K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism

The more tourist objects are in an area, the challenging it is for local governments to increase selling value of these attractions. government always strives develop attraction areas by prioritizing beauty However, visitors often have difficulty determining that match their criteria because many choices. research developed a recommendation system applying machine learning techniques. technique used was K-Nearest Neighbor (KNN) method. Several trials were conducted with dataset 315 records, consisting 11 attributes and 21 Based on dataset, preprocessing stage previously carried out improve data format selecting where separated based existing criteria, then calculating closest distance k KNN results divided into five folds each classification highest accuracy obtained at 78% k=1. It shows method can provide recommendations three classes Bangkalan. Applying determines several alternative tourists visit according natural, cultural, religious objects.