Keywords
deteksi anomali
deteksi malaria
semi-supervised
SSIM
How to Cite
Abstract
Sebuah convolutional autoencoder diterapkan dalam kerangka deteksi anomali semi-supervised untuk mengidentifikasi sel darah merah yang terinfeksi malaria. Model dilatih secara eksklusif menggunakan citra sel normal guna menangkap ciri struktural alaminya, sehingga memungkinkan deteksi anomali berdasarkan perbedaan rekonstruksi yang diukur menggunakan Structural Similarity Index Measure (SSIM). Threshold optimal ditentukan melalui analisis ROC dan statistik Youden’s J. Evaluasi pada dataset uji yang seimbang menghasilkan akurasi sebesar 76%, dengan presisi 76,5% dan recall 76%, yang menyoroti tantangan dalam mendeteksi variasi patologis yang halus. Hasil menunjukkan potensi unsupervised representation learning dalam deteksi malaria, sekaligus menyarankan bahwa penelitian di masa depan sebaiknya difokuskan pada peningkatan sensitivitas dan robustnes melalui ekstraksi fitur yang lebih canggih dan teknik hybrid supervised-unsupervised.
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