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All Journal Bulletin of Electrical Engineering and Informatics kawanad
Candra Zonyfar
Sun Moon University
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering

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Sosialisasi Aplikasi Penghitung Zakat bagi Masyarakat Taufiq Iqbal; Candra Zonyfar; Fuadi; Ijal Fahmi; Syamsul Rizal; Ismail
Kawanad : Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 1 (2023): March
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/kjpkm.v2i1.103

Abstract

Zakat is important for encouraging social awareness and correcting economic inequality, but there are still many people who do not understand how to calculate it due to several factors such as lack of understanding, outreach, and difficulty in manual calculations. In February 2023, a socialization of the zakat counter application was held for the people of Bireun Regency. This activity aims to increase understanding of zakat and facilitate the process of calculating zakat. In this event, the public was given an explanation on how to use the zakat counter application and it was hoped that they would be able to calculate zakat more easily and accurately. The expected results are increasing awareness about the importance of zakat and benefiting from the zakat counter application in helping the process of calculating zakat more effectively and efficiently. With the zakat counter application, it is hoped that it can increase the amount of zakat collected to help people in need. In addition, this socialization is also expected to strengthen cooperation between the government and the community in developing a better zakat calculating application in the future. Therefore, organizing the socialization of the zakat calculating application in Bireun Regency is an important step to increase public awareness about zakat and facilitate the process of calculating zakat effectively and efficiently.
Transfer learning-based malnutrition classification using VGG16 and comparative analysis of CNN architectures Ahmad Fauzi; Haerul Yuda Aditiya; Maharina Maharina; Sihabudin Sihabidin; Muhammad Ansari Adista; Iflan Naufal; Natasya Eka Nanda Sonia Puri; Candra Zonyfar
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11346

Abstract

Childhood malnutrition remains a critical public health challenge in developing countries, with Indonesia ranking fifth globally for stunting burden. Traditional anthropometric assessment methods are time-consuming, resource-intensive, and require trained personnel, necessitating efficient computer-based early detection approaches. This study proposes a deep learning-based method for automated nutritional status classification using facial image analysis. We developed and compared multiple transfer learning architectures including visual geometry group 16 (VGG16), densely connected convolutional network 121 (DenseNet121), mobile network version 2 (MobileNetV2), and residual network 50 (ResNet50) for classifying children into three categories: healthy, malnutrition, and stunting. Results demonstrated that VGG16, a simpler architecture trained for only 10 epochs, achieved optimal performance with 91.8% accuracy, and significantly outperforming more complex modern architectures like ResNet50. This finding challenges the conventional assumption that newer, deeper models invariably perform better, and particularly when working with limited medical datasets. The study revealed that longer training durations led to performance degradation due to overfitting, emphasizing the importance of balancing model complexity with dataset characteristics. These findings support the development of practical artificial intelligence (AI)-based malnutrition screening systems suitable for resource-constrained environments, potentially improving early detection capabilities, and public health outcomes in developing regions.
Co-Authors Ahmad Fauzi Fuadi Fuadi Haerul Yuda Aditiya Iflan Naufal Ijal Fahmi Ismail Maharina Maharina Muhammad Ansari Adista Natasya Eka Nanda Sonia Puri Sihabudin Sihabidin Syamsul Rizal Taufiq Iqbal