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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sistem Informasi dan Informatika (SIMIKA) Electronic Journal of Education, Social Economics and Technology Jurnal Informatika
Mohammad Nasucha
Universitas Pembangunan Jaya
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

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FACE RECOGNITION USING MOBILENETV2 AS A SUPPORT FOR DIGITAL PAYMENT APPLICATION USER AUTHENTICATION Muhammad Ilfanza Mustafavi; Mohammad Nasucha
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 1 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i1.4037

Abstract

The increasing use of digital payments potentially elevates the risk of personal data theft and unauthorized access to applications. To mitigate this, biometric-based authentication, such as facial recognition, can be implemented. This study aims to utilize facial recognition as user authentication within an application. The facial recognition is developed using MobileNetV2. This research encompasses data collection, pre-processing, data splitting, data augmentation, model training, model evaluation, and application development. The total facial image data collected was 100 images from 5 classes with an image size of 160 x 160 pixels in .jpg format, sourced from direct photography using a smartphone camera (12MP resolution) under controlled indoor lighting conditions with consistent distance of approximately 50 cm from the subject. The model was successfully implemented with an accuracy of 85%. The model achieved successful implementation with 85% accuracy for real-time facial authentication in digital payment applications.
Accuracy Comparison of Support Vector Machine and K-Nearest Neighbors in Face Recognition for Library User Identification Ellyza Hardianty; Mohammad Nasucha
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ji.v13i1.11424

Abstract

Traditional library book lending systems that rely on membership cards or personal IDs are prone to misuse due to human error. To address this, this study developed a web-based book lending application using face recognition enabling automatic user verification without physical cards, improving security, and reducing human errors. In this research 10 university students took roles as the application’s users. The goal is that the application is able to identify every library user who is going to borrow or return books based on their real time face image. The face recognition itself has been developed using dlib’s face detection, cropping, and feature extraction functions and Support Vector Machine (SVM) classification model. The K-Nearest Neighbors (KNN) model was also tested to for classification accuracy comparison. Model validation tests show that the dlib works well in detecting face location within an image, cropping the face area, and extracting face features while the two classification models are able to well classify student IDs too. The SVM model results in 91% accuracy, 90% precision, 91% recall, and 91% F1-score, which is however slightly better than KNN’s 89% accuracy, 89% precision, 88% recall and 88% F1-score. The SVM has been then chosen for the application. Following the completion of application development, a system test has been conducted with black box method and returns with system accuracy of 90%. This finding confirms that implementing dlib and an SVM model for user identification for an application can be a promising method. 
PENGUJIAN KINERJA EKSPERIMENTAL RESNET-18 PADA PENGENALAN 30 KARAKTER DASAR AKSARA SUNDA: Aksara Sunda, ResNet-18, Pembelajaran Mendalam, Jaringan Saraf Konvolusional, Klasifikasi Citra, Pengenalan Karakter Tulisan Tangan Arief Fadhiel Januarizky; Mohammad Nasucha
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8113

Abstract

Sundanese script is one of the cultural heritages that holds significant historical value for the Sundanese community. However, the use of Sundanese script in daily life has gradually declined, creating a need for preservation efforts through digital technology. One potential approach is the automatic recognition of Sundanese script using deep learning techniques. The ability of deep learning to identify visual patterns in images makes it suitable for handwritten Sundanese script classification. This study aims to evaluate the performance of the ResNet-18 architecture in recognizing 30 basic Sundanese script characters from handwritten image data. The research began with the collection of a self-created dataset obtained from 10 respondents. Each respondent was asked to write 30 basic Sundanese script characters, resulting in a total of 300 image samples. The collected images then underwent preprocessing stages, including cropping, resizing, and grayscale conversion. The processed dataset was divided into three data-splitting scenarios, namely 60:40, 70:30, and 80:20 for training and testing purposes. For each scenario, ResNet-18 was trained using both pretrained and non-pretrained approaches. After the training process, the resulting weights were saved and used during the evaluation stage. Model performance was evaluated using confusion matrices and classification metrics, including accuracy, precision, recall, and F1-score. The evaluation results from each scenario were then compared to analyze the influence of training data size on classification performance. The experimental results demonstrate that ResNet-18 is capable of classifying 30 basic Sundanese script characters with satisfactory performance. The best performance was achieved using the 80:20 data-splitting scenario with the pretrained ResNet-18, obtaining an accuracy of 98.33%, precision of 98.89%, recall of 98.33%, and F1-score of 98.22%. Furthermore, the results indicate that increasing the amount of training data contributes positively to classification performance. Based on these findings, ResNet-18 can be considered an effective approach for Sundanese script recognition and has the potential to support cultural preservation efforts through deep learning-based image processing technology.
Road Condition Recognition Based on Object Classification Using YOLOv8 Nathaniel Putra Haryanto; Mohammad Nasucha
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v%vi%i.1428

Abstract

The quality of road infrastructure is one of the important factors in supporting the safety and comfort of road users as well as the smooth distribution of transportation. Road maintenance requires periodic monitoring by authorized institutions or agencies. Manual road condition monitoring tends to require considerable time, cost, and manpower, and is also prone to subjectivity. Therefore, a computational system capable of performing this task is needed. Based on this background, this study aims to develop a computer vision-based application for recognizing road conditions. Data consisting of road images with proper annotations (damaged or good) were used to train the YOLOv8 vision model. Our test found that the system accuracy, precision, recall, and F1-score is 0.96, 0.93, 1.00, and 0.96 respectively. The developed application allows users to input road images through a live camera and obtain real-time road condition classification results.
PRIVA: Selective Face Blurring Video App Using YOLOv8-Face and MobileFaceNet Muhammad Satrio; Mohammad Nasucha
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16273

Abstract

The increasing use of vlog videos on social media creates privacy risks because third-party faces are often unintentionally recorded and distributed without consent. Existing face blurring approaches generally apply uniform anonymization to all detected faces and do not provide an identity-selective mechanism that keeps the content creator visible while blurring other individuals. This study develops PRIVA, a desktop-based selective face blurring application that runs locally without an external AI server. The proposed pipeline integrates YOLOv8n-Face-960 for face detection, MobileFaceNet for face recognition using 512-dimensional embeddings, and Deep SORT for maintaining identity consistency across video frames. Face enrollment is performed through guided multi-pose webcam capture, while video evaluation is conducted on extracted YOLO analysis frames from five real vlog-like test videos. YOLOv8n-Face-960 achieved an overall detection precision of 95.02%, recall of 89.32%, and F1-score of 92.09%. The baseline comparison showed that YOLOv8n-Face-960 achieved a higher mean detection F1-score than MTCNN, while MobileFaceNet provided a smaller and faster recognition model than FaceNet for CPU-based local inference. For correctly detected face instances, PRIVA achieved a system precision of 99.45%, recall of 98.70%, F1-score of 99.08%, and accuracy of 98.50% in determining whether faces should be blurred or kept visible. Processing performance testing showed an average analysis speed of 4.83 FPS, average export speed of 70.05 FPS, and average processing ratio of approximately 2.40 times the original video duration. These results indicate that PRIVA can support practical local identity-selective face blurring for video privacy protection, although detection robustness remains important under low-light, crowded, distant, or partially occluded face conditions.
Co-Authors Arief Fadhiel Januarizky Ellyza Hardianty Muhammad Ilfanza Mustafavi Muhammad Satrio Nathaniel Putra Haryanto