The Integration of Kansei Engineering and Kano Model on Natural Language Processing (NLP) to Support Development of Service Product in the Borobudur Temple Tourism

Abdullah Azzam (1), Nayoko Prasetyo Jati (2), Meilinda Maghfiroh (3)
(1) Department of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta, Indonesia
(2) Department of Industrial Engineering, Universitas Islam Indonesia, Yogyakarta, Indonesia
(3) Faculty of Transport and Logistics, Muscat University, Muscat, Oman
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Azzam, Abdullah, et al. “The Integration of Kansei Engineering and Kano Model on Natural Language Processing (NLP) to Support Development of Service Product in the Borobudur Temple Tourism”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 600-7, doi:10.18517/ijaseit.14.2.19290.
The Borobudur Temple Compounds is one of the spearheads of Indonesian tourism, anticipating introduction to the international community. Currently, the management of the compound is focused on maintaining the existing condition of tourism and increasing enthusiasm among visitors to experience the temple area. Amidst the challenges posed by the COVID-19 pandemic, the management has implemented stringent health procedures to ensure the safety and health of visitors. Therefore, this study aimed to investigate the service required to increase visitor enthusiasm and satisfaction in the Borobudur Temple Compounds using the Natural Language Processing (NLP) method to extract visitor sentiments as Kansei words from TripAdvisor. The results obtained were processed using Kansei Engineering type 1 to determine the most critical service quality category through Kano model. The integration of NLP, Kansei Engineering, and Kano model proved effective, showing attributes like popular Tripadvisor searches, such as exit, morning, temple, sunrise, and heat. Other attributes not listed but considered necessary based on Kansei Engineering and Kano model integration were identified, such as ticket prices, elephant enclosure management, and pamphlets. Based on the results, it was discovered that the management of Borobudur Temple Compounds needed to improve in two main aspects. These included the distance between each facility and the compound elephant cage, considered unenjoyable.

H. Reinhart et al., “Assessment of geological diversity, geosites, and geotourism potencies at Menoreh Mountain for designation of geopark area,” International Journal of Geoheritage and Parks, vol. 11, no. 3, pp. 385–406, Sep. 2023, doi: 10.1016/j.ijgeop.2023.05.005.

Kementrian PUPR, “Sinergitas Pengembangan Lima Destinasi Pariwisata Super Prioritas,” Kementrian PUPR, pp. 1–66, 2020, [Online]. Available: https://bpiw.pu.go.id/uploads/publication/attachment/Buletin BPIW SINERGI Edisi 44 - Januari 2020.pdf

A. Prawira Bima, H. A. Jofari, and E. P. Candrawidodo, “Tantangan Indonesia dalam Penataan Pariwisata Super Prioritas dalam Persaingan Global,” in Prosiding Simposium Nasional ''Tantangan Penyelenggaraan Pemerintahan di Era Revolusi Indusri 4.O", 2020, pp. 1551–1570. [Online]. Available: http://research-report.umm.ac.id/index.php/PSIP/article/view/3560

J. M. Wibowo and S. Hariadi, “Indonesia Sustainable Tourism Resilience in the COVID-19 Pandemic Era (Case Study of Five Indonesian Super- priority Destinations),” Millennial Asia, p. 09763996221105143, Jul. 2022, doi: 10.1177/09763996221105143.

J. Pan et al., “3D reconstruction of Borobudur reliefs from 2D monocular photographs based on soft-edge enhanced deep learning,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 183, pp. 439–450, Jan. 2022, doi: 10.1016/j.isprsjprs.2021.11.007.

N. A. I. Hasanah, D. Maryetnowati, F. N. Edelweis, F. Indriyani, and Q. Nugrahayu, “The climate comfort assessment for tourism purposes in Borobudur Temple Indonesia,” Heliyon, vol. 6, no. 12, p. e05828, Dec. 2020, doi: 10.1016/j.heliyon.2020.E05828.

