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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Gaib, Amalan Fadil
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Computer Science & IT Electrical & Electronics Engineering Mathematics

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Advanced Sleep Disorder Classification: An ML-Based Study with Optuna for Model Optimization Gaib, Amalan Fadil; Mahayudha, I Gusti Ngurah Bagus Ferry; Wijaya, Anggito Karta; Andini, Nurul; Royan, Ando Zamhariro
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7380

Abstract

Hyperparameter optimization plays a crucial role in improving the performance of machine learning models, particularly in sleep disorder classification. However, searching for optimal hyperparameters often requires extensive computational resources and prolonged execution time. To address this issue, this study implements Optuna, a hyperparameter optimization framework based on the Tree-structured Parzen Estimator (TPE) and pruning mechanisms to enhance the efficiency of model configuration search adaptively. This study compares the performance of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest, and Multi-Layer Perceptron (MLP) in classifying sleep disorders based on health and lifestyle variables. The data undergoes several preprocessing steps, including handling missing values, encoding, normalization (StandardScaler), and class balancing using SMOTE. The models are then developed and optimized using Optuna to determine the best hyperparameter configurations. Evaluation is conducted using Accuracy, Precision, Recall, and F1-score. Experimental results show that before optimization, the Random Forest model achieved an accuracy of 94%, XGBoost 96%, SVM 93%, and MLP 96%. After being optimized with Optuna, accuracy increased to 97% for Random Forest, 97% for XGBoost, 98% for SVM, and 97% for MLP. This improvement indicates that Optuna effectively enhances model performance, especially for SVM, which experienced the most significant accuracy boost after optimization. Thus, the use of Optuna not only accelerates hyperparameter tuning but also improves the efficiency and accuracy of machine learning models in sleep disorder classification. This approach has great potential in supporting AI-based medical diagnosis systems, enabling faster and more accurate detection of sleep disorders.
An Explainable Deep Learning Approach for Brain Tumor Detection Using MobileNet and Grad-CAM Visualization Gaib, Amalan Fadil; Ardiyansa, Safrizal Ardana; Wijaya, Anggito Karta; Julianto, Eric; Mahayudha, I Gusti Ngurah Bagus Ferry; Royan, Ando Zamhariro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35901

Abstract

Brain tumor detection remains a significant challenge due to the complex variations in tumor appearance. Although deep learning models have demonstrated high accuracy, their limited interpretability hinders clinical adoption. To address this issue, this study integrates Gradient-weighted Class Activation Mapping (Grad-CAM) into Convolutional Neural Networks (CNNs) to enhance the visual interpretability of predictions. Grad-CAM extends Class Activation Mapping (CAM) and is applicable to a wide range of deep learning architectures. The primary contribution of this work is the demonstration that combining Grad-CAM with MobileNet architectures yields an interpretable and efficient framework for diagnosis of brain tumor, effectively balancing accuracy, computational efficiency, and clinical transparency. Using a Brain Tumor MRI dataset, MobileNetV4 achieved an accuracy of 98.29% with the shortest training time (1738.82 seconds) and an ROC accuracy of 99.96%. MobileNetV3 achieved 99.62% accuracy with an ROC accuracy of 99.92%. Grad-CAM effectively highlighted tumor regions while showing uniform attention in non-tumor cases, thereby reducing false positives. These results demonstrate that lightweight models can achieve a strong balance between predictive performance, training efficiency, and interpretability. The proposed framework thus supports the development of explainable and efficient diagnostic tools for clinical practice.
Co-Authors Andini, Nurul Ardiyansa, Safrizal Ardana Julianto, Eric Mahayudha, I Gusti Ngurah Bagus Ferry Royan, Ando Zamhariro Wijaya, Anggito Karta Zanestri, Tegar Fadillah