This study explores the impact of tourism on environmental health in the coastal tourist area of Ancol Beach, Jakarta. As a major tourist destination in North Jakarta, Ancol Beach has experienced significant growth in visitor numbers annually. While tourism in this area provides positive contributions such as boosting the local economy and creating employment opportunities, it also poses negative environmental impacts, particularly in terms of marine pollution and increased waste accumulation during peak tourist seasons. Using a qualitative approach, this research combines in- depth interviews and field observations to analyze these issues. The results indicate that, despite adequate waste management efforts, the surge in visitors creates challenges in maintaining environmental cleanliness and marine ecosystem health. Additionally, tourists' awareness of environmental responsibility tends to decline during high-traffic periods. This study recommends enhancing waste management facilities and intensifying environmental education to support sustainable tourism management at Ancol Beach.
 

This research focuses on car classification and the use of the ResNet-50 neural network architecture to improve the accuracy and reliability of car detection systems. Indonesia, as one of the countries with high daily mobility, has a majority of the population using cars as the main mode of transportation. Along with the increasing use of cars in Indonesia, many automotive industries have built factories in this country, so the cars used are either local or imported. The importance of car classification in traffic management is a major concern, and vehicle make and model recognition plays an important role in traffic monitoring. This study uses the Vehicle images dataset which contains high-resolution images of cars taken from the highway with varying viewing angles and frame rates. This data is used to analyze the best- selling car brands and build car classifications based on output or categories that consumers are interested in. Digital image processing methods, machine learning, and artificial neural networks are used in the development of automatic and real-time car detection systems.The ResNet-50 architecture was chosen because of its ability to overcome performance degradation problems and study complex and abstract features from car images. Residual blocks in the ResNet architecture allow a direct flow of information from the input layer to the output layer, overcoming the performance degradation problem common in neural networks. In this paper, we explain the basic concepts of ResNet-50 in car detection and popular techniques such as optimization, augmentation, and learning rate to improve performance and accuracy. in this study, it is proved that ResNet has a fairly high accuracy of 95%, 92% precision, 93% recall, and 92% F1-Score.

The purpose of this research is to analyze the acceptance of the Learning Management System as the Cloud Storage for students in digital-based learning at Universitas Kristen Satya Wacana. The research is conducted using a descriptive quantitative research design. The data is acquired from 250 students of Universitas Kristen Satya Wacana who participate in the online lesson using the Cloud Storage in the Learning Management System. The technique of data collection is by using a closed questionnaire through random sampling. The acceptance of Cloud Storage usage is measured using Technology Acceptance Model (TAM). The result of the research shows a high outcome. During online lessons, the students use Cloud Storage in LMS which is integrated with the data storage feature in form of lesson materials, assignment files, teacher-students discussion forums, presence lists, meeting links, etc. Cloud Storage usage in LMS provides convenience, and benefit gain so that it can increase class performance which is effective and efficient, convenient, and fun, and it will trigger the interest in actual use of digital platforms.