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All Journal ILMU USHULUDDIN Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Speed - Sentra Penelitian Engineering dan Edukasi JUITA : Jurnal Informatika CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Pilar Nusa Mandiri Jurnal Biologi Tropis ILKOM Jurnal Ilmiah FIKRAH Jurnal Teknologi dan Informasi MULTINETICS INTI Nusa Mandiri Dedikasi: Jurnal Pengabdian Masyarakat JUDICIOUS: Journal of Management Computer Science (CO-SCIENCE) Jurnal Pemikiran Islam (JPI) Simpatik: Jurnal sistem Informasi dan Informatika J-Intech (Journal of Information and Technology) Smart: Journal of Sharia, Tradition, and Modernity Jurnal Studi Sosial Keagamaan Syekh Nurjati Akademika : Jurnal Pemikiran Islam Jurnal Fuaduna: Jurnal Kajian Keagamaan dan Kemasyarakatan Jurnal Pengabdian Masyarakat Bhinneka Ladang Artikel Ilmu Komputer Journal of Accounting Information System Nuansa Informatika El-Mujtama: Jurnal Pengabdian Masyarakat EDUKASI EMPIRISMA: JURNAL PEMIKIRAN DAN KEBUDAYAAN ISLAM Edukasia Islamika: Jurnal Pendidikan Islam Hawa : Jurnal Pemberdayaan Dan Pengabdian Masyarakat (HAWAJPPM) Al-madinah: Journal of Islamic Civilization Fikrah : Jurnal Pendidikan Agama Islam
Taufik Hidayatulloh
Universitas Bina Sarana Informatika
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology 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 A.Gunawan Aan Rukmana Abdillah Abdillah Ahmad Fauzi Akrom, Akrom Andhika Putra Munggaran ANI OKTARINI SARI, ANI OKTARINI Anisa Fajria Arif Purnama Arif, Ridwan Asti Herliana, Asti Aziizil Fauzia bin Mohd Noor, Khairunnizam Clare Harvey Daniel Ratag Dasya Arif Firmansyah Dede Wintana Desi Susilawati - AMIK BSI Sukabumi Dinar Ismunandar Dini Nurlaela Dwi Rahma, Indira Dzulfaqor, Arul Fadyah Ernawati Eva Marsusanti Fadhilah, Ulfah Nur Febrian, Rojja Fuad Mahbub Siraj Fuad Nur Hasan Gunawan Gunawan Gunawan Hajam Hajam, Hajam Hartin Kurniawati Hasan, Fuad Nur Ika Rahayu Satyaninrum Iklima Iklima Indra Aditya Prayoga Isep Djuanda Ita Yulianti Ita Yulianti Jabang Nurdin Jasisca Marleftan Kamarzaman, Mohd Haidhar Lestari Yusuf Lila Dini Utami Lis Saumi Ramdhani Lisnawati Dewi, Lisnawati Miftah Farid Adiwisastra Mohamad Syarif Sumantri Munawaroh, Fitri Mustika Madliah Mustika Dewi Muttaqien Neng Sella Zakiatun Nufus Neng Senja Nekida Nofrita Nofrita Nugraha, Setyo Bagus Nuri Aslami Nursyifa, Eva Pirim Setiarso Pribadi, Denny Purwaningtias, Deasy Rabathy, Qisthy Rahmawati, Ami Rida Nutria Lestari Rifa Nurafifah Syabaniah Rifai, Pipip A. Risdiansyah, Deni Rival Afrian Rizal Amegia Saputra Rusda Wajhillah Saeful Bahri Saeful Bahri Saputra, Alfian Ady Satia Suhada, Satia Saumantri, Theguh Siskawati Siskawati Siti Fatimah Siti Nurajizah Sunaryo Sunaryo Syifa, Bahro Theguh Saumantri Toni Arifin Tya Septiani Nurfauzia Koeswara Utami, Dian Tri Veithzal Rivai Zainal Wahyu Nugraha Wahyudi Akmaliah, Wahyudi Wijaya Kusuma, Ceeptadi Winardi, Ardi Winda Nidya Putri Fitriana Yenni Yunita Yulhan Wahyudin Yusti Farlina Zulmi Ramdani