Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time

1, 1 (2025) Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time. Journal of Information System Research (JOSH), 6 (4). pp. 1879-1885. ISSN 2686-228X

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Abstract

This study implements a direct facial expression detection system via the web using teachable machine and tensorflow.js. This system utilizes machine learning technology that operates directly in the browser without the need for a special server. With the transfer learning method, the model is trained to recognize various facial expressions such as happy, sad, angry, and neutral. This implementation uses a convolutional neural network (cnn) architecture that has been optimized for web activities. The results of the test show a detection accuracy level of 85-90% with a response time of under 200ms. This solution provides a lightweight option for emotion recognition applications that can be easily accessed via a web browser. The main advantages of this system are ease of implementation, cross-platform support, and maintaining data privacy because the process is carried out locally.

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Teknik Informatika
Depositing User:	Faldy Irwiensyah
Date Deposited:	18 Dec 2025 08:44
Last Modified:	18 Dec 2025 08:44
URI:	http://repository.uhamka.ac.id/id/eprint/48551

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