Sultansyah, Muhammad Al Hakiim Fakhri (2026) Sistem Monitoring Konsumsi Energi Listrik Berbasis Esp32-Cam dan Visi Komputer pada Meteran PLN. Other thesis, Institut Teknologi Sepuluh Nompember.
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Abstract
Listrik merupakan kebutuhan utama bagi masyarakat Indonesia, di mana setiap rumah tangga memiliki meteran listrik prabayar yang menggunakan sistem token. Untuk mendapatkan sejumlah KWH, masyarakat harus membeli token listrik, dan apabila KWH habis maka aliran listrik di rumah akan terputus. Kondisi ini menuntut pengguna untuk rutin memantau sisa KWH pada meteran agar tidak mengalami pemadaman mendadak. Namun, pemantauan masih dilakukan secara manual dengan melihat angka pada display meteran, sehingga dinilai kurang praktis dan rawan terabaikan. Penelitian ini mengusulkan pengembangan sistem monitoring KWH pada meteran listrik berbasis visi komputer dengan integrasi IoT. Modul ESP32-CAM digunakan untuk menangkap citra display meteran listrik, kemudian citra dikirim ke server IoT untuk diproses menggunakan model pengenalan karakter. Hasil pembacaan angka KWH secara otomatis dikonversi menjadi data konsumsi listrik yang dapat dipantau secara real-time melalui Text localization IoT. Sistem ini dirancang untuk memberikan alternatif pemantauan KWH yang lebih praktis dan akurat dibandingkan pencatatan manual, serta membuka peluang integrasi dengan sistem pengisian token listrik otomatis pada penelitian sebelumnya.
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Electricity is a primary necessity for Indonesian households, where each residence is equipped with a prepaid electricity meter using a token system. To obtain a certain amount of KWH, users must purchase electricity tokens, and once the KWH is depleted, the household’s electricity supply will be automatically cut off. This condition requires users to regularly monitor the remaining KWH on the meter to avoid unexpected power outages. However, monitoring is still carried out manually by checking the meter display, which is considered impractical and prone to being overlooked. This research proposes the development of a KWH monitoring system on electricity meters based on computer vision with IoT integration. The ESP32-CAM module is used to capture images of the electricity meter display, which are then transmitted to an IoT server for processing with a character recognition model. The automatically recognized KWH digits are converted into electricity consumption data that can be monitored in real time via an IoT dashboard. This system is designed to provide a more practical and accurate alternative to manual monitoring while also opening opportunities for integration with the automatic electricity token filling system from previous research.
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