Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Rekayasa Komputer Media Journal of General Computer Science (MJGCS)
Dinda Putri Ramadani
Universitas Dinamika Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
The Development of a Sign Language Detection System Using TensorFlow and CVzone Dinda Putri Ramadani; Prayitno; Praditya Oktanza Djaduk Wibisono; Mochammad Rizky Abadi Sutoyo
Media Journal of General Computer Science Vol. 3 No. 2 (2026): MJGCS
Publisher : MASE - Media Applied and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62205/mjgcs.v3i2.50

Abstract

Sign language is a communication system used to help deaf people interact with each other. In Indonesia, there are two main sign languages, namely Indonesian Sign Language (BISINDO) and Indonesian Sign Language System (SIBI), each of which has differences in the use of hands. This research develops a SIBI-based sign language recognition system using TensorFlow and CVzone. This system uses computer vision technology and machine learning to recognize SIBI alphabet hand gestures. The model is developed using a dataset captured by a camera and trained with TensorFlow to enable real-time gesture recognition, while CVzone facilitates effective gesture tracking. Test results show that the average accuracy in recognizing the SIBI alphabet reaches more than 90%, which reflects the system's good performance in recognizing the alphabet. It is expected that the accuracy of detecting hand gestures in sign language will be improved to support communication for the deaf community in Indonesia.
Penerapan Metode Transfer Learning Berbasis EfficientNet untuk Klasifikasi Tumor Otak pada Citra MRI Dinda Putri Ramadani; Kurniabudi; Lola Yorita Astri
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 6 No 1 (2026): JAKAKOM Vol 6 No 1 APRIL 2026
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jakakom.2026.6.1.2758

Abstract

Brain tumors are among the most dangerous diseases that require fast and accurate detection and diagnosis. One of the most commonly used medical imaging modalities for brain tumor diagnosis is Magnetic Resonance Imaging (MRI). However, manual identification and classification of brain tumors in MRI images are time-consuming and highly dependent on the expertise of medical specialists. Therefore, this study aims to apply a transfer learning method based on the EfficientNet-B5 architecture for automatic brain tumor classification in MRI images. The dataset used in this research is the Brain Tumor MRI Dataset obtained from the Kaggle platform, which consists of four classes: glioma, meningioma, pituitary tumor, and no tumor. The research stages include image preprocessing, data augmentation, dataset splitting into training, validation, and testing sets, as well as model training using a transfer learning approach. The EfficientNet-B5 architecture is utilized as a feature extractor with the addition of custom classification layers. Model performance is evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC metrics. The results show that the EfficientNet-B5 model achieves excellent classification performance with an accuracy of 98.75%, precision of 98%, recall of 98%, F1-score of 98%, and a ROC-AUC value of 0.99 in multi-class classification. These findings indicate that the EfficientNet-B5-based transfer learning approach is effective for classifying brain tumors in MRI images and has the potential to assist medical professionals in achieving faster and more accurate diagnostic processes.
Co-Authors Kurniabudi, Kurniabudi Lola Yorita Astri, Lola Yorita Mochammad Rizky Abadi Sutoyo Praditya Oktanza Djaduk Wibisono Prayitno