Analisis Kalman filter berbasis Google Trends untuk Prediksi Kedatangan Wisatawan Mancanegara di Indonesia Pasca Pandemi

Pada tahun 2019 kunjungan wisatawan mancanegara (wisman) ke Indonesia mengalami peningkatan yang cukup signifikan. Sehingga, pariwisata diprediksi menjadi salah satu penopang terbesar dari penerimaan negara. Namun, saat wabah Coronavirus terjadi di akhir tahun 2019, sektor ini menjadi sektor industri yang paling terdampak dengan penurunan yang sangat tajam dan perkirakan akan membaik sekitar tahun 2035 hingga 2045. Kejadian tersebut mendorong penelitian untuk merumuskan model proyeksi terbaik bagi wisatawan asing pasca pandemi dengan menggunakan metode Kalman filter. Kalman filter merupakan model state space yang dapat diulang untuk menghasilkan nilai akurasi estimasi yang tinggi. Model ini didukung oleh analisis google trends yang mampu menangkap minat negara lain terhadap pariwisata Indonesia, terutama di masa pandemi. Hasil penelitian menunjukkan bahwa meskipun pandemi, beberapa negara masih memiliki minat terhadap objek wisata di Indonesia. Selain itu, Kalmanfilter memiliki akurasi yang tinggi dalam peramalan wisatawan asin

Deep learning approach and topic modelling for forecasting tourist arrivals

Online review data attracts the attention of researchers and practitioners in various fields, but its application in tourism is still limited. The social media data can finely reflect tourist arrivals forecasting. Accurate prediction of tourist arrivals is essential for tourism decision-makers. Although current studies have exploited deep learning and internet data (especially search engine data) to anticipate tourism demand more precisely, few have examined the viability of using social media data and deep learning algorithms to predict tourism demand. This study aims to find the key topics extracted from online reviews and integrate them into the deep learning model to forecast tourism demand. We present a novel forecasting model based on TripAdvisor reviews. Latent topics and their associated keywords are captured from reviews through Latent Dirichlet Allocation (LDA), These generated features are then employed as an additional feature into the deep learning (DL) algorithm to forecast the monthly tourist arrivals to Hong Kong from USA. We used machine learning models, artificial neural networks (ANNs), support vector regression (SVR), and random forest (RF) as benchmark models. The empirical results show that the proposed forecasting model is more accurate than other models, which rely only on historical data. Furthermore, our findings indicate that integration of the topics extracted from social media reviews can enhance the prediction

Growing Business in Live Commerce: A Tripartite Perspective and Product Heterogeneity

Live streaming becomes an important channel helping organizations and individual sellers boost their sales. Our research takes an integrated perspective and examines the simultaneous influences of streamers-, consumers-, and products-related factors on sales volume in live commerce. We apply multiple linear regression to analyze a panel data set collected from Taobao live in Double 11, 2020, which contained 34,925 product sales records. We find that streamers’ social capital, consumers’ engagement, and products’ live demonstration all significantly contribute to product sales volume. In addition, product heterogeneity matters in live commerce such that the effects of streamers’ social capital and products’ live demonstration on sales volume work only for experience products (not for search products) and for the products with less popular brands (not for the products with popular brands). Our research offers comprehensive insights for both researchers and practitioners on how to grow business in live commerce

una predicción mediante inteligencia artificial para los hoteles en Cartagena

145 - 161 p. :ilustraciones, libro electrónicoEste capítulo identifica las variables que permiten explicar la recomendación o no de los hoteles, de usuarios en la ciudad de Cartagena, a partir de algoritmos de inteligencia artificial. Se identificaron los algoritmos que mejor desempeño tenían: Random Forest (100%), SVM (100%), KNN (98%), AdaBoost (94%), árbol de decisión (92%), redes neuronales (86%) y CN2 (82%). Para las alternativas de tal vez recomendaría y no recomendaría, no hubo algoritmos con capacidad de predicción superior al 75%.Capítulo 6ISBN: 978958580477

Using social media big data for tourist demand forecasting: A new machine learning analytical approach

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing