Penentuan Emosi pada Video dengan Convolutional Neural Network

Prasetyawan, Daru; Uyun, Shofwatul

doi:10.14421/jiska.2020.51-04

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Berbagai pendekatan telah diusulkan dalam penelitian sebelumnya untuk mengenali ekspresi wajah, mulai dari penggunaan Convolutional Neural Network (CNN) hingga algoritma You Only Look Once (YOLO). Seperti penelitan yang pernah dilakukan oleh Prasetyawan, beliau menerapkan CNN untuk identifikasi emosi melalui ekspresi wajah manusia (Prasetyawan et al, 2020). Azizi (2021) menguji tiga metode optimasi dan variasi jumlah epoch untuk mendeteksi emosi berdasarkan ekspresi (Azizi, 2021).…”

Section: Pendahuluanunclassified

Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring

Salma,

Hidayat

2024

jimik

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The implementation of Face Emotion Recognition (FER) technology in online classes opens up new opportunities to effectively monitor students' emotional responses and adjust the teaching approach. Through FER, instructors can monitor students' emotional responses to learning materials in real-time and enable quick adjustments based on individual needs. Additionally, this technology can also be used to detect the level of enthusiasm or lack thereof among students towards the learning process, allowing for the optimization of teaching strategies. This study focuses on the implementation of the YOLOv8 algorithm in detecting students' enthusiasm, comparing the performance of YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l models. Test results show that YOLOv8n performs the best with an accuracy rate of 95.3% and a fast inference time of 62ms, enabling real-time object detection. Thus, the application of YOLOv8 in this context aims to detect students' enthusiasm in real-time and allows instructors to quickly adjust their approach to meet students' needs. Furthermore, this research contributes to improving the quality of online learning by providing insights into students' emotional engagement and serving as a tool to help instructors better understand and respond appropriately to students' emotions during the online learning process.

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Section: Pendahuluanunclassified

Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring

Salma,

Hidayat

2024

jimik

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“…Ekspresi wajah dapat menjadi sebuah komunikasi non-verbal yang dapat menyampaikan sebuah keadaan berdasarkan apa yang dialami oleh seseorang berdasarkan emosi yang dirasakannya, baik itu emosi positif maupun emosi negative dan ekspresi akan secara tidak sengaja muncul untuk menggambarkan suasana emosi yang dirasakannya (Prasetyawan & 'Uyun, 2020). Pada dasarnya emosi memiliki empat karakteristik, yaitu: senang, sedih, marah, dan netral.…”

Section: Pendahuluanunclassified

Classification of Sad Emotions and Depression Through Images Using Convolutional Neural Network (CNN)

Prayuda

2021

JIUP

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The human face has various functions, especially in expressing something. The expression shown has a unique shape so that it can recognize the atmosphere of the feeling that is being felt. The appearance of a feeling is usually caused by emotion. Research on the classification of emotions has been carried out using various methods. For this study, a Convolutional Neural Network (CNN) method was used which serves as a classifier for sad and depressive emotions. The CNN method has the advantage of preprocessing convolution so that it can extract a hidden feature in an image. The dataset used in this study came from the Facial expression dataset image folders (fer2013) where the dataset used for classification was taken with a ratio of 60% training and 40% validation with the results of the trained model of 60% total loss and 68% test accuracy.

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“…One of the popular and valuable applications of computer vision is face detection. Face detection is the primary step for carrying out facial identification, and facial analysis, including facial recognition (Cahyo et al, 2023;Kristanto et al, 2023;Mataram, 2020;Prasetyawan & 'Uyun, 2020).…”

Section: Introductionmentioning

confidence: 99%

Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods

Damarsiwi,

Pambudi,

Fitriani

et al. 2024

SinkrOn

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Face detection is a process by a computer system that can find and identify human faces in digital images or videos. One of the main challenges faced in the face detection process is the complex background. Complex backgrounds, such as many color combinations in the image, can interfere with the detection process. To overcome this challenge, this research uses a combination of two methods: Scale Invariant Feature Transform (SIFT) and Haar Cascade Classifier. Scale Invariant Feature Transform (SIFT) is a method used in image processing to identify and describe unique features in an image. The SIFT method looks for keypoint descriptors in images that can be used as a reference in comparing different images. After the keypoint descriptor is found with SIFT, the Haar Cascade Classifier method is used to detect faces in the image. Haar Cascade Classifier is a practical algorithm for object detection in images. After facial features are extracted with these two methods, the results are compared with the K-Nearest Neighbor (KNN) approach. This research involves the introduction of 28 color images with complex backgrounds. The results of combining these two methods produce an accuracy of 81.75%. This shows that combining these two methods effectively overcomes complex background challenges in face detection.

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Penentuan Emosi pada Video dengan Convolutional Neural Network

Cited by 6 publications

References 17 publications

Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring

Deteksi Antusiasme Siswa dengan Algoritma Yolov8 pada Proses Pembelajaran Daring

Classification of Sad Emotions and Depression Through Images Using Convolutional Neural Network (CNN)

Face Detection in Complex Background using Scale Invariant Feature Transform and Haar Cascade Classifier Methods

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