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All Journal Sinergi JURNAL NASIONAL TEKNIK ELEKTRO Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Engineering, Science and Information Technology Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Rekayasa dan Wirausaha
Basir, Nurlida
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Performance Comparison of Variational Mode Decomposition and Butterworth in Processing EEG Signals of Autism Patients Wardana, Surya; Melinda, Melinda; Ramdhana, Rizka; Yunidar, Yunidar; Away, Yuwaldi; Basir, Nurlida
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.105

Abstract

Electroencephalography (EEG) is a non-invasive technique for monitoring and recording the brain's electrical activity with electrodes applied to the scalp. The method is important in neurological studies, like that of Autism Spectrum Disorder (ASD), because it measures patterns of brain waves that can identify developmental abnormalities. However, EEG signals are often contaminated by multiple noise sources, including eye movements, muscle activity, and extraneous interference. This interference can significantly reduce the quality and intelligibility of signals. Therefore, preprocessing is required to enhance the reliability and precision of the data obtained. In this study, a Butterworth Band-Pass Filter (BPF) was used during preprocessing to filter out undesirable frequency components and to mitigate noise. After filtering, EEG signals were handled using the Variational Mode Decomposition (VMD) technique. VMD is an adaptive method for decomposing multidimensional signals into intrinsic mode functions while preserving critical details of the original data. For performance comparison, four quantitative metrics were used: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Signal-to-Noise Ratio (SNR). Results showed that VMD performed better than BPF alone. As an example, for Subject 1, VMD achieved an MAE of 0.26 and MSE of 0.42, which was far superior to the MAE of 13.72 and MSE of 674.96 of BPF. Subject 3 had the least RMSE (0.40) when using VMD, whereas BPF scored 25.90. VMD also reported a highest SNR of 28.56, compared to BPF's 2.43. Overall, integrating VMD with BPF significantly improves EEG signal quality and enables more accurate analysis, particularly in ASD-related studies.
Smart Door Locking System for Children Using HC-SR04 and IoT Technology Melinda, Melinda; Yunidar, Yunidar; Khairia, Syaidatul; Miftahujjannah, Rizka; Sakarkar, Gopal; Basir, Nurlida
JURNAL NASIONAL TEKNIK ELEKTRO Vol 14, No 2: July 2025
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v14n2.1293.2025

Abstract

The increasing incidence of minors accessing hazardous indoor areas—such as staircases, balconies, and rooms with sharp objects—raises serious safety concerns, often due to insufficient parental supervision. This study proposes an Internet of Things (IoT)-based automatic door lock system to enhance child safety in home environments. The system integrates dual ultrasonic sensors for distance and height detection, a KY-037 sound sensor, and an ESP32-CAM for real-time video monitoring, all accessible via a web interface. A key novelty lies in the integration of multi-sensor spatial awareness with live surveillance, enabling automated control and proactive safety features. Tested on ten children aged 4 to 6 years, the system achieved a 90% success rate in locking the door when a child under 120 cm approached within 1 meter, with an average response time of approximately 2 seconds. A sound-based alarm is also triggered when noise levels exceed 120 decibels, serving as an emergency alert. However, a 10% false negative rate was observed when children were detected at distances of 1.3 to 1.5 meters, suggesting the need for further sensor calibration. Overall, the system demonstrates strong potential as a scalable and cost-effective smart home safety solution, combining automation, real-time monitoring, and child-specific access control. Future work should focus on improving detection accuracy and extending functionality for multi-object scenarios.
Automated Z-Score Based Nutritional Status Classification for Children Under Two Using Smart Sensor System Yunidar, Yunidar; Melinda, Melinda; Ridara, Rina; Basir, Nurlida
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.111

