%0 Journal Article
%@ 2686-228X
%A 1, 1
%D 2025
%F repository:48551
%J Journal of Information System Research (JOSH)
%N 4
%P 1879-1885
%T Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time
%U http://repository.uhamka.ac.id/id/eprint/48551/
%V 6
%X 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.