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Comparison of CNN’s Architecture GoogleNet, AlexNet, VGG-16, Lenet -5, Resnet-50 in Arabic Handwriting Pattern Recognition Nugraha, Gibran Satya; Darmawan, Muhammad Ilham; Dwiyansaputra, Ramaditia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 2, May 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i2.1667

Abstract

The Arabic script is written from right to left and consists of 28 characters, with no capital or lowercase letters. The Arabic script has several orthographic and morphological properties that make handwriting recognition of the Arabic script challenging. In addition, one of the biggest challenges in recognizing Arabic script patterns is the different handwriting styles and characters of each person's writing. The authors propose a study to compare the accuracy of handwriting pattern recognition in Arabic script which has been done previously by comparing five CNN architectures, namely GoogleNet, AlexNet, VGG-16, LeNet-5, and ResNet-50. Considering that previous research has not obtained excellent accuracy. The number of datasets used is 8400 image data and the most optimal comparison of testing and training data is 80:20. Based on the research that has been done, there are several things that the author can conclude. The model is made using 64 filters for each convolution layer because the optimal size is used for 5 architectures, kernel size is 3x3, neurons is 128, dropout weight is 50% to reduce overfitting, learning rate is 0.001, image size is 64x64, the normalization method with the ReLU activation function, and 1-dimensional input image (grayscale), and with a comparison of testing and training data of 80:20. The VGG-16 architectural model is the architecture that gets the highest score, namely 83.99%. This can have good potential to be developed as a medium for learning Arabic script.
Implementasi Digitalisasi Sekolah Dasar sebagai Upaya Penguatan Kinerja Sekolah Dasar berbasis Transformasi Digital melalui Program PkM KKN Arum, Wahyu Sri Ambar; Maksum, Arifin; Sakmal, Juhana; Darmawan, Muhammad Ilham; Putri, Neta Yolanda; Septiani, Nabillah Syahfa; Handayani, Lia; Aisy, Assifa Rohadatul; Dewiyani, Leola; Marini, Arita
Amaliah: Jurnal Pengabdian Kepada Masyarakat Vol 9 No 1 (2025): Amaliah Jurnal: Pengabdian kepada Masyarakat
Publisher : LPPI UMN AL WASHLIYAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32696/ajpkm.v9i1.5165

Abstract

The teacher mentoring activity carried out through the Community Service Program (PkM) integrated with the Student Community Service (KKN) at SDN Pluit 03 primarily aims to support digital transformation in elementary education as a strategic effort to enhance school performance. This transformation is implemented by providing broad access to digital learning resources that can be accessed anytime and anywhere, thereby overcoming spatial and temporal limitations in the learning process. Additionally, the activity is specifically designed to strengthen the implementation of the Merdeka Curriculum, with an emphasis on project-based learning that promotes independent learning and knowledge exploration. In this context, strengthening digital literacy among teachers and students becomes crucial—not only to improve technological capabilities but also to broaden the range of richer and more varied teaching materials. The benefits of this empowerment initiative include improved teacher competencies in utilizing various digital tools and online learning resources, which in turn support the effectiveness of the teaching and learning process. Besides offering practical insights into digitalization, the program also fosters new habits among teachers to be more adaptive and open to the use of educational technology, enabling them to design lessons that are more responsive to student needs while remaining aligned with the flexible and student-centered principles of the Merdeka Curriculum.