COMPARISON OF K-NEAREST NEIGHBOR AND NEURAL NETWORK FOR PREDICTION INTERNATIONAL VISITOR IN EAST JAVA

Abstract

Tourism is one of the government's priority sectors for economic growth. East Java is one of Indonesia's provinces and is attractive to international visitors. International visitors will appreciate the natural beauty and multiculturalism offered by East Java. In this study, predictions of international visitor visits in East Java from the entrance of Juanda International Airport were carried out using k-NN (k-Nearest Neighbor) and a neural network. The dataset used is based on BPS statistics of Jawa Timur Province in the form of the number of international visitor arrivals from January 2000 to February 2024. The datasets were distributed by dividing the data into 70% for training data and 30% for testing data. The creation of the k-NN model is carried out using k-values 2 to 7. The creation of a modern neural network using hidden layers 1 to 3. The prediction results that were made using k-NN obtained optimal RMSE at k-values 2, resulting in an RMSE of 1594,674 or an error of 3,98%. Meanwhile, the prediction results that have been made using neural networks obtained optimal RMSE at two hidden layers, which resulted in an RMSE of 1873, 355 or an error of 4,68%. So, it is recommended that the k-NN algorithm be used to predict the number of international visitors in East Java. The results of this study can be used to provide quantitative information for the government and stakeholders in adjusting the program to the development of international visitors visiting East Java.