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All Journal JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science Madani: Multidisciplinary Scientific Journal
Umair, Muhammad
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences Other

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Kemampuan Penerapan Aturan Tata Bahasa Dalam Penulisan Bahasa Arab Emiyiati, Rifka; Amaliah, Rizaktul; Alfayed, Muhammad; Umair, Muhammad; Nasution, Sahkholid
Madani: Jurnal Ilmiah Multidisiplin Vol 1, No 12 (2024): Madani, Vol. 1 No. 12 2024
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10452255

Abstract

Arabic language learning in Islamic educational institutions in Indonesia, especially in madrasahs, faces challenges primarily stemming from teacher weaknesses, suboptimal curricula, and often unsystematic materials. It is crucial to develop integrated learning materials that focus on grammar, consider difficulty levels, and provide practical exercises to enhance interest and learning outcomes. Efforts to improve the quality of Arabic language education should concentrate on teacher training to boost competence. Additionally, curriculum revisions, emphasizing the development of materials aligned with learners' needs, can be a strategic step. Ensuring active student involvement by creating engaging materials that follow challenging yet accessible learning principles is also essential. With these steps, it is anticipated that Arabic language learning in madrasahs will undergo significant positive changes.
Practical Evaluation of Federated Learning in Edge AI for IoT Pal, Sauryadeep; Umair, Muhammad; Tan, Wooi-Haw; Foo, Yee-Loo
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2329

Abstract

AI running locally on IoT Edge devices is called Edge AI. Federated Learning (FL) is a Machine Learning (ML) technique that builds upon the concept of distributed computing and preserves data privacy while still supporting trainable AI models. This paper evaluates the FL regarding practical CPU usage and training time. Additionally, the paper presents how biased IoT Edge clients affect the performance of an AI model. Existing literature on the performance of FL indicates that it is sensitive to imbalanced data distributions and does not easily converge in the presence of heterogeneous data. Furthermore, model training uses significant on-device resources, and low-power IoT devices cannot train complex ML models. This paper investigates optimal training parameters to make FL more performant and researches the use of model compression to make FL more accessible to IoT Edge devices. First, a flexible test environment is created that can emulate clients with biased data samples. Each compressed version of the ML model is used for FL. Evaluation is done regarding resources used and the overall ML model performance. Our current study shows an accuracy improvement of 1.16% from modifying training parameters, but a balance is needed to prevent overfitting. Model compression can reduce resource usage by 5.42% but tends to accelerate overfitting and increase model loss by 9.35%.
A simulation-based investigation into the bidirectional charge and discharge dynamics in lead-acid batteries Noor Zelan, Muhammad Aiman; Hidayat, Muhammad Nabil; Nik Ali, Nik Hakimi; Umair, Muhammad; Mohd Mawardi, Muhammad Izzul; Ahmad, Ahmad Sukri; Abdullah, Ezmin
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp783-796

Abstract

This paper presents a comprehensive simulation-based investigation into the bidirectional charge and discharge dynamics of lead-acid batteries within electric vehicles (EVs) and energy storage systems (ESS). Utilizing a bidirectional DC-DC converter (BDC) integrated with a lead-acid battery, the study explores the performance of these batteries through various charging and discharging scenarios. The simulation model, implemented using MATLAB, assesses the impact of charging strategies on battery behavior, focusing on key metrics such as state of charge (SOC), energy performance, and charging rates. The results reveal that lead-acid batteries, when paired with appropriate charging infrastructure and strategies, demonstrate enhanced performance and reliability in both EV and ESS applications. The study highlights the significant role of BDC topology in facilitating efficient energy transfer and optimizing battery usage. The findings underscore the potential for improved performance and widespread adoption of bidirectional converters in sustainable energy solution.
Co-Authors Abdullah, Ezmin Ahmad, Ahmad Sukri Alfayed, Muhammad Amaliah, Rizaktul Emiyiati, Rifka Foo, Yee-Loo Hidayat, Muhammad Nabil Mohd Mawardi, Muhammad Izzul Nik Ali, Nik Hakimi Noor Zelan, Muhammad Aiman Pal, Sauryadeep Sahkholid Nasution Tan, Wooi-Haw