Sentiment Analysis of Ampera Bridge as a National Tourism Destination

Mariana Purba, Yadi Yadi

Abstract


Ampera Bridge is one of the leading tourism icons in Palembang which attracts thousands of visitors every year. This research aims to analyze visitors' opinions about the Ampera Bridge using opinion mining techniques in Google Review reviews. Research methods include collecting review data from Google Reviews, data preprocessing, sentiment analysis, and aspect analysis. The data collected includes 307 reviews taken in the period April 2024. These reviews were analyzed using the Support Vector Machine (SVM) algorithm to classify sentiment as positive, negative, or neutral. The analysis results show that 83% of reviews have positive sentiment, 9% are negative, and 8% are neutral. The main aspects often discussed by visitors include the view and beauty of the bridge, historical and cultural value, accessibility and transportation, facilities and cleanliness, as well as tourist experiences and activities. Positive sentiments were mainly related to the beauty of the bridge's architecture and lighting, as well as its historical value. However, negative sentiment was mainly caused by cleanliness issues and traffic jams around the bridge. Based on these findings, several recommendations put forward include improving cleaning facilities, better traffic management, developing public facilities, and diversifying tourist activities. It is hoped that the implementation of these recommendations can improve the quality of the visitor experience and the attractiveness of the Ampera Bridge as a major tourist destination. This research provides valuable insights for tourism managers and local governments to improve the quality of services and facilities at the Ampera Bridge.

Keywords


Ampera; Bridges; Google reviews; Sentiment analysis; Tourism

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References


E. B. Santoso and A. Nugroho, “Analisis sentimen calon presiden indonesia 2019 berdasarkan komentar publik di facebook,” J. Eksplora Inform., vol. 9, no. 1, pp. 60–69, 2019, doi: https://doi.org/10.30864/eksplora.v9i1.254.

D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional,” J. Tekno Kompak, vol. 15, no. 1, pp. 131–145, 2021, [Online]. Available: https://ejurnal.teknokrat.ac.id/index.php/teknokompak/article/view/744

A. K. Fauziyyah, “Analisis sentimen pandemi Covid19 pada streaming Twitter dengan text mining Python,” J. Ilm. SINUS, vol. 18, no. 2, pp. 31–42, 2020, [Online]. Available: https://p3m.sinus.ac.id/jurnal/index.php/e-jurnal_SINUS/article/view/491

E. Halim, R. Purba, and A. Andri, “Consumer Opinion Extraction Using Text Mining for Product Recommendations On E-Commerce,” … Artif. Intell. Data Min. …, 2021, [Online]. Available: https://caritulisan.com/media/397625-consumer-opinion-extraction-using-text-m-f2bd5311.pdf

N. C. Lengkong, O. Safitri, S. Machsus, Y. R. Putra, and ..., “Analisis Sentimen Penerapan Psbb Di Dki Jakarta Dan Dampaknya Terhadap Pergerakan Ihsg,” J. Teknokr., vol. 15, no. 1, pp. 20–25, 2021, doi: DOI: https://doi.org/10.33365/jti.v15i1.866.

G. Hagerer, W. S. Leung, Q. Liu, H. Danner, and ..., “A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining,” arXiv Prepr. arXiv …, 2021, [Online]. Available: https://arxiv.org/abs/2111.02259

A. P. Giovani, A. Ardiansyah, and T. Haryanti, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, pp. 116–124, 2020, doi: DOI: https://doi.org/10.33365/jti.v14i2.679.

N. M. K. Saeed, N. A. Helal, N. L. Badr, and T. F. Gharib, “The Impact of Spam Reviews on Feature-based Sentiment Analysis,” in Proceedings - 2018 13th International Conference on Computer Engineering and Systems, ICCES 2018, Z. A.M., T. M., E.-K. M.W., S. A., B. E.-D. A.M., and A. H.M., Eds., Information Systems Department, Ain Shams University, Faculty of Computer and Information Sciences, Cairo, Egypt: Institute of Electrical and Electronics Engineers Inc., 2019, pp. 633–639. doi: 10.1109/ICCES.2018.8639343.

