@article{repository48551,
         journal = {Journal of Information System Research (JOSH)},
          volume = {6},
           pages = {1879--1885},
            year = {2025},
           title = {Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time},
          number = {4},
           month = {July},
          author = {1, 1},
        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.},
             url = {http://repository.uhamka.ac.id/id/eprint/48551/},
            issn = {2686-228X}
}