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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Pratama, Ridho Danang Budi
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Computer Science & IT Decision Sciences, Operations Research & Management

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Implementasi Metode Teachable Machine Untuk Pengidentifikasian Ekspresi Wajah Secara Real-Time Pratama, Ridho Danang Budi; Irwiensyah, Faldy
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7819

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.
Co-Authors Faldy Irwiensyah, Faldy