Abstract

The classification of nutritional status in children under two years old is crucial for monitoring growth and early detection of nutritional problems. However, in many healthcare facilities, this classification is still performed manually, requiring relatively long processing times and being prone to human error in both measurement and data recording. The problem addressed in this study is the inefficiency and potential inaccuracy of manual nutritional status classification in toddlers. This research aims to develop an automatic and digital device capable of measuring body length and weight and classifying nutritional status in children under two years old efficiently, accurately, and in real time. The device utilizes electronic sensors integrated with a microcontroller to streamline the process and reduce measurement error. The main contribution of this study is the design and realization of a portable automation device that integrates an HC-SR04 ultrasonic sensor for measuring body length and a 50 kg full-bridge load cell sensor for measuring body weight, both controlled by an ATmega328P microcontroller. The device processes the data measurement digitally, displays the results on a 20 × 4 LCD, and provides a printed copy via a thermal printer, enhancing the data recording efficiency. The method involves the design of hardware circuits, sensor calibration, software programming using the C language in the Arduino IDE, and performance testing of the device by comparing its results to standard measuring instruments. The device’s performance is evaluated based on measurement error percentage and precision level. The results demonstrate that the device achieved an error percentage of 1.26% for body length measurement and 0.98% for body weight measurement. The overall system error is recorded at 0.5%, with a precision level ranging from ±0.08 to ±0.4.
Performance Analysis of H2O and H2O with HCl Material Image Classification Using Inception V3, VGG19, DenseNet201, and Otsu Segmentation Yunidar, Yunidar; Melinda, Melinda; Putri, Mauliza; Irhamsyah, Muhammad; Basir, Nurlida; Khairah, Alfita
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1253

Abstract

Challenges in classifying signals with fluctuations remain a focus in the field of image and signal processing. Deep learning technology, especially CNN (Convolutional Neural Network), has proven effective for complex visual classification; however, its performance can still be improved, particularly for signal nonlinearity distributions that are not evenly distributed. This study develops a system for classifying signals that exhibit high fluctuations using a merged Otsu segmentation and deep learning ensemble approach with InceptionV3, VGG19, and DenseNet201 models. The methodology employed is a quantitative study based on a deep learning ensemble. H?O and H?O with HCL signal datasets were processed using Otsu segmentation and then extracted using three CNN architectures, which were then combined with the methods of soft voting and stacking. Evaluation is conducted through the analysis of accuracy, precision, recall, loss, and a confusion matrix. DenseNet201 records the highest accuracy of 95%, precision of 0.90, recall of 0.86, and f1-score of 0.95. InceptionV3 achieves equivalent accuracy (95%) but with a recall of 0.83. VGG19 noted an accuracy of 91%, a precision of 0.82, and a recall of 0.78. The ensemble results show improvement in stability classification, especially in class H?O segmentation. However, the classification class HCL segmentation still shows more mistakes. The integration of Otsu segmentation and deep learning ensemble models has been proven effective in increasing the accuracy of classifying signal fluctuations. Segmentation helps highlight the importance of spatial features, while ensemble enhances model generalization. Research furthermore recommended exploring method segmentation and adaptive data augmentation to handle more complex and unbalanced distributions.
Precise Electrocardiogram Signal Analysis Using ResNet, DenseNet, and XceptionNet Models in Autistic Children Yunidar, Yunidar; Melinda, Melinda; Albahri, Albahri; Ramadhani, Hanum Aulia; Dimiati, Herlina; Basir, Nurlida
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1044

