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All Journal Nuansa Informatika Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Paradigma EVOLUSI : Jurnal Sains dan Manajemen Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Infotek : Jurnal Informatika dan Teknologi Jurnal Pengabdian kepada Masyarakat Abditeknika - Jurnal Pengabdian Kepada Masyarakat ABDIPRAJA (Jurnal Pengabdian kepada Masyarakat) J-Icon : Jurnal Komputer dan Informatika Nuansa Informatika
Miftah Farid Adiwisastra
Universitas Bina Sarana Informatika
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Journal : Nuansa Informatika

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Feature Extraction Using Markov Random Field (MRF) On Improving CNN Classification Results For Alzheimer's Disease Diagnosis: Ekstraksi Fitur Menggunakan Markov Random Field (MRF) untuk Meningkatkan Hasil Klasifikasi CNN pada Diagnosis Penyakit Alzheimer Bahri, Saeful; Adiwisastra, Miftah Farid; Hidayatulloh, Taufik
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.369

Abstract

Alzheimer’s disease is a degenerative neurological disorder that significantly affects patients’ cognitive functions and social lives. It is a leading form of dementia, characterized by the progressive death of brain cells. One widely adopted diagnostic approach involves magnetic resonance imaging (MRI) to evaluate brain structures. Recent advances in machine learning have enabled automated image analysis, with Convolutional Neural Networks (CNNs) commonly used for image feature extraction and classification. However, CNNs face a major limitation in maintaining consistency during image segmentation, which results in reduced classification accuracy. This issue arises from CNNs’ limited ability to preserve local pixel-level consistency during feature extraction. To address this, we propose integrating a Markov Random Field (MRF)-based layer into the CNN architecture, which has been shown to enhance segmentation consistency. This study utilizes publicly available MRI datasets of Alzheimer’s patients and employs a k-fold cross-validation scheme for evaluation. The results show that the CNN-MRF model improves classification accuracy to 63%, compared to 61% with the standard CNN. Furthermore, the loss value is reduced from 0.80 to 0.74. Although the improvement in accuracy is incremental, a paired t-test confirms that the difference is statistically significant (p < 0.05). This method has proven effective in enhancing the reliability of image-based diagnostic systems for early detection of Alzheimer’s disease.
Co-Authors Agung Baitul Hikmah Ai Ilah Warnilah Angga Ardiansyah Ari Zainal Fauziah Atyla Azfa Al Harits Bambang Kelana Simpony Deddy Supriadi Dini Silvi Purnia, Dini Silvi Fatah, Haerul Fauziah Nur Madinah Fauziah, Ari Zainal Fiqih Falah Saputra Fitri, Andini Hadaina Herlan Sutisna, Herlan Hidayatul Muhajir Ilham Nugraha Ilham Santoso Iskandar, Iqbal Dzulfiqar Ivanisevic Hanuraiga El-Ahmed Linda Nurmala, Linda Lintang Juwita Safitri Melisa Winda Pertiwi Mujahidillah, Vidi Azzahra Noor Cholis Basjaruddin Rahmani, Alfia Rusda Wajhillah Saeful Bahri Saeful Bahri Safitri, Lintang Juwita Salman Topiq Sari Susanti Sopiati Dewi Sulaeman Sulaeman Sulaeman Sulaeman Surahman, Mumun Taufik Hidayatulloh Tuti Alawiyah Vidi Azzahra Mujahidillah Wini Fadhilah Nugraha yani sri mulyani yani sri mulyani Yanti Apriyani Yanti Apriyani Yudi Iskandar Yudi Iskandar, Yudi Yudi Ramdhani Yuli Maulidah Hasanah