Klasifikasi Jenis Kelamin Wajah Bermasker Menggunakan Algoritma Supervised Learning
DOI:
https://doi.org/10.30865/mib.v6i1.3377Keywords:
Classification, K-NN method, SVM method, Random Forest MethodAbstract
The human face is an example of the unique biometric data that each person has. The human face contains a lot of information, including face shape, skin color, eye shape, nose shape, mouth shape, and some additional attributes such as beard, mustache, hair, and eyebrows. This biometric information can be used to obtain further information regarding the human identity itself. In this study, sample data of human faces aged 20 to 30 years were used. The purpose of this study is to classify gender in human facial images using masks, while the algorithm used uses supervised learning. The research process carried out was collecting human face sample data, conducting training stages which included face resizing, storing data in arrays, feature extraction using faceNet, training data and gender models. Perform testing stages which include video data acquisition, video extraction into frames, video frames, face detection, facial image resixing, storing data in arrays, feature extraction using faceNet and prediction of gender determination. The benefits of this research can provide information in determining the gender of human faces with masks, apply the application of supervised learning algorithms to processing human face images and can be applied to face detection on highways, public areas and shopping centers. Based on testing using the K-NN method, the train accuracy value is above 87% and the test accuracy is above 96%. Testing using the SVM method obtained train accuracy above 99% and test accuracy above 98%. Tests using the random forest method obtained 100% train accuracy and test accuracy above 88%.References
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