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All Journal Techno.Com: Jurnal Teknologi Informasi Journal of Soft Computing Exploration
Atmajaya, Gde KM
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Computer Science & IT Engineering

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Integrasi Perangkat Energy Meter iEM3255 Pada Sistem Pemantau Konsumsi Energi Listrik Berbasis Internet of Things (IoT) Menggunakan Komunikasi ModBus Atmajaya, Gde KM; Abdullah, Muhammad Husein; Wahyudi, Aditio; Yuliansyah, Harry
Techno.Com Vol. 24 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i1.12110

Abstract

Energi listrik merupakan salah satu energi yang banyak dimanfaatkan dalam menjalankan segala aktivitas manusia. Konsumsi energi listrik yang tidak terkendali dapat menjadi potensi pemborosan yang dapat merugikan dari segi ekonomi dan lingkungan. Berdasarkan permasalahan tersebut, dirancang suatu sistem pemantau konsumsi energi listrik berbasis Internet of Things (IoT) untuk memantau konsumsi energi listrik. Sistem ini tersusun dari sensor arus, energy meter iEM3255, Mikrokontroller ESP32, perangkat LoRa, dan suplai daya. Komponen – komponen tersebut diintegrasikan menggunakan komunikasi ModBus dan hasil pengukuran dapat dilihat melalui aplikasi smartphone yang dibuat menggunakan platform Kodular. Berdasarkan hasil implementasi dan pengukuran arus, tegangan, dan daya diperoleh nilai error sebesar 3,84%. Berdasarkan hasil tersebut dapat disimpulkan perangkat Energy Meter iEM3255 dapat diintegrasikan dengan sistem pemantau konsumsi energi listrik berbasis IoT dengan menyesuaikan kapasitas beban yang terpasang.   Kata kunci: Sistem Pemantau Energi Listrik, Energy Meter, Komunikasi ModBus, IoT.
Improved human image density detection with comparison of YOLOv8 depth level architecture and drop-out implementation Yulita, Winda; Ramadhani, Uri Arta; Mufidah, Zunanik; Atmajaya, Gde KM; Bagaskara, Radhinka; Kesuma, Rahman Indra; Aprilianda, Mohamad Meazza
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.556

Abstract

Energy inefficiency due to Air Conditioners (AC) running in empty rooms contribute to unnecessary energy consumption and increased CO₂ emissions. This study explores how different depth levels of the YOLOv8 architecture and dropout regularization can enhance human density detection for smarter AC control systems. By evaluating model accuracy through Mean Average Precision (mAP50-95), we provide quantitative insights into how these modifications improve detection performance. Our dataset consists of 1363 images taken in an office environment at ITERA under varying lighting conditions and different human presence densities. The results show that the YOLOv8m model performs best, achieving an mAP50-95 score of 0.814 in training and 0.813 in validation, outperforming other YOLOv8 variants. Furthermore, applying dropout regularization improves model generalization, increasing mAP50-95 from 0.552 to 0.6 and effectively reducing overfitting. This study highlights the balance between architectural depth and dropout regularization in YOLOv8, demonstrating its effectiveness in energy-efficient smart buildings. The findings support the potential of deep learning-based human density detection in improving energy conservation strategies, making it a valuable solution for intelligent automation systems.
Co-Authors Abdullah, Muhammad Husein Aprilianda, Mohamad Meazza Bagaskara, Radhinka Mufidah, Zunanik Rahman Indra Kesuma Ramadhani, Uri Arta Wahyudi, Aditio Yuliansyah, Harry Yulita, Winda