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Every country in the world needs to report its fish production to the Food and Agriculture Organization of the United Nations (FAO) every year. In 2018, Indonesia ranked top five countries in fish production, with 8 million tons globally. Although it ranks top five, the fisheries in Indonesia are mostly dominated by traditional and small industries. Hence, a solution based on computer vision is needed to help detect and classify the fish caught every year. The research presents a method to detect and classify fish on mobile devices using the YOLOv3 model combined with ResNet18 as a backbone. For the experiment, the dataset used is four types of fish gathered from scraping across the Internet and taken from local markets and harbors with a total of 4,000 images. In comparison, two models are used: SSD-VGG and autogenerated model Huawei ExeML. The results show that the YOLOv3-ResNet18 model produces 98.45% accuracy in training and 98.15% in evaluation. The model is also tested on mobile devices and produces a speed of 2,115 ms on Huawei P40 and 3,571 ms on Realme 7. It can be concluded that the research presents a smaller-size model which is suitable for mobile devices while maintaining good accuracy and precision.

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Smart home system aim to maximize surveillance, monitoring, and security. This system is integrated with telecommunications and control systems from the microcontroller to create the Internet of Things (IoT). Nowadays, home appliances are integrated with the Smart System that connected to the internet. On the other hand, messenger applications now integrated with chat bot with Artificial Intelligence to make user easier to communicate. This trend made a possibility to implement a system where a home appliance can be operated by only using a messenger application. In this research, a Smart home system designed with a client-server system based on Raspberry Pi as microcontroller and Telegram Messenger as interface that perform the control communication. The process separated into three stages: design, implementation, and result. The design consists of designing the server, interface, and Smart Home control system. To test the performance, the Messenger Bot are compared with other direct controller application. The result show that the Telegram Messenger application is suitable and more convinient for being the IoT controller.