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Melinda Melinda
Universitas Syiah Kuala, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Autism Face Detection System using Single Shot Detector and ResNet50 Melinda Melinda; Muhammad Fauzan Alfariz; Yunidar Yunidar; Agung Hilm Ghimri; Maulisa Oktiana; Rizka Miftahujjannah; Nurlida Basir; Donata D. Acula
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1331

Abstract

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.
Implementation of Discrete Wavelet Transform and Xception for ECG Image Classification of Arrhythmic Heart Disease Patients Muhammad Irhamsyah; Melinda Melinda; Yunidar Yunidar; Ikram Muttaqin; Lailatul Qadri Zakaria
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1341

Abstract

The electrocardiogram (ECG) is one of the most important methods in the process of diagnosing heart disease. Visualizes the voltage and time relationship of the electrical activity of the heart. Cardiovascular or heart disease can be classified into several types, one of which is arrhythmia, a condition that involves changes in heartbeat rhythm, either too fast or too slow at rest. This study aims to develop a cardiac arrhythmia classification model using Deep Wavelet Transform (DWT) and Xception. It was evaluated on 2,200 spectrogram samples from the MIT-BIH dataset, containing normal and arrhythmia classes. The process compared epochs 30, 50, and 100 with learning rates of 0.001 and 0.0001 using cross-validation. Data were converted into spectrogram images for classification with Xception. The highest accuracy, 99.79%, was achieved at epoch 100 with a 0.0001 learning rate. Then, the highest precision occurs when the epoch is 50 with a learning rate of 0.001 and 0.0001, which is 100%. Lastly, Xception performed very well in the ECG image classification. This advantage demonstrates the ability of the model to recognize complex patterns in ECG data more effectively, increasing the reliability of arrhythmia detection. In addition, using DWT as a feature extraction technique allows better signal processing,which contributes to optimal results.
Mobile Application Development for Facial Classification of Autistic Children Based on MobileNet-V3 Irsyan Ramadhan; Melinda Melinda; Yunidar Yunidar; Donata D Acula; Rizka Miftahujjannah; Siti Rusdiana; Zulfan Zainal
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1363

Abstract

Early detection of autism spectrum disorder (ASD) is crucial to support timely interventions that can improve children’s cognitive and social development. However, conventional approaches still rely on subjective observations and parental reports. This study proposes the development of a Flutter-based mobile application for face classification of autistic and non-autistic children using the MobileNetV3-Small architecture. The dataset contains 600 original facial images of children aged 4 to 14 years (300 autistic and 300 non-autistic), which were expanded to 1,860 images through augmentation techniques such as Gaussian noise addition, flipping, and contrast adjustment. The model was trained using transfer learning and optimized with the SGD optimizer and sigmoid activation function. During training, the model achieved a training accuracy of 95.27% and a validation accuracy of 97.92%, indicating effective learning with minimal overfitting. Evaluation on the test data showed perfect performance, with accuracy, precision, recall, and F1-score all reaching 100%. The model was then converted to TensorFlow Lite format to allow on-device inference on mobile platforms. The app enables users to upload photos via camera or gallery and instantly receive classification results, which are also saved to Firebase for history tracking. Testing showed a fast response time (1–2 seconds) and a smooth, user-friendly experience. These results highlight the potential of the system as a lightweight, efficient, and accessible facial image-based ASD screening tool, particularly in regions with limited access to specialized healthcare. Future work should include validation using larger and more diverse datasets across different demographics to ensure model robustness, fairness, and generalizability in real-world environments.
Robust Facial Classification of Down Syndrome using Lightweight CNNs Yunidar Wahab; Muhammad Dika Rafi Kasha; Melinda Melinda; Nurlida Basir; Siti Rusdiana
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1525

Abstract

Down Syndrome (DS) is a genetic disorder caused by trisomy 21 and is characterized by distinctive facial features that can support early screening. However, access to conventional diagnostic tools remains limited, particularly in resource-constrained regions. This study presents a comparative evaluation of two lightweight convolutional neural network (CNN) architectures, EfficientNet-B1 and MobileNetV3-Large, for facial image-based DS classification. A curated dataset of 3,030 facial images underwent quality control and image enhancement processes applied exclusively to the training data, resulting in 2,620 images. The dataset was split into training, validation, and test sets at a 70:20:10 ratio. Both models were fine-tuned using ImageNet-pretrained weights and evaluated based on accuracy, precision, recall, and F1-score. Performance robustness and statistical significance between models were assessed using five-fold cross-validation and one-way ANOVA. The experimental results demonstrate that both architectures achieved high classification performance; however, EfficientNet-B1 exhibited superior stability, more balanced class predictions, and lower fold-to-fold variability. Furthermore, Grad-CAM visualization confirmed that both models focused on clinically relevant facial regions, with EfficientNet-B1 showing more consistent and interpretable attention patterns. These findings suggest that EfficientNet-B1 is a robust and interpretable model for facial-based DS screening, offering significant potential for deployment in resource-limited healthcare settings.
Classification of H2O with HCl and H2O with NaOH Solution Images Using Otsu Segmentation and CNN Mauliza Putri; Melinda Melinda; Siti Rusdiana; Aufa Rafiki; Lailatul Qadri Zakaria
JURNAL INFOTEL Vol 18 No 2 (2026): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i2.1541

Abstract

The classification of the image of chemical solutions is crucial in laboratory automation and chemical industry applications; however, it remains challenging when solutions such as H2O with HCl and H2O with NaOH exhibit nearly identical visual characteristics under imaging conditions, particularly when their spectral fluctuation patterns are visually subtle. This study proposes an image classification framework that integrates Otsu-based segmentation in the HSV color space with convolutional neural network (CNN) models to classify High Height Fluctuation (HHF) images generated from a Multi-Scale chemical detection system (MSCS). The dataset consists of 102 HHF images, evenly distributed between the two solution classes. Transfer learning is applied using three CNN architectures, namely EfficientNetV2S, DenseNet201, and EfficientNetB0, and performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that DenseNet201 achieves the best overall performance, while EfficientNetV2S provides competitive results with computational efficiency and Efficient-NetB0 yields a lighter model with lower recall. These findings indicate that combining segmentation with modern CNN architectures can effectively improve classification robustness in chemically similar solutions. This study presents a practical framework that combines Otsu-based HSV segmentation with transfer-learning CNNs to classify chemically similar solutions, providing actionable insights for deep learning-based chemical sensing applications.
Co-Authors Agung Hilm Ghimri Aufa Rafiki Donata D Acula Donata D. Acula Ikram Muttaqin Irsyan Ramadhan Lailatul Qadri Zakaria Lailatul Qadri Zakaria Maulisa Oktiana Mauliza Putri Muhammad Dika Rafi Kasha Muhammad Fauzan Alfariz Muhammad Irhamsyah Nurlida Basir Rizka Miftahujjannah Siti Rusdiana Yunidar Wahab Yunidar Yunidar Zulfan Zainal