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All Journal International Journal of Advances in Intelligent Informatics International Journal of Artificial Intelligence in Medical Issues
Amaliah, Tazkirah
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Computer Science & IT Dentistry Health Professions Medicine & Pharmacology Public Health

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Comparative Machine Learning Models for Dementia Prediction Using SMOTE Puspitasari, Rahma; Amaliah, Tazkirah; Darwis, Herdianti
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.351

Abstract

Dementia is a progressive neurodegenerative disorder that leads to cognitive decline and significantly affects patients' quality of life. Early detection is crucial for determining appropriate medical interventions and slowing disease progression. This study aims to develop a machine learning-based dementia prediction model and compare the performance of three algorithms: Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The dataset, obtained from the Kaggle platform, consists of 373 MRI-based patient records categorized into three diagnosis groups: Converted, Demented, and Nondemented. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Experimental results show that the XGBoost algorithm achieved the best performance, with an accuracy of 93.86%, precision of 94%, recall of 94%, and F1-score of 94%, outperforming SVM and Random Forest. The application of SMOTE improved the model’s sensitivity to minority classes. The combination of XGBoost and SMOTE demonstrates high accuracy in dementia prediction and holds potential for integration into clinical decision support systems (CDSS) to assist early diagnosis.
A comprehensive comparative analysis of chicken meat classification techniques through machine learning models Anraeni, Siska; Lahuddin, Harlinda; Ramdaniah, Ramdaniah; Melani, Erika Riski; Amalia, Andi Cici; Amaliah, Tazkirah
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2014

Abstract

This study develops a digital image processing technique to distinguish between fresh and rotten chicken. Chicken freshness has a significant impact on public health and industry sustainability. This study uses a multi-stage approach including data acquisition, preprocessing, feature extraction, and classification. A total of 1,000 chicken images were obtained, consisting of 800 images for training and 200 images for testing, with a proportion of 80:20. Feature extraction was performed using a combination of the HSI (Hue, Saturation, Intensity) color model to capture the color characteristics of chicken and the Local Binary Pattern (LBP) to extract texture information. Classification was performed using the K-Nearest Neighbor (KNN) algorithm with various K values and distance metrics. The experimental results show that the combination of color and texture features provides higher accuracy than using either feature alone. The best model using HSI and LBP feature extraction with K = 1 and K = 3 in the Euclidean distance metric achieved the highest accuracy of 95.4%. With a promising level of accuracy, this method can be applied in automated inspections in the poultry supply chain, improving food safety and helping consumers make better purchasing decisions. However, the main challenge in this study is the variation in lighting during image capture, which causes the fresh and rotten chicken feature values to overlap, thus hindering perfect classification.
Co-Authors Amalia, Andi Cici Darwis, Herdianti Lahuddin, Harlinda Melani, Erika Riski Puspitasari, Rahma Ramdaniah Ramdaniah Siska Anraeni