دورية أكاديمية
Classifying BISINDO Alphabet using TensorFlow Object Detection API
| العنوان: | Classifying BISINDO Alphabet using TensorFlow Object Detection API |
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| المؤلفون: | Lilis Nur Hayati, Anik Nur Handayani, Wahyu Sakti Gunawan Irianto, Rosa Andrie Asmara, Dolly Indra, Muhammad Fahmi |
| المصدر: | Ilkom Jurnal Ilmiah, Vol 15, Iss 2, Pp 358-364 (2023) |
| بيانات النشر: | Fakultas Ilmu Komputer UMI, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | artificial intelligence, bisindo, computer vision, real-time, tensorflow object detection api, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English Indonesian |
| تدمد: | 2087-1716 2548-7779 |
| Relation: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1692; https://doaj.org/toc/2087-1716; https://doaj.org/toc/2548-7779 |
| DOI: | 10.33096/ilkom.v15i2.1692.358-364 |
| URL الوصول: | https://doaj.org/article/605049b9c9b34cd7bf4f2a9a97fee679 |
| رقم الأكسشن: | edsdoj.605049b9c9b34cd7bf4f2a9a97fee679 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20871716 25487779 |
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| DOI: | 10.33096/ilkom.v15i2.1692.358-364 |