Badan Pusat Statistik, “Pengunjung Candi Borobudur 2015-2020,” Online. Accessed: Mar. 31, 2022. [Online]. Available: https://magelangkab.bps.go.id/indicator/16/327/1/pengunjung-candi-borobudur.html

Media Indonesia, “Jumlah Wisatawan ke Borobudur pada Liburan Akhir Tahun Meningkat,” Online. Accessed: Mar. 31, 2022. [Online]. Available: https://mediaindonesia.com/nusantara/461810/jumlah-wisatawan-ke-borobudur-pada-liburan-akhir-tahun-meningkat

M. Álvarez-Carmona et al., “Natural language processing applied to tourism research: A systematic review and future research directions,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 10125–10144, Nov. 2022, doi:10.1016/j.jksuci.2022.10.010.

A. Kemperman, “A review of research into discrete choice experiments in tourism: Launching the Annals of Tourism Research Curated Collection on Discrete Choice Experiments in Tourism,” Ann Tour Res, vol. 87, p. 103137, Mar. 2021, doi: 10.1016/j.annals.2020.103137.

J. Gao, P. Peng, F. Lu, C. Claramunt, P. Qiu, and Y. Xu, “Mining tourist preferences and decision support via tourism-oriented knowledge graph,” Inf Process Manag, vol. 61, no. 1, p. 103523, Jan. 2024, doi: 10.1016/j.ipm.2023.103523.

Y. Zhao, X. Xu, and M. Wang, “Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews,” Int J Hosp Manag, vol. 76, pp. 111–121, 2019, doi:10.1016/j.ijhm.2018.03.017.

S. Yousaf and J. M. Kim, “Dark personalities and online reviews: A textual content analysis of review generation, consumption and distribution.,” Tour Manag, vol. 98, p. 104771, 2023, doi:10.1016/j.tourman.2023.104771.

W. F. Satrya, R. Aprilliyani, and E. H. Yossy, “Sentiment analysis of Indonesian police chief using multi-level ensemble model,” Procedia Comput Sci, vol. 216, pp. 620–629, 2023, doi:10.1016/j.procs.2022.12.177.

P. Mukherjee, Y. Badr, S. Doppalapudi, S. M. Srinivasan, R. S. Sangwan, and R. Sharma, “Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection,” Procedia Comput Sci, vol. 185, pp. 370–379, 2021, doi:10.1016/j.procs.2021.05.038.

Samsir et al., “Naives Bayes Algorithm for Twitter Sentiment Analysis,” J Phys Conf Ser, vol. 1933, no. 1, p. 012019, Jun. 2021, doi:10.1088/1742-6596/1933/1/012019.

A. K, K. P, L. Celestine S, and V. V Kumar, “Naive Bayes Algorithm for Sentiment Analysis on Twitter,” in 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 2021, pp. 1–4. doi: 10.1109/icscan53069.2021.9526473.

A. S. M. Alharbi and E. de Doncker, “Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information,” Cogn Syst Res, vol. 54, pp. 50–61, 2019, doi:10.1016/j.cogsys.2018.10.001.

R. Sann and P.-C. Lai, “Understanding homophily of service failure within the hotel guest cycle: Applying NLP-aspect-based sentiment analysis to the hospitality industry,” Int J Hosp Manag, vol. 91, p. 102678, 2020, doi:10.1016/j.ijhm.2020.102678.

K. Li, C. Zhou, X. (Robert) Luo, J. Benitez, and Q. Liao, “Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning,” Decis Support Syst, p. 113752, 2022, doi: 10.1016/j.dss.2022.113752.

G. Roy, “Travelers’ online review on hotel performance – Analyzing facts with the Theory of Lodging and sentiment analysis,” Int J Hosp Manag, vol. 111, p. 103459, 2023, doi:10.1016/j.ijhm.2023.103459.

D. Obembe, O. Kolade, F. Obembe, A. Owoseni, and O. Mafimisebi, “Covid-19 and the tourism industry: An early stage sentiment analysis of the impact of social media and stakeholder communication,” International Journal of Information Management Data Insights, vol. 1, no. 2, p. 100040, 2021, doi:10.1016/j.jjimei.2021.100040.