Abstract

In autistic children, one of the important physiological aspects to be examined is the heart condition, which can be assessed through electrocardiogram (ECG) signal analysis. However, ECG signals in autistic children often contain interference in the form of noise, making the analysis process, both manual and conventional, challenging. Therefore, this study aims to analyze the ECG signals of autistic children using a classification method to distinguish between two main conditions: playing and calm conditions. A deep learning approach employing the Convolutional Neural Network (CNN) architectures was used to obtain accurate results in distinguishing the heart conditions of autistic children. The data used consists of 700 ECG signal data in each class, processed through the filtering, windowing, and augmentation stages to obtain balanced data. Three CNN architectures, ResNet, DenseNet, and XceptionNet, were tested in this study. Although these architectures are originally designed for 2D and 3D image data, modifications were made to adapt the input data structure to perform 1D data calculations. The evaluation results show that the XceptionNet model achieved the best performance, with accuracy, precision, recall, and F1-score of 97,14% each, indicating a good ability in capturing the complex patterns of ECG signals. Meanwhile, the ResNet obtained good results with 96,19% accuracy, while DenseNet performed slightly lower results with 94,76% accuracy and evaluation metrics. Overall, this study demonstrates that a deep CNN architecture based on dense connections can enhance the accuracy of ECG signal classification in autistic children.
Pengenalan Robotika sebagai Media Pembelajaran STEM di SMA Labschool Unsyiah Banda Aceh Melinda, Melinda; Yunidar, Yunidar; Irhamsyah, Muhammad; Islamy, Fajrul; Priandana, Karlisa; Basir, Nurlida; Safitri, Rini
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi robotika telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk dunia pendidikan. Robotika, sebagai bidang multidisiplin yang menggabungkan aspek mekanika, elektronika, dan ilmu komputer, memiliki potensi besar untuk diterapkan dalam pembelajaran berbasis Science, Technology, Engineering, and Mathematics (STEM). Artikel ini bertujuan mendeskripsikan pelaksanaan kegiatan pengabdian masyarakat berupa pengenalan sistem robotika di SMA Labschool Unsyiah Banda Aceh serta mengkaji dampaknya terhadap pengetahuan dan motivasi siswa. Metode pelaksanaan kegiatan meliputi identifikasi kebutuhan mitra, perencanaan materi, pelaksanaan kegiatan, praktik sederhana, hingga evaluasi. Dokumentasi kegiatan menunjukkan antusiasme tinggi dari siswa selama mengikuti seluruh rangkaian kegiatan. Hasil evaluasi melalui kuesioner dan observasi lapangan mengindikasikan bahwa siswa memperoleh peningkatan pemahaman tentang konsep dasar robotika serta terdorong untuk lebih berminat pada bidang STEM. Guru pendamping juga menilai kegiatan ini relevan dengan kebutuhan pembelajaran dan membuka peluang pengembangan robotika sebagai kegiatan ekstrakurikuler di sekolah. Dengan demikian, kegiatan pengenalan robotika tidak hanya berkontribusi pada peningkatan literasi teknologi siswa, tetapi juga menjadi langkah awal dalam membangun ekosistem pendidikan berbasis teknologi yang berkelanjutan di SMA Labschool Unsyiah Banda Aceh.
MEWT-Enhanced EEGNet for ASD EEG Classification: Performance Evaluation with k-Fold Cross-Validation Fathur Rahman, Imam; Melinda, Melinda; Yunidar, Yunidar; Basir, Nurlida; Rafiki, Aufa
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.313

Abstract

Accurate and reliable classification of autism spectrum disorder (ASD) from electroencephalography (EEG) signals remains challenging due to the inherently nonstationary, nonlinear, and multichannel nature of EEG data. These properties complicate the extraction of discriminative features that are both stable and computationally efficient. To address this challenge, this study proposes a compact deep-learning pipeline that integrates the Multivariate Empirical Wavelet Transform (MEWT) with EEGNet for ASD–EEG classification. MEWT decomposes multichannel EEG signals into spectrally aligned subbands while preserving inter-channel relationships. The resulting MEWT-based features are then processed by EEGNet, a lightweight convolutional neural network specifically designed for EEG-based learning tasks. Performance was evaluated using 5-fold cross-validation. The proposed MEWT with the the EEGNet model achieved a mean test accuracy of 98.35%, with consistently high precision (98.23%), recall (98.45%), F1-score (98.34%), and specificity (98.24%) across all folds. Confusion-matrix results indicated very few and well-balanced false positives and false negatives, supporting stable discrimination between ASD and control EEG segments. A one-sample one-tailed t-test against a 50% chance level confirmed that all evaluated metrics were significantly above chance (p < 0.0001). When benchmarked against previously reported results on the same dataset, the proposed approach slightly improved upon EMD with EEGNet (97.99%) and clearly outperformed EWT with EEGNet (95.08%), suggesting that MEWT-derived multichannel features are well matched to compact convolutional architectures for ASD–EEG analysis. Despite these strong results, the study is limited by a small, single-site cohort and the use of a single deep-learning model. Future work will focus on standardized retraining across multiple feature families and validation on larger and more diverse populations to further assess robustness and generalizability
Comparative analysis of EEG pre-processing in ASD using Hanning and Blackman Harris filters Melinda, Melinda; Waladah, Buleun; Yunidar, Yunidar; Mahfuzha, Raudhatul; Gazali, Syahrul; Rusdiana, Siti; Basir, Nurlida
SINERGI Vol 30, No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2026.1.023

