APPLICATION OF WEIGHTED AVERAGE ALGORITHM IN RECREATIONAL PARK TOURIST DESTINATION RECOMMENDATION SYSTEM BASED ON GOOGLE MAPS USER RATINGS

Nayla Faiza, Hervilla Amanda R. Siregar, Nur Shafwa Aulia Sitorus, Agung Nugroho, Muhammad Fathir Aulia, Mhd Furqan

Abstract


Abstract: The development of digital technology has changed the behavior patterns of tourists in choosing travel destinations. Google Maps is now not only used to find restaurant locations but has also become the main source for searching nearby tourist destinations based on user ratings and reviews. This research aims to build a recommendation system for recreational park tourist destinations in Medan City by applying the Weighted Average algorithm using Google Maps user rating data. The data used comes from reviews by five users of five popular recreational parks in Medan City during the period from January 1, 2025, to April 30, 2025. The Weighted Average algorithm was chosen because it can provide a more objective and fair assessment by taking into account the weight of each rating given by users. As a result, this system can recommend the best recreational parks based on user experiences related to cleanliness, parking facilities, toilets, security, running paths, and accessibility. It is hoped that this system can help tourists choose destinations that meet their needs and preferences, as well as provide a more enjoyable and satisfying travel experience.

Keywords : digital technology; google maps; recommendation system; weighted average algorithm


Abstrak: Perkembangan teknologi digital telah mengubah pola perilaku wisatawan dalam memilih destinasi wisata. Google Maps kini tidak hanya digunakan untuk mencari lokasi restoran, tetapi juga menjadi sumber utama dalam mencari destinasi wisata terdekat berdasarkan rating dan ulasan pengguna. Penelitian ini bertujuan untuk membangun sistem rekomendasi destinasi wisata taman rekreasi di Kota Medan dengan menerapkan algoritma Weighted Average menggunakan data rating pengguna Google Maps. Data yang digunakan berasal dari lima ulasan pengguna terhadap lima taman rekreasi populer di Kota Medan selama periode 1 Januari 2025 hingga 30 April 2025. Algoritma Weighted Average dipilih karena mampu memberikan penilaian yang lebih objektif dan adil dengan memperhatikan bobot setiap rating yang diberikan pengguna. Hasilnya, sistem ini dapat merekomendasikan taman rekreasi terbaik berdasarkan pengalaman pengguna terkait aspek kebersihan, fasilitas parkir, toilet, keamanan, lintasan lari, dan aksesibilitas. Diharapkan sistem ini dapat membantu wisatawan dalam memilih destinasi yang sesuai dengan kebutuhan, preferensi, dan memberikan pengalaman wisata yang lebih menyenangkan dan memuaskan.

 

Kata Kunci: google maps; sistem rekomendasi; teknologi digital; weighted average algorithm



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References


Boneta, B.A., Dutha, I., & Kusumawidjaya, E. (2022). The Little Book of Jakarta: A Backpacker's Guide . Publisher DeePublish.

Chang, Y. (2025). Weighted association rule algorithm application research in cultural tourism recommendation. GeoJournal , 90 (2), 50. https://doi.org/10.1007/s10708-025-11293-w

da Silva, D.C., Manzato, M.G., & Durão, F.A. (2021). Exploiting personalized calibration and metrics for fairness recommendations. Expert Systems with Applications , 181 , 115112. https://doi.org/10.1016/j.eswa.2021.115112

Fayyaz, Z., Ebrahimian, M., Nawara, D., Ibrahim, A., & Kashef, R. (2020). Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities. Applied Sciences , 10 (21), 7748. https://doi.org/10.3390/app10217748

Iqbal, S., Zhang, C., Arif, M., Hassan, M., & Ahmad, S. (2020). A new fuzzy time series forecasting method based on clustering and weighted average approach. Journal of Intelligent & Fuzzy Systems , 38 (5), 6089–6098. https://doi.org/10.3233/JIFS-179693

Kim, M., & Baek, S. (2024). Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction. Proceedings of the 2024 ACM Web Conference , 389–400. https://doi.org/10.1145/3589334.3645372

Linares-Mustarós, S., Ferrer-Comalat, J.C., Corominas-Coll, D., & Merigó, J.M. (2021). The weighted average multiexpert. Information Sciences , 557 , 355–372. https://doi.org/10.1016/j.ins.2020.08.029

Mohamed, A. A., & Kronenberg, J. (2025). Users' experiences of park accessibility and attractiveness based on online review analytics. Scientific Reports , 15 (1), 4268. https://doi.org/10.1038/s41598-025-88500-8

Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology & Tourism , 22 (3), 455–476. https://doi.org/10.1007/s40558-019-00160-3

Phuangsuwan, P., Siripipatthanakul, S., Limna, P., & Pariwongkhuntorn, N. (2024). The impact of Google Maps applications on the digital economy. Corporate and Business Strategy Review , 5 (1), 192–203. https://doi.org/10.22495/cbsrv5i1art18

Pitoura, E., Stefanidis, K., & Koutrika, G. (2022). Fairness in rankings and recommendations: an overview. The VLDB Journal , 31 (3), 431–458. https://doi.org/10.1007/s00778-021-00697-y

Shin, E. J. (2024). Shifts in the neighborhood distribution of daytime population after the COVID-19 outbreak: A comparison across age groups. Cities , 147 , 104825. https://doi.org/10.1016/j.cities.2024.104825

Wang, J., Li, Y., Wu, B., & Wang, Y. (2021). Tourism destination image based on tourism user generated content on internet. Tourism Review , 76 (1), 125–137. https://doi.org/10.1108/TR-04-2019-0132

Wankhade, S., Sahni, M., Mellado-Cid, C., & Leon-Castro, E. (2023). Using the Ordered Weighted Average Operator to Gauge Variation in Agriculture Commodities in India. Axioms , 12 (10), 985. https://doi.org/10.3390/axioms12100985

Zhou, X., Tian, J., Peng, J., & Su, M. (2021). A Smart Tourism Recommendation Algorithm Based on Cellular Geospatial Clustering and Multivariate Weighted Collaborative Filtering. ISPRS International Journal of Geo-Information , 10 (9), 628. https://doi.org/10.3390/ijgi10090628




DOI: https://doi.org/10.54314/teknisi.v5i2.3790

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