R. Sann and P.-C. Lai, “Understanding homophily of service failure within the hotel guest cycle: Applying NLP-aspect-based sentiment analysis to the hospitality industry,” Int J Hosp Manag, vol. 91, p. 102678, 2020, doi:10.1016/j.ijhm.2020.102678.

N. Saraswathi, T. Sasi Rooba, and S. Chakaravarthi, “Improving the accuracy of sentiment analysis using a linguistic rule-based feature selection method in tourism reviews,” Measurement: Sensors, vol. 29, p. 100888, 2023, doi: https:10.1016/j.measen.2023.100888.

R. Safa, P. Bayat, and L. Moghtader, “Automatic detection of depression symptoms in twitter using multimodal analysis,” J Supercomput, vol. 78, no. 4, pp. 4709–4744, 2022, doi:10.1007/s11227-021-04040-8.

S. W. Kelley, C. N. Mhaonaigh, L. Burke, R. Whelan, and C. M. Gillan, “Machine learning of language use on Twitter reveals weak and non-specific predictions,” NPJ Digit Med, vol. 5, no. 1, p. 35, 2022, doi: 10.1038/s41746-022-00576-y.

K. Fiok, W. Karwowski, E. Gutierrez, and M. Wilamowski, “Analysis of sentiment in tweets addressed to a single domain-specific Twitter account: Comparison of model performance and explainability of predictions,” Expert Syst Appl, vol. 186, p. 115771, 2021, doi:10.1016/j.eswa.2021.115771.

M. Bibi et al., “A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis,” Pattern Recognit Lett, vol. 158, pp. 80–86, 2022, doi:10.1016/j.patrec.2022.04.004.

H. Quan, S. Li, C. Zeng, H. Wei, and J. Hu, “Big Data and AI-Driven Product Design: A Survey,” Applied Sciences, vol. 13, no. 16, 2023, doi: 10.3390/app13169433.

J. Cha, S. Kim, and E. Park, “A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community,” Humanit Soc Sci Commun, vol. 9, no. 1, p. 325, 2022, doi: 10.1057/s41599-022-01313-2.

K. North, M. Zampieri, and M. Shardlow, “Lexical Complexity Prediction: An Overview,” ACM Comput. Surv., vol. 55, no. 9, Jan. 2023, doi: 10.1145/3557885.

Y. Jiao and Q.-X. Qu, “A proposal for Kansei knowledge extraction method based on natural language processing technology and online product reviews,” Comput Ind, vol. 108, pp. 1–11, 2019, doi:10.1016/j.compind.2019.02.011.

T. Hou, B. Yannou, Y. Leroy, and E. Poirson, “Mining Changes in User Expectation Over Time From Online Reviews,” Journal of Mechanical Design, vol. 141, no. 9, Apr. 2019, doi:10.1115/1.4042793.

K. Chen, J. Jin, and J. Luo, “Big consumer opinion data understanding for Kano categorization in new product development,” J Ambient Intell Humaniz Comput, vol. 13, no. 4, pp. 2269–2288, 2022, doi:10.1007/s12652-021-02985-5.

J.-W. Bi, Y. Liu, Z.-P. Fan, and E. Cambria, “Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model,” Int J Prod Res, vol. 57, no. 22, pp. 7068–7088, Nov. 2019, doi: 10.1080/00207543.2019.1574989.

Y. Gan et al., “Integrating aesthetic and emotional preferences in social robot design: An affective design approach with Kansei Engineering and Deep Convolutional Generative Adversarial Network,” Int J Ind Ergon, vol. 83, p. 103128, 2021, doi:10.1016/j.ergon.2021.103128.

X. Lai, S. Zhang, N. Mao, J. Liu, and Q. Chen, “Kansei engineering for new energy vehicle exterior design: An internet big data mining approach,” Comput Ind Eng, vol. 165, p. 107913, 2022, doi:10.1016/j.cie.2021.107913.

Z. Liu, J. Wu, Q. Chen, and T. Hu, “An improved Kansei engineering method based on the mining of online product reviews,” Alexandria Engineering Journal, vol. 65, pp. 797–808, 2023, doi:10.1016/j.aej.2022.09.044.

