Alannurochman, Iqbal (2025) SISTEM DIGITALISASI DATA PASIEN KLINIK BHAKTI MEDIKA DENGAN IMAGE PROCESSING OCR (OPTICAL CHARACTER RECOGNITION) MENGGUNAKAN TESSERACT. S1 thesis, Universitas Mercu Buana-Menteng.
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Abstract
Digitalisasi data pasien menjadi kebutuhan penting dalam meningkatkan efisiensi dan akurasi pengelolaan informasi di fasilitas kesehatan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem digitalisasi data pasien pada Klinik Bhakti Medika menggunakan web framework CodeIgniter, database MySQL, dan teknologi Optical Character Recognition (OCR) berbasis Tesseract. Sistem ini dirancang untuk memungkinkan pengguna mengunggah gambar dokumen medis melalui antarmuka web, mengekstraksi teks secara otomatis menggunakan Tesseract, dan menyimpannya dalam database terstruktur. Metode yang digunakan mencakup observasi, wawancara, dokumentasi, serta pengujian sistem dengan pendekatan black-box. Hasil penelitian menunjukkan bahwa sistem berhasil meningkatkan efisiensi pengolahan data, mempercepat akses informasi, serta mengurangi kesalahan input manual. Penggunaan Tesseract OCR terbukti efektif dalam mengekstraksi informasi dari gambar berkualitas baik, dengan validasi menggunakan Confusion Matrix untuk mengukur akurasi hasil ekstraksi. Sistem ini diharapkan dapat menjadi solusi praktis dalam mendukung transformasi digital layanan kesehatan. The digitization of patient data has become an essential need in improving the efficiency and accuracy of information management in healthcare facilities. This study aims to design and implement a patient data digitization system at Bhakti Medika Clinic using the CodeIgniter web framework, a MySQL database, and Optical Character Recognition (OCR) technology based on Tesseract. The system is designed to allow users to upload medical document images through a web interface, automatically extract text using Tesseract, and store the results in a structured database. The methods used in this research include observation, interviews, documentation, and system testing using a black-box approach. The results show that the system successfully improves data processing efficiency, accelerates information access, and reduces manual input errors. The use of Tesseract OCR has proven effective in extracting information from high-quality images, with validation conducted using a Confusion Matrix to measure the accuracy of the extraction results. This system is expected to serve as a practical solution in supporting the digital transformation of healthcare services.
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