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.

Wakatobi is one of the tourist destinations renowned for its underwater beauty, holding great potential to be developed as a sustainable tourism destination. This research aims to determine the priorities for sustainable tourism development in Wakatobi using the Analytical Hierarchy Process (AHP) method. The AHP method is employed to identify and prioritize various criteria and sub-criteria contributing to sustainable tourism development. Based on the research findings, infrastructure and facilities were found to be the highest priorities, with a focus on improving transportation infrastructure and public facilities. These findings indicate that the development of transportation infrastructure and public facilities is crucial to support the sustainability of tourism in Wakatobi.