EXPLORING AIRBNB SATISFACTION IN INDONESIAâS TOP 5 TOURIST DESTINATION CITIES: A MACHINE LEARNING PERSPECTIVE - Abimanyu, Avisenna
This research presents Airbnb customer satisfaction insights in Indonesia using machine learning models on Airbnb data. To develop the models, this research utilizes Airbnb data from Airbnb website as main data for machine learning process through web scraping and supporting analysis data from Central Bureau of Statistics Indonesia (BPS Indonesia). Customer reviews, listings scoring attributes (communication, cleanliness, accuracy, economic value, location, check in, total reviews, area, and price.), and cities are set as predictors. Author use different machine learning methods such as linear regression, sentiment analysis, LDA, and BERT each with its own respective predictor variables. For regression method author utilized 5 distinct data composition by city and for NLP based machine learning methods author utilized 8 distinct data composition: 3 parts by time period from overall data and 5 parts by city from overall data. To evaluate the model, author use root mean square error (RMSE) and mean absolute error (MAE) for linear regression. For LDA, author use log likelihood and perplexity score. For BERT, author use silhouette score.