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

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.