Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Communications in Science and Technology International Journal of Zakat (IJAZ)
Al Firdausi, Muhammad
Unknown Affiliation
Religion Engineering

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

Found 2 Documents
Search

 1 
The Zakat, Economic Growth, and Poverty Alleviation: An Artificial Neural Networks Analysis Muis, Abdullah Ahadish Shamad; Al Firdausi, Muhammad; Akbar, Chairil; Gamal, Shrouq; Saleh, Haitham
International Journal of Zakat Vol 9 No Special (2024): International Journal of Zakat
Publisher : Center of Strategic Studies (PUSKAS) BAZNAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37706/ijaz.v9iSpecial.467

Abstract

This research investigates the multifaceted relationship between zakat, economic growth (GDP), and poverty alleviation in Indonesia using Artificial Neural Networks (ANN). Our findings reveal that zakat has more prominent impact on poverty alleviation than on GDP. In particular, we identify a positive correlation between zakat distribution and GDP in Indonesia, indicating that higher zakat distribution contributes significantly to economic growth. It further reveals a negative correlation between zakat distribution and poverty, consistent with most earlier studies, suggesting that zakat significantly contributes to poverty alleviation in Indonesia. Our findings practically imply the potential economic significance of zakat in human development and solving poverty issues. Our research contributes to the novelty of Islamic philanthropic research with the power of advanced AI algorithms, offering insights that can inform policy decisions.
Concise convolutional neural network model for fault detection Al Firdausi, Muhammad; Ahmad, Shafiq
Communications in Science and Technology Vol 7 No 1 (2022)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.7.1.2022.746

Abstract

Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.
Co-Authors Abdullah Ahadish Shamad Muis Ahmad, Shafiq Chairil Akbar Gamal, Shrouq Saleh, Haitham