Analisis Performa Convolutional Neural Networks pada Deteksi Penyakit Malaria Berbasis Citra Mikroskopis

Hanifatus Sa'diyah Widihasaniputri; Dewi Chirzah; Widya  Kurniawan; Akmal  Maulana

doi:10.37729/intek.v8i1.6416

Hanifatus Sa'diyah Widihasaniputri Universitas Muhammadiyah Purworejo
Dewi Chirzah Universitas Muhammadiyah Purworejo
Widya Kurniawan Universitas Darussalam Gontor
Akmal Maulana Universitas Muhammadiyah Purworejo

DOI: https://doi.org/10.37729/intek.v8i1.6416

Keywords: Deteksi Malaria, Convolutional Neural Network, Deep learning;

Abstract

Deteksi otomatis malaria melalui analisis citra mikroskopis sel darah memegang peranan penting dalam diagnosis tepat waktu. Penelitian ini berfokus pada implementasi dan evaluasi model Convolutional Neural Network (CNN) untuk mengklasifikasikan citra sel darah ke dalam kategori 'Tidak Terinfeksi' dan 'Parasit'. Arsitektur CNN, yang terdiri dari lapisan konvolusi, pooling, dan dense, dilatih selama 13 epoch menggunakan optimizer Adam dan fungsi loss Binary Crossentropy. Sebelum pelatihan, citra diubah ukurannya menjadi 128x128 piksel, dikonversi menjadi array NumPy, dan nilai piksel dinormalisasi ke rentang 0-1. Model terlatih mencapai akurasi pelatihan akhir sebesar 91.79% dengan loss 0.3356. Saat dievaluasi pada dataset pengujian yang belum pernah dilihat, model menunjukkan akurasi yang sebanding, yaitu 91.79%, mengindikasikan efektivitasnya dalam mengidentifikasi parasit malaria dalam citra sel darah. Penelitian ini menyoroti potensi signifikan CNN untuk mengembangkan alat diagnostik malaria otomatis dan akurat.

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Analisis Performa Convolutional Neural Networks pada Deteksi Penyakit Malaria Berbasis Citra Mikroskopis

Abstract

References

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