Analisis Gambar Sel Darah Berbasis Convolution Neural Network Untuk Mendiagnosis Penyakit Demam Berdarah

Wiga Maulana Baihaqi, Chyntia Raras Ajeng Widiawati, Dila Putri Sabil, Anjar Wati

Abstract


Demam berdarah masih menjadi masalah serius. Banyaknya kasus Demam Berdarah di dunia disebabkan oleh iklim yang tidak stabil dan curah hujan yang tinggi pada musim penghujan, yang berpotensi menjadi sarana perkembangbiakan nyamuk Aides Egypt. Tes darah merupakan alat diagnostik utama untuk mendeteksi beberapa penyakit seperti leukemia, demam berdarah, talasemia dan malaria. Perubahan jumlah sel darah ini dengan jelas mengidentifikasi penyebab penyakit. Penelitian ini berfokus pada sel darah merah dan sel darah putih dalam membantu dokter mendiagnosis pasien dengan virus demam berdarah, dimana Tes Hematologi dalam mendiagnosis demam berdarah memang memperhatikan persentase tingkat jumlah sel darah merah dan sel darah putih. Dalam Tes Hematologi, dilakukan penghitungan Hematokrit dan Hitung Darah Lengkap, yang merupakan metode umum untuk mendiagnosis infeksi dengue. Ukuran trombosit yang kecil membuat teknik ini tidak digunakan dalam penelitian ini. Penelitian ini mengusulkan algoritma Convolutional Neural Network untuk mengenali fitur set data sel darah dan mendeteksi demam berdarah berdasarkan masukan sel darah. Hasil penelitian yang dihasilkan menghasilkan metode dan sistem yang dapat mendiagnosis pasien DBD dengan memanfaatkan citra hapusan sel darah, sehingga dapat mempercepat proses diagnosis dan menghemat biaya.

Kata kunci—demam berdarah, klasifikasi, Convolutional Neural Network


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DOI: http://dx.doi.org/10.31154/cogito.v7i1.308.148-159

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