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All Journal JURNAL VOKASI TEKNIK (JUVOTEK)
Poppy Amalia
STMIK TIME Medan
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Implementasi Algoritma Convolutional Neural Network (CNN) Dengan Optimizer Adam Dalam Deteksi Emosionalpada Wajah Manusia Poppy Amalia; Robby Wijaya; Chandra Chandra; Pieter Octaviandy; Wilson Wilson; David David; Andy Andy; Herman Herman; Edi Edi; Didik Aryanto; Joni Joni; Johanes Terang kita Perangin Angin
JURNAL VOKASI TEKNIK Vol 3 No 1 (2025): April Issue 2025 Issue | JUVOTEK JURNAL
Publisher : CV MEDAN TEKNO SOLUSI

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Abstract

Emotional detection in human faces is an important area in image processing and emotion understanding. This research develops an emotion detection application using the Haar Cascade Classifier algorithm to detect faces and Convolutional Neural Network (CNN) with the Adam optimizer to analyze emotions. The application developed successfully detected seven types of basic emotions (anger, disgust, fear, happy, neutral, sad and surprised) in real-time with an overall accuracy of 90%. The combination of CNN and Adam optimizer shows good performance with increasing accuracy and consistent decreasing loss as the epoch increases, although there are indications of overfitting. The research results show that this system can be relied on to detect various facial expressions and provides an effective and accurate solution for emotional detection.
Co-Authors Andy Andy Chandra Chandra David David Didik Aryanto Edi Edi Herman Herman Johanes Terang kita Perangin Angin Joni Joni Pieter Octaviandy Robby Wijaya Wilson Wilson