51957 for threat—highlight the sporadic presence of harmful content. Advanced clustering techniques, such as HDBScan, k-Means, and Gaussian Mixture models, enable a comprehensive examination of thematic diversity and sentiment distribution within the comments, offering valuable insights into audience engagement and perception. These findings underline the critical need for compelling content moderation and community management strategies to mitigate toxic behaviors and promote a positive digital environment. The study concludes that as digital media evolves, further research into toxicity, thematic content, and user engagement is essential for enhancing theoretical frameworks and practical applications in digital communication.
Toxicity, social network and topic analysis of digital content: Perspective and multilingual embedding model Singgalen, Yerik Afrianto
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 3 (2024): July: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.845.pp115-128

Abstract

This research presents a comprehensive approach to analyzing digital content by integrating toxicity analysis, clustering techniques, and Social Network Analysis (SNA) to understand online interactions better. The study finds that, while the average toxicity levels are relatively low, with scores such as 0.06355 for toxicity and 0.00468 for severe toxicity, there are significant spikes, reaching maximum scores of 0.82996 for toxicity and 0.89494 for profanity. These spikes highlight the necessity for continuous monitoring and adaptive moderation strategies to minimize the impact of harmful language. Clustering methods, including K-Means, HDBScan, and Gaussian Mixture models, provide deep insights into the thematic structure of viewer discourse, identifying both prevalent and niche topics. The Gaussian Mixture model identified ten distinct clusters, while HDBScan revealed varying cluster densities, reflecting the diverse range of discussions within the community. In addition, SNA, with 1,716 nodes and 37 edges, offers critical insights into the relational dynamics of the network, pinpointing key influencers and mapping the flow of information between different user groups. By synthesizing these methodologies, the research provides a robust framework for understanding the content and context of digital interactions, facilitating more effective strategies for enhancing community engagement, mitigating toxicity, and promoting a healthier, more inclusive online environment.
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