S. Avikal, R. Singh, and R. Rashmi, “QFD and Fuzzy Kano model based approach for classification of aesthetic attributes of SUV car profile,” J Intell Manuf, vol. 31, no. 2, pp. 271–284, 2020, doi:10.1007/s10845-018-1444-5.

S. Asian, J. K. Pool, A. Nazarpour, and R. A. Tabaeeian, “On the importance of service performance and customer satisfaction in third-party logistics selection,” Benchmarking: An International Journal, vol. 26, no. 5, pp. 1550–1564, Jan. 2019, doi: 10.1108/BIJ-05-2018-0121.

J. Zhang, A. Zhang, D. Liu, and Y. Bian, “Customer preferences extraction for air purifiers based on fine-grained sentiment analysis of online reviews,” Knowl Based Syst, vol. 228, p. 107259, 2021, doi:10.1016/j.knosys.2021.107259.

J. Jin, D. Jia, and K. Chen, “Mining online reviews with a Kansei-integrated Kano model for innovative product design,” Int J Prod Res, vol. 0, no. 0, pp. 1–20, 2021, doi: 10.1080/00207543.2021.1949641.

M. Hartono, “Incorporating Service Quality Tools into Kansei Engineering in Services: A Case Study of Indonesian Tourists,” Procedia Economics and Finance, vol. 4, pp. 201–212, 2012, doi:10.1016/S2212-5671(12)00335-8.

Y. Hsiao, M. Chen, and M. Lin, “Kansei Engineering with Online Review Mining for Hotel Service Development,” in 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 2017, pp. 29–34. doi: 10.1109/IIAI-AAI.2017.12.

Y. Sugiyama, J. Zheng, T. Matsuo, H. Iwamoto, and T. Hochin, “Multilingual Review Analysis for Attracting Foreign Visitors to Local Cities - About Sightseeing in Hamamatsu City -,” in 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI), 2018, pp. 741–746. doi: 10.1109/IIAI-AAI.2018.00153.

C.-T. Yeh and M.-C. Chen, “Applying Kansei Engineering and data mining to design door-to-door delivery service,” Comput Ind Eng, vol. 120, pp. 401–417, 2018, doi:10.1016/j.cie.2018.05.011.

N. A. M. Razali et al., “Opinion mining for national security: techniques, domain applications, challenges and research opportunities,” J Big Data, vol. 8, no. 1, p. 150, 2021, doi:10.1186/s40537-021-00536-5.

N. Kano, N. Seraku, F. Takahashi, and S. Tsuji, “Attractive Quality and Must-Be Quality,” Journal of the Japanese Society for Quality Control, vol. 41, pp. 39–48, 1984.

A. Parasuraman, V. A. Zeithaml, and L. L. Berry, “A Conceptual Model of Service Quality and Its Implications for Future Research,” J Mark, vol. 49, no. 4, pp. 41–50, 1985, doi: 10.2307/1251430.

T.-M. Choi, P.-S. Chow, B. Kwok, S.-C. Liu, and B. Shen, “Service Quality of Online Shopping Platforms: A Case-Based Empirical and Analytical Study,” Math Probl Eng, vol. 2013, p. 128678, 2013, doi:10.1155/2013/128678.

B. A. Fida, U. Ahmed, Y. Al-Balushi, and D. Singh, “Impact of Service Quality on Customer Loyalty and Customer Satisfaction in Islamic Banks in the Sultanate of Oman,” Sage Open, vol. 10, no. 2, p. 2158244020919517, Apr. 2020, doi: 10.1177/2158244020919517.

P. Bhattacharya et al., “Perception-satisfaction based quality assessment of tourism and hospitality services in the Himalayan region: An application of AHP-SERVQUAL approach on Sandakphu Trail, West Bengal, India,” International Journal of Geoheritage and Parks, vol. 11, no. 2, pp. 259–275, 2023, doi:10.1016/j.ijgeop.2023.04.001.

Y. Zhang, X. Tang, Z. Liu, and G. Xiong, “A Comparative Study of Hainan Island and Jeju Island,” J Coast Res, pp. 279–284, 2020, [Online]. Available: https://www.jstor.org/stable/48640294.

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