Vol.11, No.3, August 2022.                                                                                                                                                                              ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Age Classification of Moviegoers Based on Facial Image Using Deep Learning


Abba Suganda Girsang, Dewa Bagus Gde Khrisna Jayanta Nugraha


© 2022 Abba Suganda Girsang, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 11, Issue 3, Pages 1406-1415, ISSN 2217-8309, DOI: 10.18421/TEM113-52, August 2022.


Received: 23 May 2022.

Revised:   15 August 2022.
Accepted: 21 August 2022.
Published: 29 August 2022.




The number of moviegoers in Indonesia continues to rise year after year until 2019. However, due to the COVID-19 pandemic, most Indonesian cinemas were closed in early 2020. Moviegoers are increasingly turning to digital platforms to watch films. Based on the films shown, they can be divided into three categories: films for children, films for adolescents, and films for adults. A system that can automatically classify the faces of the audience based on their age category is required. Using Deep Learning, this study aims to classify the audience's age based on facial photos. The first stage involves collecting data from three datasets: All-Age-Face, FaceAge, and FGNET, which are then combined and relabeled based on age group. Preprocessing and hyperparameter testing were also performed. Finding the best learning rate and bottleneck layer is the goal of hyperparameter testing. The training process employs learning rete and the two best bottleneck layers with six models, namely MobileNet, MobileNetV2, VGG16, VGG19, Xception, and ResNet101V2. Global Average Pooling was added at the end of the layer in each model. The MobileNet model on two bottleneck layers yielded the best testing accuracy value of 85.44 percent in this study.


Keywords – age classification, image classification, deep learning, hyperparameter tuning.



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