<![CDATA[In this digital era, the recommendation system is an important tool in helping users to make decisions, including choosing tourist destinations. This study aims to implement the K-Nearest Neighbors (k-NN) algorithm in determining the recommendation pattern for tourist destinations. The k-NN algorithm was chosen because of its ability to classify based on the closest neighbor of a point, so that it can obtain recommendation patterns that are more personal and relevant to user preferences. This implementation method involves data collection, data preparation, model building, classification process, model evaluation, and finally implementation of the model in a recommendation system. The results of this study indicate that a recommendation system based on the k-NN algorithm is able to provide suggestions for tourist destinations that are more in line with user preferences, thus potentially increasing user satisfaction and making a positive contribution to the tourism industry. However, the effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens opportunities for further research and development in the field of tourist destination recommendation systems using other algorithms or combining several algorithms to obtain more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is essential in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results. The effectiveness of this model is highly dependent on the quality and quantity of data used. Therefore, good data collection and preparation is very important in the implementation of this algorithm. This research opens up opportunities for further research and development in the field of tourist destination recommendation systems by using other algorithms or combining several algorithms to get more optimal results.]]>
