relation: http://repository.uhamka.ac.id/id/eprint/48551/
title: Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time
creator: 1, 1
subject: T Technology (General)
description: 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.
date: 2025-07-12
type: Article
type: PeerReviewed
format: text
language: en
identifier: http://repository.uhamka.ac.id/id/eprint/48551/1/artikel4%20Josh%20%28Implementasi%20Metode%20Teachable%20Machine...%29-Faldy.pdf
identifier:   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