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Junervin
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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DETEKSI DAN KLASIFIKASI OTOMATIS LAPISAN GRAFENA BERBASIS YOLOV11 Junervin
Jurnal Tera Vol 4 No 2 (2024): Jurnal Tera (September 2024)
Publisher : Fakultas Teknik dan Informatika, Universitas Dian Nusantara

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Abstract

Deteksi dan klasifikasi lapisan grafena secara otomatis merupakan tantangan dalam analisis material berbasis kecerdasan buatan. Penelitian ini bertujuan untuk mengembangkan model deteksi berbasis YOLOv11 yang mampu mengidentifikasi dan mengklasifikasikan grafena dalam berbagai ketebalan lapisan. Dataset anotasi terdiri dari empat kelas: 1-Layer, 2-Layer, 3-Layer, dan 4-Layer. Model dilatih dan diuji menggunakan berbagai skenario eksperimental untuk memastikan akurasi dan kecepatan deteksinya. Hasil evaluasi menunjukkan bahwa model mencapai precision sebesar 0,68, recall 0,623, mAP50 0,69, dan mAP50-95 0,45, dengan kategori 1-Layer menunjukkan performa terbaik. Model ini mampu mengklasifikasikan lapisan grafena secara andal dengan efisiensi tinggi, memungkinkan aplikasi dalam analisis material secara real-time. Dengan kecepatan inferensi 40,4 ms per gambar, model ini sangat potensial untuk digunakan dalam berbagai industri berbasis material maju. Kontribusi utama penelitian ini adalah menghadirkan solusi berbasis deep learning yang dapat meningkatkan efisiensi dan akurasi deteksi nanopartikel, mendukung perkembangan teknologi dalam rekayasa dan ilmu material.
Segmentasi Otomatis Nanopartikel pada Nanokomposit Karbon Menggunakan U-Net Junervin; Rambe, Syamsuwarni; Azmi, Silmi; Hakim, Muhammad Luqmanul; Alu, Amina Kurniasi
U-NET Jurnal Teknik Informatika Vol. 8 No. 2 (2024): U-NET Jurnal Teknik Informatika | Agustus
Publisher : LPPM Universitas Al Washliyah Labuhanbatu

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This research aims to develop an automated approach for nanoparticle segmentation within carbon composites using a U-Net model. Nanoparticles in carbon composites are critical for enhancing the mechanical and electrical properties of these materials, but manual detection and segmentation are challenging due to their minute size and dispersed distribution. In this study, a U-Net model with an encoder-decoder architecture was employed to segment scanning electron microscope (SEM) images of palladium-carbon (Pd/C) nanoparticles. The dataset comprised 750 SEM images, exhibiting diverse nanoparticle shapes and sizes. Preprocessing steps included image cropping to eliminate irrelevant regions and the application of Otsu Thresholding to generate ground truth segmentation masks. Model performance was assessed using metrics such as Intersection over Union (IoU), accuracy, and loss. The U-Net model demonstrated high segmentation accuracy, achieving rates between 92% and 95% after 20 training epochs. Additionally, the model was deployed via a Flask web application for real-time prediction. This work significantly advances the efficiency and accuracy of nanoparticle segmentation, offering promising applications in material science and industrial research.
Co-Authors Alu, Amina Kurniasi Luqmanul Hakim Rambe, Syamsuwarni Silmi Azmi