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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) JSAI (Journal Scientific and Applied Informatics) International Journal of Informatics and Computation REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal E-Komtek Jurnal Teknologi Informatika dan Komputer Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Ilmiah Wahana Pendidikan Journal of Technology and Informatics (JoTI) Bulletin of Information Technology (BIT) Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Pelitabangsa JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah VIDHEAS: Jurnal Nasional Abdimas Multidisiplin SAINTEK International Journal of Integrated Science and Technology Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Akademika Jurnal Teknologi Pendidikan
Ahmad Turmudi Zy
UNiversitas Pelita Bangsa
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Eye Disease Detection and Classification Optimization Using EfficientNet-B5 with Emphasis on Data Augmentation and Fine-Tuning Anggi Muhammad Rifai; Muhammad Fatchan; Ahmad Turmudi Zy; Donny Maulana; Sufajar Butsianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6519

Abstract

Eye diseases such as glaucoma, cataract, and diabetic retinopathy pose significant global health challenges, underscoring the need for accurate and efficient diagnostic systems. This study employed the EfficientNet-B5 model to enhance the detection and classification of eye diseases by incorporating advanced data augmentation and fine-tuning techniques. The research utilizes the Ocular Disease Intelligent Recognition (ODIR) dataset, consisting of 4,217 fundus images categorized into four classes: normal, glaucoma, cataract, and diabetic retinopathy. The methodology comprises three phases: baseline model training, model training with data augmentation, and fine-tuning. The baseline model achieved an accuracy of 60.43%, which improved to 63.03% with data augmentation—an increase of 2.6 percentage points. Fine-tuning further elevated the accuracy to 93.23%, representing a notable improvement of 33.8 percentage points over the baseline. Model performance was evaluated using standard classification metrics including accuracy, precision, recall, and F1-score. These findings demonstrate the technical efficacy of combining augmentation and fine-tuning to enhance model generalization. The proposed approach offers a robust framework for developing dependable AI-driven diagnostic tools to support early detection and facilitate informed clinical decision-making.
Advanced ANN Techniques for Precise Detection and Classification of Welding Defects Faza Ardan Kusuma; Muhammad Fatchan; Ahmad Turmudi Zy
International Journal of Integrated Science and Technology Vol. 2 No. 5 (2024): May 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijist.v2i5.1907

Abstract

The implementation of the artificial neural network (ANN) algorithm for detecting and classifying welding defects is detailed in this study. A total of 558 welding workpiece images were processed using techniques such as resizing, auto-orientation, flipping, rotation, and annotation, ultimately expanding the dataset to 1,288 images. Feature extraction identified 24 traits across 12,000 data points, which were then condensed to 5,735 data points for the ANN model. The model employed 100 hidden layers, the ReLU activation function, and the L-BFGS-B solver, running for 200 iterations. The configuration achieved near-perfect results, with metrics such as the area under the curve (AUC), classification accuracy, and F1 score averaging a precision of 0.97. These outcomes demonstrate the ANN model's high efficacy in detecting and classifying welding defects, underscoring its potential application for quality assurance in the welding industry. Further investigation into specific defect types, including porosity, spatter, cracks, and undercuts, could further improve detection accuracy.
UMKM Kreatif Karangbahagia Menuju Pasar Digital Syuhada, Wira; Maha Putra; Ahmad Turmudi Zy
Jurnal Ekonomi Manajemen Dan Bisnis (JEMB) Vol. 1 No. 6 (2024): Juli
Publisher : Publikasi Inspirasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62017/jemb.v1i6.1685

Abstract

Micro, Small and Medium Enterprises (MSMEs) in the creative industry sector have great potential in driving economic growth. However, the main challenge faced by MSMEs is limited market access, especially in the current digital economy era. The development of information technology and e-commerce platforms opens new opportunities for MSMEs to expand their marketing reach and increase competitiveness. This research aims to design a human resource development program for creative industry MSMEs in Cikarang Baru in effectively utilizing e-commerce platforms. The research methodology used is a mixed method approach with data collection techniques through surveys, in-depth interviews, and literature studies. Surveys are conducted to identify the profiles, challenges, and opportunities of creative industry MSMEs in Cikarang Baru. In-depth interviews are conducted to explore more information about the constraints and obstacles in utilizing e-commerce platforms as well as the need for human resource development. The research results show that most MSMEs have not optimally utilized e-commerce platforms due to limited knowledge and skills of human resources in managing online sales. The proposed development program includes training, assistance, and capacity building for human resources to operate and manage sales through e-commerce effectively. The development materials cover digital marketing strategies, product and content optimization, online store management, and customer service. With the improvement of human resource skills through this program, it is hoped that creative industry MSMEs in Cikarang Baru can increase market access, expand their marketing reach, and improve competitiveness in the digital economy era
Co-Authors Abdul Ghofir Agung Nugroho Ahmad Rivaldi Anggi Muhammad Rifa'i Anggi Muhammad Rifai Antika Zahrotul Kamalia Aprilia Nur Sa'adah Ary Fahrizal Aswan S Sunge Aswan S Sunge Aswan S. Sunge Aswan Supriyadi Sunge Bagas Pamungkas Butsianto, Sufajar Danang Nurcahyo Daspar Daspar Diani Putri Kusumaningrum Donny Maulana Edora Edora Edora Edora Edy Widodo Elkin Rilvani Endrik Ermanto Faiza Muhammad Julianto Faza Ardan Kusuma Gufranaka Samudra Irfan Afriantoro Irma Nurmaulida* Isarianto Juan Fakhri Lutfi Adji Ardiansyah M.Makmun Effendi Maha Putra Miftahuljannah Mudakir Muhamad David Ali Muhamad Fatchan Muhammad Adipa Muhammad Fatchan Muhammad Makmun Effendi Muhtajuddin Danny Nadya Khairunnisa Nugroho, Agung Putri Nurhafenda Harun Qori yumansyah Qori Ray Fathur Rizky Rhendy Diki Nugraha Riani Riani Rismawati Rismawati Rosa Noviyanti Sunita Dasman SUPRAPTO Suriyanti Suriyanti Wira Syuhada Wiyanto Wiyanto Zaenur Rozikin Zaky Ali Husaeni