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      "contact_email": "digilib@unpkdr.ac.id",
      "department": "KODEPRODI55201#Teknik Informatika",
      "date": "2026-07-13",
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      "abstract": "Penelitian ini bertujuan mengembangkan sistem deteksi kelayakan telur berbasis web menggunakan algoritma YOLOv8 untuk membantu proses penyortiran telur yang masih dilakukan secara manual sehingga lebih efektif dan mengurangi kesalahan identifikasi. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan model pengembangan Waterfall yang meliputi tahap analisis kebutuhan, perancangan sistem, implementasi, pengujian, dan pemeliharaan. Sistem dikembangkan menggunakan bahasa pemrograman Python, framework FastAPI, serta model YOLOv8. Dataset diperoleh dari hasil pengambilan citra langsung di peternakan dan dataset publik Roboflow yang telah melalui proses pelabelan, kemudian dibagi menjadi 80% data pelatihan dan 20% data pengujian dengan proses pelatihan selama 100 epoch. \r\nHasil penelitian menunjukkan bahwa sistem berhasil diimplementasikan dalam bentuk aplikasi berbasis web yang mampu mendeteksi dan mengklasifikasikan telur layak dan tidak layak jual secara otomatis melalui kamera maupun unggah gambar, serta menampilkan bounding box, jumlah telur, dan umpan balik suara kepada pengguna. Berdasarkan hasil evaluasi model diperoleh nilai Accuracy 97,8%, Precision 96,7%, Recall 94,6%, dan F1-Score 95,8%, sedangkan pengujian Black Box Testing menunjukkan seluruh fungsi utama sistem berjalan sesuai kebutuhan. Meskipun kecepatan pemrosesan real-time masih berada pada kisaran 4–5 FPS, secara keseluruhan sistem mampu meningkatkan ketepatan penyortiran telur dan mengurangi potensi kesalahan pada pemeriksaan manual.",
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      "title": "PERANCANGAN SISTEM DETEKSI KELAYAKAN TELUR MENGGUNAKAN YOLO BERBASIS WEB",
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      "referencetext": "Khairy, K., & Candra, F. (2025). Evaluasi Kinerja YOLOv8 dalam Klasifikasi Kualitas Telur Berbasis Warna dan Tekstur Cangkang. Oktober,  17(3),  326–339.  https:\/\/www.doi.org\/10.22303\/csrid-\r\n.17.3.2025.326-339\r\nYusri Nur, M., Ramadhani Pratiwi, I., & Kaswar Baso, A. (2021). Identifikasi Kualitas Telur Ayam Berbasis Pengolahan Citra Dan Jaringan. Jessi, 02(1), 33–40.\r\nRisal, Z., Hakim, R., & Abdullah, A. R. (2023). Metode Penelitian dan Pengembangan Research and Development (R&D)–Konsep, Teori- Teori, dan Desain Penelitian.\r\nSzeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). \r\n        Springer. https:\/\/doi.org\/10.1007\/978-3-030-34372-9.\r\n Jocher, G., Chaurasia, A., & Qiu, J. (2023). Ultralytics YOLOv8 (Version 8.0.0) \r\n        [Computer software]. GitHub. https:\/\/github.com\/ultralytics\/ultralytics    \r\nerven, J., & Cordova-Esparza, D. M. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. arXiv:2304.00501.\r\nBun, K., & Hurnaningsih, H. (2020). IDENTIFIKASI TELUR RETAK MENGGUNAKAN  METODE  JARINGAN  SYARAF  TIRUAN\r\nBERDASARKAN TEKSTUR TELUR. Jurnal Ilmiah Teknologi dan Rekayasa, 23(3), 183-192.\r\nGonzalez, R. C., & Woods, R. E. (2018). Digital Image Processing (4th ed.).  \r\nPearson.\r\nSugiyono. (2021). Metode Penelitian dan Pengembangan (Research and \r\n       Development\/R&D). Alfabeta.\r\n\r\n\r\n \r\nYusuf, M. N. Y. M., Ramadhani, I. P., & Kaswar, A. B. (2021). Identifikasi kualitas telur ayam berbasis pengolahan citra digital dan jaringan syaraf tiruan. Journal of Embedded Systems, Security and Intelligent Systems, 33-40.\r\nPutri, H. K., Iswanto, B. H., & Suhendar, H. (2025, January). CLASSIFICATION OF CHICKEN EGG SHELL QUALITY USING EFFICIENTNET BASED ON DIGITAL IMAGES: KLASIFIKASI KUALITAS CANGKANG TELUR AYAM MENGGUNAKAN EFFICIENTNET BERBASIS CITRA DIGITAL. In PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) (Vol. 13).\r\nMaulana, S. (2024). Deteksi Kesegaran Telur Ayam pada Citra Cangkang Menggunakan Metode Convolutional Neural Network (CNN). JIMU: Jurnal Ilmiah Multidisipliner, 2(04), 813-822.\r\nMahardika, F., Purwanto, K. A., & Saputra, D. I. S. (2017). Implementasi Metode Waterfall pada Proses Digitalisasi Citra Analog. VOLT: Jurnal Ilmiah Pendidikan Teknik Elektro, 2(1), 63-72.\r\nSetiaji, P., Adi, K., & Surarso, B. (2024). Development of Classification Method for Determining Chicken Egg Quality Using GLCM-CNN Method.    Ingenierie des Systemes d'Information, 29(2), 397–407. https:\/\/doi.org\/10.18280\/isi.29020.\r\nTechno Nusa Mandiri. (2023). Real Time Detection of Chicken Egg Quantity Using GLCM and SVM Classification Method. Techno Nusa Mandiri: Journal of Computing and Information Technology, 20(2). DOI: 10.33480\/techno.v20i2.4735.\r\nAmani, C. S. (2025). TA: Sistem Deteksi Keretakan Telur Ayam Berbasis Artificial    \r\n       Intelligence Menggunakan ESP32-CAM (Doctoral dissertation, Universitas  \r\n       Dinamika).\r\nRamírez, S. (2023). FastAPI Documentation. https:\/\/fastapi.tiangolo.com\/"
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