In gastronomic tourism, food is viewed as the central tourist attraction. Specifically, indigenous food is known to represent the expression of local culture and identity. To promote gastronomic tourism, it is critical to have a model for the food business analytics system. This research undertakes an empirical evaluation of recent transfer learning models for deep learning feature extraction for a food recognition model. The VIREO-Food172 Dataset and a newly established Sabah Food Dataset are used to evaluate the food recognition model. Afterwards, the model is implemented into a web application system as an attempt to automate food recognition. In this model, a fully connected layer with 11 and 10 Softmax neurons is used as the classifier for food categories in both datasets. Six pre-trained Convolutional Neural Network (CNN) models are evaluated as the feature extractors to extract essential features from food images. From the evaluation, the research found that the EfficientNet feature extractor-based and CNN classifier achieved the highest classification accuracy of 94.01% on the Sabah Food Dataset and 86.57% on VIREO-Food172 Dataset. EFFNet as a feature representation outperformed Xception in terms of overall performance. However, Xception can be considered despite some accuracy performance drawback if computational speed and memory space usage are more important than performance.

The gastronomy tourism industry plays an important role in boosting local economies, enhancing the travel experience, and preserving culinary traditions unique to specific places. In this context, comprehending customer sentiments is of paramount importance for business decisions making, menu choices offering, marketing strategies, and customer service improvements. Traditional sentiment analysis methods in gastronomy tourism tend to be time-consuming, prone to human error, and influenced by subjectivity. Furthermore, the absence of an effective visualization strategy hampers the reliability of sentiment analysis efforts. Compounding this, the data collected often lacks balance across sentiment classes, making it challenging to predict minority sentiments accurately. To address these challenges, our research introduces a hybrid approach, combining various lexicon-based sentiment and emotional analysis algorithms, thereby enhancing the reliability of customer review analysis in the gastronomy tourism sector. Subsequently, we optimize machine learning sentiment classification by employing data augmentation in conjunction with feature engineering strategies, with the goal of improving the recognition of minority sentiment classes. Additionally, we present a comprehensive business intelligence and visualization solution that personalized for the gastronomy tourism industry in Sarawak that offering real-time sentiment visualization. The optimization of sentiment classification, achieved through the integration of synonym augmentation and n-gram feature engineering in conjunction with kNN classifiers, has yielded impressive results. This approach attains optimal classification performance, boasting an accuracy rate of 0.98, an F1-score of 0.99, and a ROC-AUC score of 0.99. Notably, this methodology significantly enhances the recognition of minority sentiment classes within the dataset, addressing one of the main challenges in this research endeavor.

The increasing living costs have put a strain on people's ability to afford necessities like housing, transportation, education, and healthcare, which affects the quality of life in many countries. These factors have led to a rapidly growing trend of digital freelancing activities that utilize crowdsourcing technology to provide people with opportunities to earn additional income. However, a comprehensive understanding of the digital crowdsourcing landscape is hampered by the lack of studies examining it through a geographical lens, freelancer performance metrics, skillsets, and platform information. Therefore, this study addresses this issue by scraping data from various crowdsourcing platforms to analyze digital freelancing trends, particularly in Malaysia. Using descriptive analytics and visualization techniques, this study has revealed insights into the geographical distribution of Malaysian freelancers, freelancer skillsets in demand, platform preferences among freelancers, and factors influencing freelancer performance. The findings highlight the growing economic significance of digital freelancing and the role of crowdsourcing platforms in shaping the industry. Key information gaps were identified, including limited insights into freelancer demographics and performance metrics. These findings can empower individuals, inform stakeholders, and stimulate further research, ultimately contributing to the development of a more robust, inclusive, and thriving digital freelancing ecosystem in Malaysia.