Abstract:
In present, the learning of machine and development of digital platform for data service to customer has used widely. Therefore, service businesses, etc. a large amount of purchase data and the business are highly competitive in the market to attain its target customers. How can customer purchase data be analyzed and organize promotional items according to their shopping needs. Creating data association rules from customer purchases can reach customers target and make a competitive advantage. this paper applied machine learning which is called Apriori Algorithm to recommend for community-based tourism product based on user log behavior who are used digital platform. The objectives of this research are 1) applying association rules techniques to develop recommendation system for community-based tourism product and 2) evaluation applying association rules techniques to develop recommendation system for community-based tourism product. This research consisted of 4 steps: (1) data gathering, (2) data preparation, (3) association algorithm, and (4) Visualization, by using association rules techniques and user log behavior collection. In this paper, the example consists of community-based tourism product is equal to 24 piece and users is 80 persons. Moreover, the number of click for testing the interest behavior of tourists is equal to 800 clicks and then the click average of tourists is equal to 10 clicks per person in difference time of them. According to experimental results, as a standard for data mining using Association Rule, applied the Apriori algorithm to find Frequent ItemSet and analyze for relationships can be generated to five association rules based on the support value is equal to 50% and more than 90% for confidence value. As results shown that the process of association rules techniques can applied to development of digital platform which can recommend community-based tourism product to tourists based on their behavior