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All Journal JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Sistem informasi dan informatika (SIMIKA) Jutsi: Jurnal Teknologi dan Sistem Informasi J-Com (Journal of Computer) Jurnal Bangun Abdimas Journal of Applied Business and Technology Jurnal Teknologi Ilmu Komputer
Ramdhan, William
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Arts Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Materials Science & Nanotechnology Public Health Social Sciences Other

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Journal : Journal of Applied Business and Technology

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Transfer Learning Model Evaluation on CNN Algorithm: Indonesian Sign Language System (SIBI) Jollyta, Deny; Prihandoko, Prihandoko; Johan, Johan; Ramdhan, William; Santoso, Erick
Journal of Applied Business and Technology Vol. 6 No. 2 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i2.213

Abstract

In Indonesia as much as elsewhere, the deaf can communicate using sign language. The Indonesian Sign Language System (SIBI) is one of the sign language systems used in Indonesia. A model produced by the Convolutional Neural Network (CNN) method can be used in computer science for the recognition of sign language. By using the Transfer Learning paradigm, CNN's performance may be enhanced. However, not many researches have been conducted to assess the effectiveness of transfer learning on sign language models, particularly those that use the TensorFlow library. In fact, the evaluation results can influence the selection of the transfer learning model together with CNN. This study aims to evaluate the efficacy of using the CNN model for SIBI sign language through Transfer Learning. The data used are images of 24 SIBI alphabets and are processed through the TensorFlow library. The images will be recognized through the transfer learning performance of 6 models, namely VGG16, VGG19, Resnet50, Desenet121, Inception-V3 and MobileNet-V2. The results of the study found that through the TensorFlow library, Mobilenetv2 had the highest accuracy of 78% after 20 epochs.
Co-Authors Alfina, Chalista Amalia Amalia Amalia, Laily Rizky Chaniago, Arif Syarifuddin Deny Jollyta dermawan, ari Erick Santoso Farenza, Dinda Novri Ida Ayu Putu Sri Widnyani Ihsan, Mhd. Ihsan Ishan, Mhd Johan Johan Kifti, Wan Mariatul Mariatul Kifti, Wan Marpaung, Darmiyanti Nabila, Inaya Nadia Nadia Nursia, Akris Nursia Nurwati Nurwati Parini Prihandoko Prihandoko Rahayu, Elly Safitri, Lisda sahren, sahren SANTOSO SANTOSO Sri Amelia, Sri Sudarmin Sudarmin Susilo, Widya Amanda Syahputra, Abdul Karim Tiara, Adinda Zein, Jihan Raihan