Purpose: The purpose of this study was to evaluate the role of community in economic sustainability towards post-visit behavior to cultural tourism destinations in the city of Palembang. Research methods: This research is a quantitative study, which examines the relationship between the variables of the role of community, in this case, the community, in the economy sustainability of tourism and several other variables, which is the role of the community in ecological, social, cultural sustainability toward post-visit behavior, in the form of tourist satisfaction. The technique analysis used was multiple regression analysis techniques and the data of this study were collected through a survey in 2019, using a questionnaire of 107 respondents who were tourists who had visited one of the cultural tourism destinations in Palembang. Result and Discussion: The results of this study indicate that the role or participation of community in the sustainability of tourism has a positive and significant effect on visitor or tourist satisfaction Conclusion: the community participation in tourism sustainability is significantly important in order to create tourist satisfaction; hence it’s important to focus on community empowerment.

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Standard fetal heart echocardiography view consists of several specific views that can be prolific to optimize the visualization of various structures and anomalies including three vessel and trachea view, right ventricular outflow tract view, four chamber view, left ventricular outflow tract, and right ventricular outflow tract. With the use of current technological developments specifically deep learning, it can classify images from the visualization of the echocardiography point of view obtained. One of the deep learning models that has the best performance in image recognition and classification is the Convolutional Neural Network. Research consists of several stages, namely data collection, data pre-processing, data augmentation, data sharing designing the Convolutional Neural Network model architecture, training, testing, and results. 5 types of echocardiography videos were used based on the echocardiography point of view, resulting in 3,995 images consisting of 3,196 training data and 799 test data. The implementation of convolutional neural networks for the classification of fetal echocardiography images based on point of view obtained good results. The Convolutional Neural Network used consists of 2 convolution layers, 2 layers, 1 flatten layer, 2 dense layers, and 2 Dropout layers. The accuracy rate obtained from the CNN model with a learning rate value of 0.01 and the number of epochs of 50 gets an accuracy value of 98%.

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