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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control International Journal of Engineering, Science and Information Technology Jurnal INFOTEL
Miftahujjannah, Rizka
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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University Students Stress Detection During Final Report Subject by Using NASA TLX Method and Logistic Regression Khairah, Alfita; Melinda; Hasanuddin, Iskandar; Asmadi, Didi; Arifin, Riski; Miftahujjannah, Rizka
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6401

Abstract

Stress is a psychological response that occurs when someone faces pressure or demands that exceed their ability to adapt. In the context of a final-year student, stress is often a significant problem due to academic pressure, such as completing final assignments, as well as demands to immediately prepare to enter the workforce and demands to immediately prepare to enter the workforce. Research shows that stress that is not managed properly can cause various negative effects, such as sleep disorders and decreased cognitive function. This study aimed to identify and analyze stress levels among final-year students who completed a final report by integrating physiological and psychological data. In this study, 30 students were assessed using a wearable system to obtain physiological data, such as heart rate and body temperature, while subjective assessments were carried out using the NASA-TLX method to measure mental workload. The results showed that 19 out of 30 respondents experienced significant levels of stress and 11 respondents were in normal conditions, with the main causal factors including high academic pressure and distance regarding the future. In addition, the logistic regression analysis applied in this study succeeded in developing a predictive model with an accuracy of 94% in identifying students' stress conditions. This shows that this method is sufficiently accurate for detecting stress symptoms in final-year students.
Classification of Arrhythmia Electrocardiogram Signals Using Kernel Principal Component Analysis and Naive Bayes Melinda, Melinda; Farhan; Irhamsyah, Muhammad; Miftahujjannah, Rizka; D Acula, Donata; Yunidar, Yunidar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2219

Abstract

Arrhythmia is a cardiovascular disorder commonly detected through electrocardiogram (ECG) signal analysis. However, classifying arrhythmias based on ECG signals remains challenging due to signal complexity and individual variability. This study aims to develop a more accurate and efficient method for arrhythmia classification. The proposed method utilizes Kernel Principal Component Analysis (KPCA) and the naïve Bayes algorithm to classify arrhythmic ECG signals. KPCA is chosen for its ability to reduce data dimensionality, facilitating the processing of complex ECG signal and improving classification accuracy by minimizing noise. The naïve Bayes algorithm is chosen for its simplicity and computational speed, as well as its effective performance, even with limited data. ECG signals are processed using KPCA to reduce data dimensionality and extract relevant features. Subsequently, the naïve Bayes algorithm is then applied to classify the ECG signals into four categories: Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB).  The model's performance is evaluated using metrics such as accuracy, sensitivity, specificity, precision, and F1-score. The naïve Bayes model achieves an overall accuracy of 97.67%, with the highest performance observed in the RBBB class at 99.33%. Additionally, the F1-scores across all classes range from 96.62% to 98.57%, demonstrating the model's capability in detecting arrhythmias effectively. These results indicate that the combination of KPCA and naïve Bayes is effective for arrhythmic ECG signals classification.
Autism Face Detection System using Single Shot Detector and ResNet50 Melinda, Melinda; Alfariz, Muhammad Fauzan; Yunidar, Yunidar; Ghimri, Agung Hilm; Oktiana, Maulisa; Miftahujjannah, Rizka; Basir, Nurlida; Acula, Donata D.
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.
Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda, Melinda; Yunidar, Yunidar; Miftahujjannah, Rizka; Rusdiana, Siti; Amalia, Amalia; Qadri Zakaria, Lailatul
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.v5i4.1206

Abstract

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.
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.
Mobile Application Development for Facial Classification of Autistic Children Based on MobileNet-V3 Ramadhan, Irsyan; Melinda, Melinda; Yunidar, Yunidar; Acula, Donata D; Miftahujjannah, Rizka; Rusdiana, Siti; Zainal, Zulfan
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.
Co-Authors Acula, Donata D Acula, Donata D. Alfariz, Muhammad Fauzan Amalia Amalia Arifin, Riski Asmadi, Didi Basir, Nurlida D Acula, Donata Farhan Ghimri, Agung Hilm Hasanuddin, Iskandar Khairah, Alfita Khairia, Syaidatul Melinda Melinda Melinda Muhammad Irhamsyah Oktiana, Maulisa Qadri Zakaria, Lailatul Ramadhan, Irsyan Sakarkar, Gopal Siti Rusdiana Yunidar Zainal, Zulfan