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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi JOIN (Jurnal Online Informatika) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Electronics, Electromedical Engineering, and Medical Informatics Media Jurnal Informatika Jurnal Pengabdian Masyarakat Prosiding SeNTIK STI&K
Suheryadi, Adi
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Performance Evaluation of Face Mask Detection Using Feature Descriptor and Supervised Learning Method Suheryadi, Adi; Adhinata, Faisal Dharma; Wijanarko, Heru
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11999

Abstract

The use of masks as a measure to prevent the spread of dangerous diseases such as COVID-19 and others has become a social norm. Manual detection is less effective, especially in areas with high mobility. This study develops and evaluates an artificial intelligence (AI)-based face mask detection system using feature description and machine learning models. An optimal and lightweight model can help hospitals implement face mask detection systems in areas prone to disease transmission. Image preprocessing, feature description, supervised learning model studies, and performance evaluation were conducted using accuracy, precision, recall, and F1-score metrics, and a confusion matrix was used to assess the overall model performance. The performance evaluation results show that the combination of the LBP feature description with the random forest model is the best choice, with a relatively high and stable accuracy of around 96.3% with an average value, precision, recall, and F1-score of around 96% using K-Fold Cross-Validation. These findings suggest that this method is helpful in detecting mask use while minimizing error and computation rates. This study contributes to the development of lightweight mask detection systems that can be used in real time.
Co-Authors A. Sumarudin Adiwilaga, Anugrah Alifia Puspaningrum Arief, Azran Budi Darsih, Darsih Deden Pradeka Devi Aprianti Rimadhani Agustini Faisal Dharma Adhinata Firdaus Firdaus Himawan, Salamet Nur Kurnia Adi Cahyanto Mustamiin, Muhamad Nuriman, Rizki P, Willy Permana Pamungkas, Kiki Adi PY, Reza Sembada, Muhammad Rifqi Sitanggang, Filemon