<![CDATA[ABSTRAKPenelitian ini mengkaji pengembangan sistem konversi tulisan tangan ke teks digital menggunakan metode deep learning dengan mengombinasikan arsitektur Convolutional Neural Network (CNN), YOLOv8, dan Connectionist Temporal Classification (CTC). Dataset yang digunakan terdiri dari 700 citra tulisan tangan huruf kapital (A–Z) yang diperoleh dari dokumen resmi Dinas Kependudukan dan Pencatatan Sipil Kabupaten Barru. Tahapan penelitian meliputi prapemrosesan citra berupa grayscale, normalisasi, perataan teks, serta augmentasi data, dilanjutkan dengan anotasi bounding box menggunakan Roboflow. Dataset kemudian dibagi menjadi data pelatihan, validasi, dan pengujian. Model YOLOv8 dilatih untuk mendeteksi karakter dan hasilnya diproses menggunakan CTC untuk menghasilkan teks akhir. Evaluasi menunjukkan performa yang baik dengan precision 98,38%, recall 87,25%, F1-score 92,44%, serta mAP@0.5 sebesar 87,19%. Hasil ini menunjukkan metode yang diusulkan efektif untuk mendukung digitalisasi dokumen administrasi publik.Kata Kunci: YOLOv8, Konversi Tulisan Tangan, Deep Learning, Citra Digital, Administrasi Publik, Roboflow, CNN, CTC ABSTRACTThis study investigates the development of a handwritten text-to-digital text conversion system using deep learning by combining Convolutional Neural Network (CNN), YOLOv8, and Connectionist Temporal Classification (CTC) architectures. The dataset consists of 700 images of uppercase handwritten letters (A–Z) obtained from official documents of the Department of Population and Civil Registration of Barru Regency. The research stages include image preprocessing such as grayscale conversion, normalization, text alignment, and data augmentation, followed by bounding box annotation using Roboflow. The dataset is then divided into training, validation, and testing sets. The YOLOv8 model is trained to detect characters, and the outputs are processed using CTC to generate the final text. Evaluation results demonstrate strong performance, achieving a precision of 98.38%, recall of 87.25%, an F1-score of 92.44%, and an mAP@0.5 of 87.19%. These findings indicate that the proposed method is effective in supporting the digitalization of public administrative documents.Keyworsds: YOLOv8, Handwriting Conversion, Deep Learning, Digital Image, Public Administration, Roboflow, CNN, CTC ]]>
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