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All Journal Jurnal Simetris JUITA : Jurnal Informatika JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL REKAYASA TEKNOLOGI INFORMASI IJISTECH (International Journal Of Information System & Technology) Jurnal Sistem Cerdas Innovation in Research of Informatics (INNOVATICS) IJISTECH Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Husni Mubarok
Prodi Informatika, Fakultas Teknik, Universitas Siliwangi
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models Aradea; Rianto; Husni Mubarok
Scientific Journal of Informatics Vol. 11 No. 3: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.6494

Abstract

Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.
Co-Authors Aay Ramdan ramdan Alfi Andryan Suryadi Andi Nur Rachman Aradea Aradea Aradea Euis Nur Fitriani Dewi Gunawan, Rohmat Hadi Purwadi Hariska Paunsyah Heni Sulastri Mitha Maharani Muhammad Anwari Nur Widiyasono Nur Widyasono Rahmi Nur Shofa Rianto Rianto, Rianto Sidik Madiyanto Syifa Sefia Iasha Vini Indri Yani