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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika
Mohammad Nasucha
Universitas Pembangunan Jaya
Computer Science & IT Control & Systems Engineering

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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. 
Co-Authors Ellyza Hardianty Muhammad Ilfanza Mustafavi