H. Tuhuteru, “Analisis Sentimen Masyarakat Terhadap Pembatasan Sosial Berksala Besar Menggunakan Algoritma Support Vector Machine,” J. Inf. Syst. Dev., vol. 5, no. 2, pp. 7–13, 2020, [Online]. Available: https://ejournal.medan.uph.edu/index.php/isd/article/view/381

S. Samsir, A. Ambiyar, U. Verawardina, and ..., “Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes,” J. Media Inform. Budidarma, vol. 5, no. 1, pp. 157–163, 2021, doi: DOI: http://dx.doi.org/10.30865/mib.v5i1.2580.

A. Rácz, D. Bajusz, R. A. Miranda-Quintana, and K. Héberger, “Machine learning models for classification tasks related to drug safety,” Mol. Divers., vol. 25, no. 3, pp. 1409–1424, 2021, doi: 10.1007/s11030-021-10239-x.

N. Tsapatsoulis and C. Djouvas, “Opinion mining from social media short texts: Does collective intelligence beat deep learning?,” Front. Robot. AI, 2019, doi: 10.3389/frobt.2018.00138.

S. Y. Kim, K. Ganesan, P. Dickens, and S. Panda, “Public sentiment toward solar energy—opinion mining of twitter using a transformer-based language model,” Sustainability, 2021, [Online]. Available: https://www.mdpi.com/1018186

M. Z. Asghar, A. Khan, S. R. Zahra, S. Ahmad, and F. M. Kundi, “Aspect-based opinion mining framework using heuristic patterns,” Cluster Comput., 2019, doi: 10.1007/s10586-017-1096-9.

R. Mahendrajaya and G. A. Buntoro, “Analisis Sentimen Pengguna Gopay Menggunakan Metode Lexicon Based Dan Support Vector Machine,” Jounal Komputek, vol. 3, no. 2, pp. 52–63, 2019, doi: DOI: 10.24269/jkt.v3i2.270.

M. Heydari and B. Teimourpour, “Persian Opinion Mining:A Networked Analysis Approach,” in 7th International Conference on Web Research, ICWR 2021, Tarbiat Modares University, School of Industrial and System Engineering, MSc Graduate in Network Science, Tehran, Iran: Institute of Electrical and Electronics Engineers Inc., 2021, pp. 142–149. doi: 10.1109/ICWR51868.2021.9443158.

M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” Smatika J., vol. 10, no. 2, pp. 71–76, 2020, doi: https://doi.org/10.32664/smatika.v10i02.455.

A. V. M. Kumar and N. K. AN, “Sentiment analysis using robust hierarchical clustering algorithm for opinion mining on movie reviews-based applications,” Inter J Innov. Technol Explor. …, 2019, [Online]. Available: https://www.researchgate.net/profile/Mohan-Kumar-Av/publication/343878502_Sentiment_Analysis_Using_Robust_Hierarchical_Clustering_Algorithm_for_Opinion_Mining_On_Movie_Reviews-Based_Applications/links/5fe34cea92851c13feb1fe7b/Sentiment-Analysis-Using-Robu

Y. A. Singgalen, “Pemilihan metode dan algoritma dalam analisis sentimen di media sosial: sistematic literature review,” J. Inf. Syst. Informatics, vol. 3, no. 2, pp. 278–302, 2021, doi: DOI: 10.33557/journalisi.v3i2.125.

Q. Wan, X. Xu, J. Zhuang, and B. Pan, “A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data,” Expert Syst. Appl., vol. 185, 2021, doi: 10.1016/j.eswa.2021.115629.

K. Karunia, A. E. Putri, M. D. Fachriani, and M. H. Rois, “Evaluation of the Effectiveness of Neural Network Models for Analyzing Customer Review Sentiments on Marketplace,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 2, no. 1, pp. 52–59, Apr. 2024, doi: 10.57152/predatecs.v2i1.1100.




DOI: http://dx.doi.org/10.24014/ijaidm.v7i2.30132

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