The COVID-19 pandemic severely impacted Indonesia, leading to substantial socio-economic disruptions and widespread social anxiety. In response, the government implemented a range of measures, including restrictions and mandatory health protocols, to control the virus. Although the pandemic is officially declared over, the risk of infection remains. Therefore, continued adherence to health protocols is essential. This study aimed to identify the key factors driving public compliance with health protocols in the post-pandemic period within Indonesia, providing valuable insights for policymakers to prevent future outbreaks. The findings revealed that knowledge emerged as the most significant determinant of community adherence to ongoing health protocol implementation. Consequently, the government must consistently emphasize the importance of adhering to health protocols whenever individuals are outside their homes.

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.