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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Ilmu dan Teknologi Kesehatan
Bintang Vieshe Mone
Telkom University
Computer Science & IT Health Professions

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Digital Image Processing to Detect Sumba Woven Fabric Contour Using Gray Level Co-occurrence Matrix and Self Organizing Map Mone, Bintang Vieshe; Kaesmetan, Yampi R; Meo, Meliana O.
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28355

Abstract

Sumba woven cloth is one of the cultural heritages of the island of Sumba. Based on its manufacture, the classification process for Sumba woven fabrics is based on the identification of colors or motifs. However, the classification process is not an easy process. In addition to the classification process, the wider community also does not get much information about Sumba woven fabrics clearly, therefore digital image processing technology is needed to build a system that can overcome the problems faced. The image of the Sumba woven fabric sample is converted to grayscale and resized, then segmented using Sobel detection. Then extracted using Gray level co-occurrence matrix (GLCM). After extraction, it will be classified using a Self Organizing Map (SOM). Based on the results of this study, it was concluded that the accuracy of the validation test was 80%, and the program was successful.
Pendekatan Hibrida menggunakan Sistem Inferensi Fuzzy dan Pembelajaran Mendalam untuk Klasifikasi Penyakit Alzheimer pada Citra MRI Bagas Wibowo; Andy Maulana Yusuf; Bintang Vieshe Mone; Sabrina Adinda Sari
jitek Vol 13 No 2 (2026): Maret 2026
Publisher : Poltekkes Kemenkes Jakarta III

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32668/jitek.v13i2.2381

Abstract

Early detection of Alzheimer's disease using brain MRI image data can substantially improve clinical intervention and patient management. Our study evaluates the performance of an Alzheimer's classification system based on Fuzzy Inference Systems (FIS), specifically for the Mamdani and Sugeno models, in identifying four patient categories: (1) Non-Dementia, (2) Very Mild Dementia, (3) Mild Dementia, and (4) Moderate Dementia. In addition, this study compares the classification performance and computational efficiency of several deep learning architectures, including a traditional CNN (VGG16), a modern model (EfficientNet-B0), and a hybrid Fuzzy Convolutional Inference Engine (FCIE) that integrates CNN-based feature extraction with fuzzy logic reasoning. The dataset used consists of normalized and augmented Alzheimer's MRI images, and each model was trained and validated using a 70%:15%:15% split for training, validation, and testing. Experimental results show that the Mamdani and Sugeno FIS models achieve validation accuracies of about 32% and 35%, respectively, which highlights the limitations of pure texture-based features in capturing complex classification patterns. In contrast, VGG16 and EfficientNet-B0 produced validation accuracies of 82.81% and 85.22%, respectively, with AUC values of 0.95 and 0.96, respectively. However, when both schemes were combined into a hybrid model FCIE achieved the highest validation accuracy of 98.03% and AUC of 0.99. Comparative analysis of metrics, including precision, recall, F1-score, AUC, and training duration, shows a clear trade-off between accuracy and computational efficiency. This study recommends the FCIE model for clinical applications requiring high diagnostic accuracy, while EfficientNet-B0 is suggested for medical environments with moderate GPU resource constraints.
Co-Authors Andy Maulana Yusuf Bagas Wibowo Kaesmetan, Yampi R Meliana O Meo Sabrina Adinda Sari