<![CDATA[This study aims to develop an Augmented Reality (AR) application integrated with a Convolutional Neural Network (CNN) as an interactive system for detecting damage in floating solar power plant (PLTS) panels at Embung Sidobandung in order to maintain the efficiency of the photovoltaic energy system. Conventional manual inspection methods are considered inefficient and prone to errors due to human factors. Therefore, a deep learning approach is employed to automatically and interactively detect and classify solar panel damage. AR technology is utilized to display panel condition information directly through a mobile device camera, enabling real-time damage monitoring. The dataset consists of 615 solar panel images, including 472 images of physical damage and 143 images of electrical damage. Experimental results show that the system is capable of classifying solar panel damage types in real time, achieving a precision of 93.48%, recall of 89.58%, and an F1-score of 91.49% for physical damage, and a precision of 70.59%, recall of 80.00%, and an F1-score of 75.00% for electrical damage, with an overall accuracy of 87.30%. Although the developed application provides interactive and informative visualization, varying lighting conditions in aquatic environments and differences in image acquisition angles remain challenges that affect system accuracy. Overall, the integration of CNN and AR has the potential to serve as an effective and efficient solution for developing damage detection systems for floating solar power plant (PLTS) panels.]]>
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