Fudholi Dhomas Hatta Primary Author mixed material bibliography Semarang Universitas Negeri Semarang 2021 e English

Artikel Jurnal

hlm : 111-118 Abstract Purpose: This study developed a deep learning-based mobile travel recommendation system that provides recommendations for local tourist destinations based on users' favorite travel photos. To provide recommendations, use cosine similarity to measure the similarity score between a person's image and a tourism destination gallery through the tag label vector. Label tags are inferred using an image classifier model run from a mobile user device via Tensorflow Lite. There are 40 tag labels that refer to categories, activities and objects of local tourism destinations. Methods: The model is trained using state-of-the-art mobile deep learning architecture EfficientNet-Lite, which is new in the domain of tourism recommender system. Result: This research has conducted several experiments and obtained an average model accuracy of more than 85%, using EfficientNet-Lite as its basic architecture. The implementation of the system as an Android application is proven to provide excellent recommendations with a Mean Absolute Percentage Error (MAPE) of 5.2%. Novelty: A tourism recommendation system is a crucial solution to help tourists discover more diverse tourism destinations. A content-based approach in a recommender system can be an effective way of recommending items because it looks at the user's preference histories. For a cold-start problem in the tourism domain, where rating data or past access may not be found, we can treat the user's past-travel-photos as the histories data. Besides, the use of photos as an input makes the user experience seamless and more effortless. Informatika SJI 24077658 Perpustakaan Teknik UPI YAI SJI V8N1 May 2021 SJI3a-015 Perpustakaan FT UPI YAI SJI V8N1 May 2021 SJI3b-015 Perpustakaan FT UPI YAI SJI V8N1 May 2021 SJI_V8N1_May_2021.png.png 59002 2023-02-08 14:31:01 2023-02-08 14:31:01 machine generated