Perancangan dan Implementasi Sistem Klasifikasi Citra Histopatologi Kanker Payudara Berbasis kombinasi metode GLCM dan EfficientNet

Doni Prasetyo

doi:10.23960/jitet.v14i1.9250

Doni Prasetyo
Universitas Islam Malang

DOI: https://doi.org/10.23960/jitet.v14i1.9250

Keywords Hybrid Deep Learning; Image Clasification; Texture Feature Extraction;.

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Abstract

Kanker payudara memerlukan metode diagnosis yang akurat dan objektif untuk mendukung deteksi dini. Penelitian ini mengusulkan sistem klasifikasi citra histopatologi kanker payudara menggunakan metode Gray Level Co-occurrence Matrix (GLCM), EfficientNet, serta pendekatan hybrid yang mengombinasikan keduanya. Hasil pengujian menunjukkan bahwa metode GLCM menghasilkan akurasi validasi sebesar 74%, sementara EfficientNet mencapai akurasi hingga 97%. Pendekatan hybrid GLCM–EfficientNet memberikan performa terbaik dengan akurasi mendekati 98% dan tingkat kesalahan klasifikasi yang sangat rendah. Temuan ini menunjukkan bahwa integrasi fitur tekstur dan fitur deep learning mampu meningkatkan kinerja klasifikasi secara signifikan. Penelitian selanjutnya diarahkan pada validasi klinis dan pengembangan sistem berbasis aplikasi untuk mendukung diagnosis medis.

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