Identification System of Personal Protective Equipment Using Convolutional Neural Network (CNN) Method

Based on BPJS Employment data (2018), 157,313 accidents case happened in Indonesia. Lack of awareness and discipline of workers in the use of Personal Protective Equipment (PPE) is a main factor of the occurrence of work accidents. This identification system uses image processing which is modified by the Convolutional Neural Network (CNN) method, where this algorithm will process and analyze images of workers using PPE. The APD detected in this study is on head area such as Safety Helmet, Safety Glasses, Safety Masks, and Safety Earmuff. Twelve classification datasets were prepared for the training process with a total number of 917 image datasets. The input of this study is an image capture of workers using PPE and additional Inductive Proximity sensor is used to detect Safety Shoes. The output of this study is the results of the classification of PPE completeness which are used by the workers, with green 12 Volt DC lamp indicator for complete category indicators and red 12 Volt DC lamp indicator for the indicator if any or all of the PPE is not used. Based on the test result of this study, it obtained that the percentage of accuracy when tested real data time was 85.83 % for respondents who were included in the dataset, 80 % with percentage for respondents who were not included in the data set, and 73.34 % with percentage for female respondent.