Neuromorphic Computing Model Based on Spiking Neural Network for an Efficient and Resilient Tsunami Early Warning System in Indonesia’s Small Islands - Jayaun Jayaun (LPPM Sekolah Tinggi Ilmu Ekonomi Ganesha, 2025)
This study aims to develop a fault-tolerant neuromorphic computing system for tsunami early detection in Indonesia’s small islands, which face significant limitations in energy and network infrastructure. The research was conducted over a three-month period (January–March 2025) using a simulated experimental approach with ocean wave data obtained from BMKG and NOAA. The system model was designed using a Spiking Neural Network (SNN) that mimics biological neuron activity to adaptively recognize ocean wave anomaly patterns. Simulation results show a detection accuracy rate of 94%, maintaining stable performance above 85% even under 25% signal interference. Furthermore, the system’s power consumption was recorded at only 0.42 watts—approximately 40–60% more efficient than conventional CNN-based models. The implications of this study include scientific contributions to the development of adaptive and energy-efficient artificial intelligence, as well as practical benefits for agencies such as BMKG and BNPB in designing autonomous and resilient tsunami early warning systems for remote and underdeveloped regions. In the future, this system has the potential to serve as a prototype for edge computing–based disaster mitigation solutions powered by artificial intelligence, particularly relevant for archipelagic nations.