PREDICTION OF SEA LEVEL MEASUREMENT IN PANGPANG BAY FOR SEAPLANE LANDING SEAPLANE LANDING USING ID CONVOLUTIONAL NEURAL NETWORK

Abstract

This research investigates the relationship between sea level height and various environmental factors in Pangpang Bay, Indonesia, using Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) modeling techniques. Daily data on sea level height, weather, and oceanography were collected from April 1 to April 15, 2024. An analysis was conducted on the factors affecting sea level height and the evaluation of predictive model performance. The findings reveal historical patterns of sea level height changes influenced by the variability of meteorological and oceanographic conditions. Although ANN and CNN models have varying degrees of accuracy, both show potential in predicting sea level height by considering environmental factors. Recommendations include the development of more advanced predictive models, deeper data observation, integration of multidisciplinary information, continuous environmental monitoring, and stakeholder collaboration. This research is expected to contribute to the understanding and management of environmental risks related to sea level height in Pangpang Bay.