Deteksi Leukemia Limfoblastik Akut menggunakan Convolutional Neural Network

Penulis

  • Mutaqin Akbar Universitas Mercu Buana Yogyakarta
  • Putri Taqwa Prasetyaningrum Universitas Mercu Buana Yogyakarta
  • Putry Wahyu Setyaningsih Universitas Mercu Buana Yogyakarta
  • Moh Ahsan Universitas PGRI Kanjuruhan Malang
  • Alexius Endy Budianto Universitas PGRI Kanjuruhan Malang

DOI:

https://doi.org/10.32493/jtsi.v7i1.34168

Kata Kunci:

convolutional neural network; klasifikasi; leukemia limfoblastik akut

Abstrak

Leukemia limfoblastik akut merupakan jenis leukemia anak yang paling penting, dan menyumbang 25% dari kanker anak. Membedakan secara akurat prekursor sel normal dari sel kanker adalah kunci diagnosis leukemia limfoblastik akut. Namun, di bawah mikroskop, sel kanker sangat mirip dengan sel normal sehingga sulit untuk mengklasifikasikannya. Artikel ini menyajikan deteksi sel leukemia limfoblastik akut menggunakan Convolutional Neural Network (CNN). Dataset diperoleh dari ALL_IDB sejumlah 582 data citra berwarna yang dibagi menjadi 482 data citra latih dan 100 data citra uji. Data citra tersebut akan diubah ukurannya menjadi 128x128x3 sebelum akhirnya menjadi masukan dari model CNN. Model CNN yang digunakan adalah multi-scale CNN yang terdiri dari 3 lapisan konvolusi (ukuran filter 3x3, jumlah filter untuk masing-masing lapisan konvolusi yakni berurutan 32, 64, dan 128, dan fungsi aktivasi ReLU), 3 lapisan subsampling menggunakan maxpool dengan ukuran filter 2x2, 1 lapisan penggabungan yang digunakan untuk menggabungkan keluaran dari masing-masing lapisan subsampling, 1 lapisan fully-connected dengan fungsi aktivasi softmax dan fungsi galat cross-entropy, dan terakhir lapisan keluaran dengan jumlah kelas 2 yakni sel normal dan sel kanker. Model CNN akan dilatih menggunakan algoritma pelatihan Adam optimizer dengan laju pelatihan 0.0002 dan dilakukan iterasi sebanyak 20 kali. Berdasarkan hasil pelatihan setelah diiterasi sebanyak 20 kali, didapatkan nilai galat terkecil yakni 0,0001 dan nilai akurasi terbesar yakni 100% pada epoch ke-20. Model CNN kemudian diuji dengan 100 data citra uji dan menghasilkan tingkat akurasi 98% dan nilai galat 0,0482.

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Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 Januari 2024

Unduhan

Diterbitkan

2024-01-30

Cara Mengutip

Akbar, M., Prasetyaningrum, P. T., Setyaningsih, P. W., Ahsan, M., & Budianto, A. E. (2024). Deteksi Leukemia Limfoblastik Akut menggunakan Convolutional Neural Network. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(1), 292–297. https://doi.org/10.32493/jtsi.v7i1.34168

Terbitan

Vol 7 No 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Mutaqin Akbar, Putri Taqwa Prasetyaningrum, Putry Wahyu Setyaningsih, Moh Ahsan, Alexius Endy Budianto

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