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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Zainal, Zulfan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Aplikasi Reservasi Maintenance Kendaraan Berbasis SMS Gateway Zulfan, Zulfan; Munawir, Munawir
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 3, No 1 (2019): JTIK
Publisher : KITA Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.691 KB) | DOI: 10.35870/jtik.v3i1.84

Abstract

Private or public vehicles as a means of transportation are now increasingly being used. This develops along with a large number of residents in their daily activities. With so many vehicles the need for vehicle maintenance centers is also growing. At present, the vehicle maintenance center is not proportional to the number of vehicles. This problem is seen by many vehicle users who are waiting to take care of their vehicles and some of them have to make reservations. Reservations made are still conventional in nature that is still making reservations at the vehicle maintenance center. Therefore, the purpose of this paper is to explain the making of the SMS Gateway vehicle reservation application. The method used is the Software Development Life Cycle (SDLC) using PHP, MySQL, and Gammu programming. This research produces an application that can make customers make vehicle maintenance reservations remotely using SMS media. And customers can receive information about vehicles that have been completed through maintenance via SMS sent via the application server.Keywords:Reservation, Vehicle, Information System, SMS gateway
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.
Thermal Image Classification of Autistic Children Using Res-Net Architecture Ahmadiar, Ahmadiar; Melinda, Melinda; Muthiah, Zharifah; Zainal, Zulfan; Mina Rizky, Muharratul
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/365fkd59

Abstract

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.
Image Segmentation Performance using Deeplabv3+ with Resnet-50 on Autism Facial Classification Melinda, Melinda; Aqif, Hurriyatul; Junidar, Junidar; Oktiana, Maulisa; Binti Basir, Nurlida; Afdhal, Afdhal; Zainal, Zulfan
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1144

Abstract

In recent years, significant advancements in facial recognition technology have been marked by the prominent use of convolutional neural networks (CNN), particularly in identification applications. This study introduces a novel approach to face recognition by employing ResNet-50 in conjunction with the DeepLabV3 segmentation method. The primary focus of this research lies in the thorough analysis of ResNet-50's performance both without and with the integration of DeepLabV3+ segmentation, specifically in the context of datasets comprising faces of children on the autism spectrum (ASD). The utilization of DeepLabV3+ serves a dual purpose: firstly, to mitigate noise within the datasets, and secondly, to eliminate unnecessary features, ultimately enhancing overall accuracy. Initial results obtained from datasets without segmentation demonstrate a commendable accuracy of 83.7%. However, the integration of DeepLabV3+ yields a substantial improvement, with accuracy soaring to 85.9%. The success of DeepLabV3+ in effectively segmenting and reducing noise within the dataset underscores its pivotal role in refining facial recognition accuracy. In essence, this study underscores the pivotal role of DeepLabV3+ in the realm of facial recognition, showcasing its efficacy in reducing noise and eliminating extraneous features from datasets. The tangible outcome of increased accuracy of 85.9% post-segmentation lends credence to the assertion that DeepLabV3+ significantly contributes to refining the precision of facial recognition systems, particularly when dealing with datasets featuring faces of children on the autism spectrum.
Co-Authors Acula, Donata D Afdhal Afdhal Ahmadiar, Ahmadiar Aqif, Hurriyatul Binti Basir, Nurlida Junidar, Junidar Melinda Melinda Miftahujjannah, Rizka Mina Rizky, Muharratul Munawir Munawir Oktiana, Maulisa Ramadhan, Irsyan Siti Rusdiana Yunidar Zharifah Muthiah