AI in Tourism: Leveraging Machine Learning in Predicting Tourist Arrivals in Philippines using Artificial Neural Network

Tourism is one of the most prominent and rapidly expanding sectors that contribute significantly to the growth of a country's economy. However, the tourism industry has been most adversely affected during the coronavirus pandemic. Thus, a reliable and accurate time series prediction of tourist arrivals is necessary in making decisions and strategies to develop the competitiveness and economic growth of the tourism industry. In this sense, this research aims to examine the predictive capability of artificial neural networks model, a popular machine learning technique, using the actual tourism statistics of the Philippines from 2008-2022. The model was trained using three distinct data compositions and was evaluated utilizing different time series evaluation metrics, to identify the factors affecting the model performance and determine its accuracy in predicting arrivals. The findings revealed that the ANN model is reliable in predicting tourist arrivals, with an R-squared value and MAPE of 0.926 and 13.9%, respectively. Furthermore, it was determined that adding training sets that contain the unexpected phenomenon, like COVID-19 pandemic, increased the prediction model's accuracy and learning process. As the technique proves it prediction accuracy, it would be a useful tool for the government, tourism stakeholders, and investors among others, to enhance strategic and investment decisions © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

"Sana All": Netizens' Perception of Government Responses to COVID-19

Netizens posted views that contradicted the results released by research agencies about the Philippine government's responses to COVID-19. In this study, Twitter, which is a key communication channels, was the main source of data to explore the public's perception of the Philippine government's performance to the pandemic response. To limit tweets to be studied, sana all, a language phenomenon mostly used at the time of community lockdowns, was observed and utilized as a code identify relevant tweets. Between March and August 2020, 257 tweets were collected and researchers used presuppositions to extract socio-political context and truths implied in tweets. Then, the data underwent a 6-level thematic analysis and eleven categories were formed. The prevalent language intention emerging from the tweets is empathy. This paper will discuss how empathy associates the sound dissatisfaction of the netizens with the responses made by the current administration to combat the COVID-19 multi-effects.