TRAVEL ITINERARY RECOMMENDER SYSTEM USING MACHINE LEARNING ANALYSIS AND WEB APPLICATION DEVELOPMENT: A CASE OF BATAM CITY REGION

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Published: Apr 13, 2023
DOI: https://doi.org/10.36050/betrik.v14i1.570
Keywords:
: Recommender System; Machine Learning; Travel Itinerary; ADDIE method; TAM model

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Hosse Fernando
Syaeful Anas Aklani
Universitas Internasional Batam

Abstract

Batam City is an island with a complete and diverse point of destination (POI). Searching for information is common when someone is making a travel itinerary. With the help of technology, planning a trip should be fast and easy. In this study, the authors will build a web application-based recommender system with the help of machine learning. This study uses the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development method  and then the TAM Model (Technology Acceptance Model) to analyze the effectiveness. The results of system testing show a range of scores with the lowest value 0.509 and the highest value 0.572. This score indicates the degree of correlation between the test and the underlying construct it is designed to measure. A score of 0.509 indicates a weak correlation between the test and constructs, while a score of 0.572 indicates a stronger correlation. Score above 0.5 Thus, the authors hope that this research can be used as a reference or knowledge for future readers or researchers.

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How to Cite
Hosse Fernando, & Syaeful Anas Aklani. (2023). TRAVEL ITINERARY RECOMMENDER SYSTEM USING MACHINE LEARNING ANALYSIS AND WEB APPLICATION DEVELOPMENT: A CASE OF BATAM CITY REGION. JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi Dan Komputer, 14(1), 56-62. https://doi.org/10.36050/betrik.v14i1.570
Issue
Vol 14 No 1 (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Alexan, A., Alexan, A., & Stefan, O. (2020). Machine learning activity detection using ML.Net. 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging, SIITME 2020 - Conference Proceedings, 188–191. https://doi.org/10.1109/SIITME50350.2020.9292294
Ardiyansah, D., Pahlevi, O., & Santoso, T. (2021). Implementasi Metode Prototyping Pada Sistem Informasi. Jurnal Teknik Dan Sains, 2(2), 17–22. http://jurnal.uts.ac.id/index.php/hexagon/article/view/1083%0Ahttps://jurnal.uts.ac.id/index.php/hexagon/article/download/1083/701
Chen, C. C., & Tsai, J. L. (2019). Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, 96, 628–638. https://doi.org/10.1016/j.future.2017.02.028
Darmawan, V., Asmara, R., & Prasetyaningrum, I. (2021). Implementasi Context Aware Pada Sistem Travel Planner Berbasis Mobile. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 5(1), 23–27. https://doi.org/10.46880/jmika.vol5no1.pp23-27
Ependi, U., Panjaitan, F., & Syakti, F. (2020). Pengembangan Aplikasi Mobile Travel Guide pada Provinsi Sumatera Selatan. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 607. https://doi.org/10.25126/jtiik.2020732107
Rawat, P., & Mahajan, A. N. (2020). ReactJS: A Modern Web Development Framework. International Journal of Innovative Science and Research Technology, 5(11), 698–702. www.ijisrt.com
Rivanthio, T. R. (2020). Perancangan Website E-commerce Rikas Collection. Jurnal Teknologi Informasi Dan Komunikasi, 7(2), 140–146.
Wisnuadhi, B., & Setiawan, I. (2021). Rekomendasi Fitur yang Mempengaruhi Harga Sewa Menggunakan Pendekatan Machine Learning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(4), 673. https://doi.org/10.25126/jtiik.2021843305
Zahid, M. Z. (2018). Aplikasi Berbasis Android untuk Pembelajaran: Potensi dan Metode Pengembangan. PRISMA: Prosiding Seminar Nasional Matematika, 1, 910–918.