Modified Multi-Kernel Support Vector Machine for Mask Detection

Authors

  • Muhammad Athoillah Universitas PGRI Adi Buana
  • Evita Purnaningrum Universitas PGRI Adi Buana
  • Rani Kurnia Putri Universitas PGRI Adi Buana

DOI:

https://doi.org/10.21512/commit.v16i2.7873

Keywords:

Modified Multi-Kernel, Support Vector Machine (SVM), Mask Detection

Abstract

Indonesia is one of the countries most affected by the Coronavirus pandemic with millions confirm cases. Hence, the government has increased strict procedures for using face masks in public areas. For this reason, the detection of people wearing face masks in public areas is needed. Face mask detection is a part of the classification problem. Thus Support Vector Machine (SVM) can be implemented. SVM is still known as one of the most powerful and efficient classification algorithms. The research aims to build an automatic face mask detector using SVM. However, it needs to modify it first because it only can classify linear data. The modification is made by adding kernel functions, and a Multi-kernel approach is chosen. The proposed method is applied by combining various kernels into one kernel equation. The dataset used in the research is a face mask image obtained from Github. The data are public datasets consisting of faces with and without masks. The results present that the proposed method provides good performance. It is proven by the average value. The values are 83.67% for sensitivity, 82.40% for specificity, 82.00% for precision, 82.93% for accuracy, and 82.77% for F1-score. These values are better than other experiments using single kernel SVM with the same process and dataset.

Dimensions

Plum Analytics

Author Biographies

Muhammad Athoillah, Universitas PGRI Adi Buana

Statistics Department, Faculty of Science and Technology

Evita Purnaningrum, Universitas PGRI Adi Buana

Management Department, Faculty of Economic and Business

Rani Kurnia Putri, Universitas PGRI Adi Buana

Mathematics Education Department, Faculty of Science and Technology

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Published

2022-06-08
Abstract 310  .
PDF downloaded 62  .