Implementasi Sistem Deteksi Golongan Darah Menggunakan Metode Sobel
DOI:
https://doi.org/10.55123/storage.v5i3.8316Keywords:
golongan darah, Sobel, pengolahan citra, Edge Density Ratio, klasifikasiAbstract
Interpretasi golongan darah secara manual sering kali menghadapi kendala subjektivitas dan variasi kondisi lingkungan, sehingga diperlukan sistem otomatisasi yang akurat dan efisien. Penelitian ini mengembangkan sistem deteksi golongan darah otomatis melalui integrasi operator Sobel sebagai ekstraktor fitur dan Random Forest sebagai model klasifikasi. Proses diawali dengan pengolahan citra menggunakan kanal hijau untuk memperjelas batas aglutinasi, kemudian nilai Edge Density Ratio (EDR) diekstraksi dari area reagen Anti-A, Anti-B, dan Anti-D sebagai input klasifikasi. Berdasarkan pengujian terhadap 432 data citra, sistem yang diusulkan mencapai akurasi global sebesar 97,92%. Hasil ini menunjukkan peningkatan signifikan dibandingkan pendekatan rule-based murni yang hanya mencapai akurasi 44,68%. Selain itu, metode ini menawarkan efisiensi komputasi yang lebih baik dibandingkan arsitektur Deep Learning konvensional, sehingga menjadi solusi yang lebih praktis untuk implementasi pada perangkat medis portabel berbasis edge computing tanpa mengorbankan performa.
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