Abstract

This study investigates the effectiveness of two Finite Impulse Response (FIR) filter designs based on the Hanning and Blackman-Harris windows for preprocessing electroencephalography (EEG) signals collected from both neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). EEG signals were recorded using a 16-channel setup and band-pass filtered between 0.5 and 40 Hz to isolate relevant neural activity. Subsequently, the signals were processed independently using each FIR filter type. Performance evaluation was conducted using four quantitative metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Power Spectral Density (PSD). The Hanning window filter showed MAE values ranging from 0.079 to 0.325, MSE from 0.026 to 0.177, SNR between 7.56 and 15.86 dB, and PSD values from 5.3 to 9.08 × 10⁻³. These results demonstrate good noise attenuation while preserving signal morphology. In contrast, the Blackman-Harris window produced higher MAE (0.061–0.318) and MSE (0.019–0.172) but achieved significantly greater SNR improvements (7.77–17.4 dB) and tighter control over PSD (4.904 – 8.442 × 10⁻³), indicating superior noise suppression and reduced spectral leakage. A paired t-test confirmed that differences in all four performance metrics were statistically significant (p < 0.05) across both neurotypical and ASD subject groups. Despite the Hanning filter's computational simplicity, the Blackman-Harris filter demonstrated more robust performance, making it a more suitable choice for high-fidelity EEG signal analysis in clinical diagnostics and neuroscience research.  
CNN-Based Facial Image Analysis for Pediatric Down Syndrome Classification Yunidar, Yunidar; Harahap, Inda Mariana; Melinda, Melinda; Rosmawinda, Rosmawinda; Basir, Nurlida; Rafiki, Aufa; Rahman, Imam Fathur
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1523

Abstract

Down syndrome (trisomy 21) is a genetic disorder caused by an extra copy of chromosome 21, resulting in distinctive developmental facial characteristics and intellectual delays. Early detection is crucial to enable timely medical intervention. However, conventional diagnostic procedures still rely on clinical observation and genetic testing, which can be invasive and expensive. This study proposes a facial image–based classification system for detecting Down syndrome using a Convolutional Neural Network (CNN) approach. Seven CNN architectures were evaluated, namely EfficientNetB0, MobileNetV2, ResNet34, ShuffleNetV2, AlexNet, VGG19, and InceptionV3, under two training scenarios: with and without early stopping. The dataset consisted of 1,000 facial images of children with and without Down syndrome, split into training, validation, and test sets at 60:20:20. Face detection was performed using the Haar Cascade Classifier, followed by data augmentation techniques including rotation, zoom, translation, horizontal flipping, and Gaussian noise to improve model generalization and reduce overfitting. Experimental results show that the VGG19 architecture achieved the best performance, with an accuracy of 94.5%, precision of 91.59%, recall of 98%, and an F1-score of 94.69%. A one-way ANOVA test yielded an F-value of 0.003 and a p-value of 0.955 (> 0.05), indicating no statistically significant difference between models trained with and without early stopping. Grad-CAM visualization highlighted key facial regions, namely the eyes, nose, and mouth, as the primary contributors to classification, while analysis using 68 facial landmark points revealed distinctive morphological patterns associated with Down syndrome. The integration of CNN models, Grad-CAM visualization, and facial landmark analysis demonstrates a promising, interpretable, and non-invasive approach to supporting early Down syndrome screening using facial images
Robust Facial Classification of Down Syndrome using Lightweight CNNs Wahab, Yunidar; Rafi Kasha, Muhammad Dika; Melinda, Melinda; Basir, Nurlida; Rusdiana, Siti
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.
Co-Authors Al Bahri Fathur Rahman, Imam Gazali, Syahrul Herlina Dimiati, Herlina Inda Mariana Harahap Islamy, Fajrul Karlisa Priandana Khairah, Alfita Khairia, Syaidatul Mahfuzha, Raudhatul Melinda Melinda Miftahujjannah, Rizka Muhammad Irhamsyah Putri Mauliza, Putri Rafi Kasha, Muhammad Dika Rafiki, Aufa Rahman, Imam Fathur Ramadhani, Hanum Aulia Ramdhana, Rizka Ridara, Rina Rini Safitri Rosmawinda, Rosmawinda Sakarkar, Gopal Siti Rusdiana Wahab, Yunidar Waladah, Buleun Wardana, Surya Yunidar Yuwaldi Away