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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Green Engineering: Journal of Engineering and Applied Science
Rizqi Elmuna Hidayah
Universitas Islam Kalimantan Muhammad Arsyad Al-Banjari Banjarmasin
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Enhanced InceptionV3 Transfer Learning with Augmentation Strategy for Multi-Class Fruit Classification Abdul Karim; Fakhri Lambardo; Rizqi Elmuna Hidayah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7531

Abstract

Fruit variety recognition using digital images is an important component in developing automated systems for agricultural handling and food-industry processing. The task is difficult because different fruit types may present nearly identical visual patterns, while image acquisition factors such as illumination, viewing position, and image quality can reduce classification reliability. To address this issue, this study designed a deep learning model for 131 fruit classes by adapting InceptionV3 through a transfer learning scheme. The pre-trained feature extraction layers were retained without retraining, while the original output structure was replaced with task-specific layers consisting of global average pooling, a 1024-unit dense layer with ReLU activation, and a softmax classifier. The image data were standardized to 224 × 224 pixels, augmented to increase visual variation, and divided into training, validation, and testing subsets using an 80:10:10 ratio. The proposed model produced an accuracy of 99.80%, with precision, recall, and F1-score values of 0.9900. These results exceeded the performance of GoogLeNet, ResNet, and VGGNet, showing that the use of pre-trained InceptionV3 features, customized classification layers, and augmentation can improve prediction consistency and reduce classification errors. Further evaluation on unconstrained real-world images and optimization for real-time use are recommended for future development.
Integration of Advanced Biodegradable Polymer Coatings with Solar-Powered Textile Waste Treatment for Reducing Microplastic Pollution in Urban Runoff Systems Rizqi Elmuna Hidayah; Yohandika Tri Apriliyanto; Beta Arya Ash Shidik
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 1 (2025): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i1.280

Abstract

Microplastic pollution, particularly from textile waste, has become a significant environmental concern, especially in urban runoff systems. These pollutants pose a considerable threat to water quality, aquatic life, and human health. Traditional wastewater treatment methods often fall short in addressing the complexities of microplastic contamination. This research explores the integration of advanced biodegradable polymer coatings with solar-powered textile waste treatment to reduce microplastic pollution in urban runoff systems. Biodegradable polymers, such as polylactic acid (PLA) and polyhydroxyalkanoates (PHA), are highlighted for their potential to efficiently filter microplastics while providing an eco-friendly alternative to conventional filtration technologies. By combining these materials with a small solar-powered unit, the prototype enables an off-grid, low-energy solution to treat textile wastewater in urban environments. The study includes testing the prototype in simulated urban runoff conditions with varying concentrations of microplastics, evaluating key performance indicators such as microplastic removal efficiency, energy consumption, and operational sustainability. Results demonstrate a significant reduction in microplastic concentration, indicating the effectiveness of biodegradable polymer coatings and solar-powered systems in treating urban runoff. The discussion addresses the feasibility of using local biodegradable materials, performance in real-world urban environments, and operational challenges such as maintenance and scalability. This innovative approach is compared with existing microplastic filtration methods, such as membrane filtration and adsorption, highlighting its advantages in terms of sustainability and cost-effectiveness. The findings suggest that this integrated system could offer a viable, low-cost solution for addressing microplastic pollution in urban drainage systems, with potential for widespread urban implementation.
Co-Authors Abdul Karim Beta Arya Ash Shidik Lambardo, Fakhri Yohandika Tri Apriliyanto