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Muhammad Zaki
Telkom University
Computer Science & IT

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Journal : Jurnal Riset Informatika

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ALEXNET ARCHITECTURE AND FUZZY ANALYSIS ON TALENT JUDGE DECISION PREDICTION BASED ON FACIAL EXPRESSION Muhammad Zaki; Anggunmeka Luhur Prasasti; Marisa W. Paryasto
Jurnal Riset Informatika Vol 4 No 4 (2022): Period of September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (971.731 KB) | DOI: 10.34288/jri.v4i4.435

Abstract

The expression on the human face is a means of non-verbal communication. In the talent search event, the facial expressions shown by the judges when watching the participants' performances became one of the components to see whether the contestant who was performing could qualify for the next round or he would fail. Haar cascade is used to provide the location of the face in the frame and to classify the expressions on the face, a CNN model with modified AlexNet architecture is used which increases the accuracy by 5% from the original alexnet. A fuzzy Algorithm is used to predict the judge's decision based on how many facial expressions appear during the participant's appearance. The decision prediction system for talent search judges based on facial expressions using fuzzy is considered effective in predicting decisions, after being tested the system can predict decisions with an accuracy rate of 83%.
ALEXNET ARCHITECTURE AND FUZZY ANALYSIS ON TALENT JUDGE DECISION PREDICTION BASED ON FACIAL EXPRESSION Muhammad Zaki; Anggunmeka Luhur Prasasti; Marisa W. Paryasto
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (706.456 KB) | DOI: 10.34288/jri.v4i4.171

Abstract

The expression on the human face is a means of non-verbal communication. In the talent search event, the facial expressions shown by the judges when watching the participants’ performances became one of the components to see whether the contestant who was performing could qualify for the next round or he would fail. Haar cascade is used to provide the location of the face in the frame and to classify the expressions on the face, a CNN model with modified AlexNet architecture is used which increases the accuracy by 5% from the original alexnet. A fuzzy Algorithm is used to predict the judge’s decision based on how many facial expressions appear during the participant’s appearance. The decision prediction system for talent search judges based on facial expressions using fuzzy is considered effective in predicting decisions, after being tested the system can predict decisions with an accuracy rate of 83%.
Co-Authors ANGGUNMEKA LUHUR PRASASTI Marisa W. Paryasto