KLASIFIKASI TUMOR OTAK MENGGUNAKAN MODEL CONVOLUTIONAL NEURAL NETWORK DENGAN PENDEKATAN PEMBELAJARAN ENSEMBLE SOFT VOTING

• KARTIKA PUSPITA
• 14220025

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ABSTRAK

ABSTRAK

• Nama               : Kartika Puspita
• NIM                  : 14220025
• Program Studi : Ilmu Komputer
• Fakultas           : Teknologi Informasi
• Jenjang            : Strata Dua (S2)

Peminatan : Artificial Intelligence dan Blockchain Judul: Klasifikasi Tumor Otak Menggunakan Model Convolutional Neural Network Dengan Pendekatan Pembelajaran Ensemble Soft Voting

Otak merupakan organ vital yang mengatur fungsi sensorik, motorik, dan kognitif pada manusia. Salah satu ancaman serius bagi kesehatan otak adalah pertumbuhan jaringan abnormal yang memicu munculnya tumor, seperti glioma, meningioma, dan pituitari. Pemindaian MRI merupakan metode diagnosis yang paling umum digunakan, namun proses klasifikasi citra secara konvensional masih rentan terhadap kesalahan. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi tumor otak dengan menerapkan strategi ensemble learning menggunakan teknik soft voting. Empat model CNN diantaranya VGG16, MobileNet, ResNet50, dan DenseNet121 digunakan dan dioptimalkan melalui pendekatan fine-tuning. Dataset terdiri dari 7.023 citra MRI yang diklasifikasikan ke dalam empat kategori. Hasil menunjukkan bahwa model ensemble mencapai akurasi 97,67% dengan nilai f1-score dan recall yang tinggi, sehingga menunjukkan potensi besar dalam pengembangan sistem diagnosis berbasis citra.

KATA KUNCI

MODEL CONVOLUTIONAL NEURAL

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DAFTAR PUSTAKA

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