Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization International Journal of Trends in Accounting Research
Rahayu, Syarifah Bahiyah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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

Found 2 Documents
Search

 1 
The Sustainability of Cash Waqf Using Blockchain Technology: a Conceptual Study Anwar, Intan Fatimah; Rahayu, Syarifah Bahiyah; Yusoff, Yuslina; Lajin, Noor Faizah Mohd; Atan, Atika
INTERNATIONAL JOURNAL OF TRENDS IN ACCOUNTING RESEARCH Vol. 5 No. 2 (2024): International Journal of Trends in Accounting Research (IJTAR)
Publisher : Asosiasi Dosen Akuntansi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54951/ijtar.v5i2.664

Abstract

This paper explores the potential of integrating blockchain technology to enhance the sustainability of cash waqf, a significant Islamic endowment supporting charitable causes. Traditional cash waqf systems are facing challenges such as opacity, accountability gaps and inefficiency. The study provides a comprehensive overview of cash waqf and blockchain technology, exploring their synergies and potential in managing cash waqf initiatives. Through a detailed examination of challenges and opportunities, the paper establishes a robust research framework incorporating experience, knowledge, and blockchain to influence cash waqf sustainability. The proposed integration includes designing a blockchain architecture, implementing a blockchain-based cash waqf system and conducting security and privacy assessments for long-term viability. Research findings show blockchain's transformative impact, demonstrating its potential to enhance transparency, reduce fraud, increase accountability, and improve overall efficiency in managing and distributing cash waqf funds. For future work, continued exploration is needed to implement and assess the proposed blockchain integration in real-world cash waqf scenarios. The effectiveness of the system should be evaluated over an extended period, considering diverse community needs and regulatory contexts. In conclusion, conceptual research lays a foundation for a more sustainable future for cash waqf in redefining the landscape of charitable activities
Comparison Analysis of CXR Images in Detecting Pneumonia Using VGG16 and ResNet50 Convolution Neural Network Model Izdihar, Nur; Rahayu, Syarifah Bahiyah; Venkatesan, K
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2258

Abstract

Pneumonia is a lung disease that causes serious fatalities worldwide. Pneumonia can be complicated for medical professionals to identify since it shares similarities with other lung diseases like lung cancer and cardiomegaly. Hospitals face difficulty finding professional radiologists who help to detect pneumonia through radioactive processes. This research proposes VGG16 and ResNet50-based system architecture using the Convolutional Neural Network (CNN) module, which allows the detection of pneumonia. This research identifies pneumonia using chest X-ray (CXR) images through VGG16 and ResNet50 of CNN model architectures. The performance of the proposed models is compared by performance parameters such as processing time, accuracy, and loss. The Pneumonia dataset was obtained from Kaggle and divided into 70% for training, 15 % for validation, and 15% for testing. The results show that the proposed ResNet50 model architecture has a better result than the VGG16 model architecture. It can be clearly observed based on both models' loss and accuracy results. Moreover, the processing time for ResNet50 in training and predicting the CXR images is much faster than the VGG16 model's processing time. Hence, ResNet50 performs better than VGG16 based on the result of loss and accuracy and the processing time for the model to train and predict the data. In conclusion, the findings show the capability of CNN models for detecting pneumonia in CXR images, thus reducing the burden of professional radiologists.
Co-Authors Anwar, Intan Fatimah Atan, Atika Izdihar, Nur Lajin, Noor Faizah Mohd Venkatesan, K Yusoff, Yuslina