Optimasi Prediksi Jumlah Wisatawan Nusantara ke Provinsi Bali Melalui Big Data Analytics dengan Integrasi Google Trends dan Tingkat Penghunian Kamar Hotel - Prayoga, Suhendra Widi; Wijayanto, Arie Wahyu (Politeknik Statistika STIS, 2024)
The tourism sector plays an important role in the Indonesian economy. Bali, as a major tourist destination, attracts a large number of domestic tourists, which has a significant impact on the local economy. However, providing accurate and real time data remains a challenge. This data limitation makes it difficult to effectively monitor tourism conditions. Therefore, this research optimises the prediction of the number of domestic tourists to Bali using hotel room occupancy rate and Google Trends index. Real-time hotel availability and search interest play an important role in this prediction. The application of big data analytics allows the analysis of large amounts of data quickly and accurately. The results show that the best model is Support Vector Regression with Mean Absolute Percentage Error, Root Mean Square Error, and Mean Absolute Error of 14.8366, 94.5575, and 77.1152, respectively. This prediction is expected to help stakeholders monitor the condition of Bali tourism.