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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 : Building of Informatics, Technology and Science

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Sentiment Classification Using BERT-CNN and SMOTE: A Case Study on Hotel Reviews Dataset Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6309

Abstract

The increasing importance of user-generated content in the hospitality industry necessitates advanced sentiment analysis tools to derive actionable insights from customer reviews. Traditional methods often struggle with the complexities of natural language, such as contextual dependencies and nuanced emotional expressions. This research leverages the BERT-CNN hybrid model, which combines BERT’s contextual language understanding with CNN’s feature extraction capabilities, to address these challenges and improve sentiment classification accuracy. Using a dataset of 1,828 hotel reviews from Eastparc Hotel Yogyakarta, the model achieved an impressive accuracy of 99.59%, with precision and recall exceeding 0.99. The application of SMOTE effectively resolved class imbalance, enhancing the model’s ability to generalize across diverse sentiment classes. Training and validation loss curves exhibited steady convergence, indicating robust learning and minimal overfitting. These results provided actionable insights into customer satisfaction, offering targeted recommendations for enhancing service quality and operational strategies. This study demonstrates the practicality of integrating advanced machine learning architectures in sentiment analysis, enabling the hospitality sector to transform unstructured feedback into meaningful insights. The findings contribute to academic advancements in natural language processing and practical innovations in customer experience management. Future research may expand this framework to other domains, further underscoring its adaptability and impact.
Data-Driven Hospitality: Advanced Forecasting Models for Hotel Occupancy Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6611

Abstract

Accurate forecasting of hotel booking demand is essential for resource optimization, revenue maximization, and enhanced customer experience in the hospitality industry. This study evaluates the performance of three forecasting models, ARIMA, Prophet, and LSTM, using historical booking data to identify the most effective approach for predicting demand. The evaluation employed four key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R²), providing a comprehensive comparison. The results indicate that the LSTM model outperformed the others in prediction accuracy, achieving the lowest MAE (2.71) and MAPE (21.33%), demonstrating its strength in capturing complex patterns. However, its negative R-squared value (-0.20) suggests limitations in explaining overall data variance compared to ARIMA (0.51) and Prophet (0.50). The Prophet model excelled in seasonal decomposition but showed the highest MAPE (71.86%), while ARIMA delivered robust residual diagnostics, adhering well to model assumptions with consistent variance and randomness in residuals. The findings suggest that while LSTM is most effective for short-term forecasting, ARIMA and Prophet provide better interpretability and reliability for long-term trend analysis. A hybrid approach combining the strengths of all three models is recommended to enhance predictive accuracy and robustness. This study provides actionable insights for industry stakeholders seeking to improve decision-making processes and operational efficiency through advanced forecasting techniques.
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