SISTEM DETEKSI KERUSAKAN PANEL PLTS APUNG DI EMBUNG SIDOBANDUNG BERBASIS CONVOLUTIONAL NEURAL NETWORK DENGAN VISUALISASI AUGMENTED REALITY

Thomas Brian; Immanuel Freddy Augustino; Parman Parman; Muhamad Sukarno

doi:10.46764/teknimedia.v7i1.364

Thomas Brian Politeknik Perkapalan Negeri Surabaya
Immanuel Freddy Augustino Politeknik Perkapalan Negeri Surabaya
Parman Parman Politeknik Perkapalan Negeri Surabaya
Muhamad Sukarno Politeknik Perkapalan Negeri Surabaya

DOI: https://doi.org/10.46764/teknimedia.v7i1.364

Keywords: Augmented Reality, Convolutional Neural Network, Damage Detection, Floating Photovoltaics, Small Reservoir

Abstract

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.

Author Biographies

Thomas Brian, Politeknik Perkapalan Negeri Surabaya

Teknik Kelistrikan Kapal

Immanuel Freddy Augustino, Politeknik Perkapalan Negeri Surabaya

Teknik Permesinan Kapal

Parman Parman, Politeknik Perkapalan Negeri Surabaya

Teknik Bangunan Kapal

Muhamad Sukarno, Politeknik Perkapalan Negeri Surabaya

Teknik Bangunan Kapal

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DAMAGE DETECTION SYSTEM FOR FLOATING SOLAR PANELS AT SIDOBANDUNG POND BASED ON CONVOLUTIONAL NEURAL NETWORKS WITH AUGMENTED REALITY VISUALIZATION

Abstract

Author Biographies

References