DETEKSI PENYAKIT ALZHEIMER BERDASARKAN CITRA MRI OTAK DENGAN EKSTRAKSI FITUR GRAY LEVEL CO-OCCURRENCE MATRIX DAN METODE KLASIFIKASI NAIVE BAYES
Alzheimer's is one of the diseases that occurs in memory disorders, Alzheimer's is often too late to be detected, even though if this disease is silenced for a long time it can become severe and can even lead to death. As technology develops, Alzheimer's can now be overcome by using m...
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2022-12-15.
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| 245 | 0 | 0 | |a DETEKSI PENYAKIT ALZHEIMER BERDASARKAN CITRA MRI OTAK DENGAN EKSTRAKSI FITUR GRAY LEVEL CO-OCCURRENCE MATRIX DAN METODE KLASIFIKASI NAIVE BAYES |
| 260 | |c 2022-12-15. | ||
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| 520 | |a Alzheimer's is one of the diseases that occurs in memory disorders, Alzheimer's is often too late to be detected, even though if this disease is silenced for a long time it can become severe and can even lead to death. As technology develops, Alzheimer's can now be overcome by using magnetic resonance imaging(MRI) images. This MRI image will be the data in this study. The MRI images used are 240 with a division of 4 classes, namely 60 non demented images, 60 moderated demented images, 60 mild demented images, and 60 very mild demented images. To retrieve features from the imagery, a gray level co-occurrence matrix(GLCM) feature extraction method with angles of 0, 45, 60, and 90 is used as well as dissimilarity, homogeneity, contrast, asm, energy, and correlation features. The results of this GLCM are in the form of 24 features used for classification. To improve the performance of the classification model, dimension reduction was carried out using the principal component analysis method with n as many as 8 main feature components. Classification in the study using the Naïve Bayes method. Naïve bayes classification using the PCA result feature results in an accuracy of 85%, a precision of 87%, and a recall of 84%. | ||
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