Efficiency Test of K-Means Clustering Algorithm on Collaborative Trip Advisory and Planning System

Lim Yen Yee, Seetha Letchumy M. Belaidan, Nor Azlina Abd Rahman and Khalida Shajaratuddur Harun

Fueled by the help of the Internet, more and more tourists nowadays tend to plan trips by themselves in order to have trip plans that meet their preferences and convenience perfectly. However, tourists may face some problems when they plan a trip by themselves, which makes the whole trip planning process challenging and tiring. These problems include extensive tourism information, manually constructing an itinerary for a trip and difficulty in satisfying the needs of all trip participants. Therefore, a web-based trip planning system was developed to solve all these problems in order to help tourists in planning their desired trips more effectively and efficiently. This system helps users to search for attractions and restaurants in Southeast Asian countries faster by providing filtering and prioritizing features. This system also facilitates the decision-making process of tourists when choosing places to visit and restaurants by utilizing the power of word-of-mouth (reviews). Besides, this system aids tourists by reducing the need to manually construct the itinerary for a trip. The k-means clustering algorithm is used to auto-arrange the trip itinerary to ensure places close to each other are arranged to be visited on the same day so that tourists can save on unnecessary transportation costs and time. Lastly, this system promotes collaborative trip planning by providing a platform for all the participants in a trip to discuss and plan their trip together.

Volume 11 | 11-Special Issue

Pages: 937-945

DOI: 10.5373/JARDCS/V11SP11/20193118