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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Arita Witanti
Universitas Mercu Buana Yogyakarta, Sleman
Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Ras Kelinci Menggunakan Convolutional Neural Network (CNN) untuk Optimasi Sistem Identifikasi Visual Maasyaril Kirom Mi’Rojul Huda; Arita Witanti
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6627

Abstract

Rabbits are mammals that come in many varieties with unique and diverse physical characteristics. Differentiating various types of rabbits, especially those with physical similarities and color patterns, is a challenge for some people because of their similar visual appearance. The purpose of this research is to develop a Convolutional Neural Network (CNN)-based rabbit breed classification system using MobileNetV3 architecture. A dataset of 1,500 images of three rabbit breeds (bligon, hyla, and new zealand white) was processed through resizing, augmentation, and normalization to improve data quality. The model was trained using Adam's optimizer with 97% accuracy on the validation data and 90% on the external dataset, showing good generalization ability. These results confirm the effectiveness of CNNs over manual methods in visual pattern recognition, while overcoming time constraints and human error. However, limitations in dataset variations, such as lighting and image capture angle, affect the generalization of the model. This research not only supports the efficiency of livestock management but also shows the great potential of AI application in Indonesia's livestock sector. Development of more diverse datasets and exploration of other model architectures are recommended for future performance improvements.
Implementasi Algoritma YOLOv11 untuk Sistem Klasifikasi Kelayakan Setor Sampah Anorganik dalam Pengelolaan Bank Sampah Fredy Saputro; Arita Witanti
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7486

Abstract

Inorganic waste management in waste banks faces challenges in sorting and quality evaluation processes that still rely on manual methods with high levels of subjectivity. Bank Sampah 34 Ngasemrejo experiences problems with community uncertainty regarding waste eligibility standards, causing high material rejection rates and suboptimal community behavior in waste deposit. Therefore, this research aims to develop an automatic inorganic waste eligibility detection system before depositing to waste banks. This research develops an inorganic waste eligibility detection system based on computer vision using the You Only Look Once version 11 (YOLOv11) algorithm to classify plastic bottles, duplex, and newspaper waste based on eligible and ineligible physical conditions for deposit. The research dataset consists of 2,800 images divided into 70% training data, 20% validation data, and 10% testing data. Data preprocessing was performed using the Roboflow platform including annotation, augmentation, and resize to 640x640 pixels. The YOLOv11n model was trained for 50 epochs with optimized hyperparameters. Evaluation results show excellent performance with mAP50 of 99.4%, mAP50-95 of 95.8%, precision rate of 98.3%, and recall of 98.6%. Testing on testing data shows that the system can accurately classify the eligibility of inorganic waste according to waste bank standards. This system is expected to help residents sort waste independently, improve waste bank operational efficiency, and support higher quality and sustainable recycling processes.
Co-Authors Fredy Saputro Maasyaril Kirom Mi’Rojul Huda