Klasifikasi Penyakit Alzheimer Menggunakan Metode Hybrid Inception v3- RELM

Annisa Widya Dwirani, Nurissaidah Ulinnuha

Abstract

Alzheimer merupakan penyebab demensia yang paling umum dan ditandai oleh kerusakan sel saraf (neuron) pada area otak yang berperan penting dalam fungsi kognitif. Proses diagnosis manual terhadap perubahan kecil pada biomarker otak menggunakan citra Magnetic Resonance Imaging (MRI) sering menghadapi kendala dari segi akurasi dan konsistensi. Untuk mengatasi hal tersebut, pendekatan Computer Aided Diagnosis (CAD) dikembangkan untuk membantu proses deteksi dan klasifikasi secara otomatis. Penelitian ini menggunakan data dari Alzheimer’s Disease Neuroimaging Initiative (ADNI) dan menerapkan metode hybrid Inception v3 sebagai ekstraktor fitur dan Reguarized Extreme Learning Machine (RELM) sebagai pengklasifikasi hasil ekstraksi. Tujuan penelitian ini adalah menentukan kombinasi hyperparameter terbaik melalui proses tuning untuk menghasilkan model klasifikasi yang optimal terhadap citra MRI penderita Alzheimer. Model ini dirancang untuk mengklasifikasikan enam tingkat keparahan kondisi kognitif, yaitu cognitively normal, subjective memory complaints, early mild cognitive impairment, mild cognitive impairment, late mild cognitive impairment, dan Alzheimer’s disease. Berdasarkan hasil pengujian, model denga parameter terbaik diperoleh pada nilai C=0.01, fungsi aktivasi Swish, 32768 neuron, dan batch size 2048, dengan akurasi sebesar 81.67%, precision 82.23%, recall 81.76%, specificity 96.23%, dan f1-score 81.31%.

Keywords

Alzheimer’s Disease, CNN, Inception v3, MRI, RELM

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References

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DOI: https://doi.org/10.31284/j.integer.2026.v11i2.8397

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