Optimizing Battery Consumption for Android-based Automatic Identification System (AIS) Application for Indonesian Fishermen

Fishing is integral to Indonesia's economy and the livelihood of many citizens. Indonesian fishermen often spend long periods at sea with limited access to power, making energy efficiency crucial for mobile applications, especially those used for navigation and safety. AIS is vital for maritime navigation, providing real-time information on vessel locations, weather, and emergencies. However, high battery consumption can cause AIS apps to fail when needed most. This study aims to optimize battery consumption for a mobile app used by Indonesian fishermen by implementing techniques like batch data transmission, efficient background task management, and optimized location services. The effectiveness of these techniques was evaluated based on key performance indicators such as battery usage, latency, data transmission, and task completion rates. The results show a significant reduction in average battery usage from 3000 mAh to 2000 mAh and improved application performance with latency decreasing from 150 ms to 120 ms. Data transmission reduced by 10% and task completion rates increased from 85% to 95%. Further analysis revealed that Wi-Fi networks are more energy-efficient than cellular networks, with average battery usage of 1800 mAh compared to 2200 mAh. Enhancing the energy efficiency of mobile applications is crucial for users in remote areas with limited power resources. The implemented techniques improved the reliability and efficiency of the AIS application, thereby enhancing the safety and operational effectiveness of Indonesian fishermen at sea.