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All Journal Signal and Image Processing Letters International Journal of Artificial Intelligence and Science
Khoiriyah, Rofiatul
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Artificial Neural Network for Corn Quality Classification Based on Seed Damage and Aflatoxin Attributes Maulida, Innayah; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.119

Abstract

Corn plays a critical role in Indonesia’s agricultural sector, functioning as both a staple food for human consumption and a key component of livestock feed. However, its quality is frequently compromised by factors such as mechanical damage during harvesting, fungal contamination, and fluctuating climate conditions, all of which pose challenges to maintaining consistent standards. Traditionally, corn quality classification relies on manual methods, which are not only time-consuming but also prone to human error and inconsistency. To address these limitations, this study employs a Neural Network approach to classify corn into two distinct categories: breeder and commercial grades. The research utilizes a dataset of 2,026 records, meticulously divided into 70% for training, 20% for validation, and 10% for testing, ensuring robust model evaluation. The methodology includes comprehensive data preprocessing, feature standardization to normalize input variables, and hyperparameter optimization, with the model trained over 100 epochs using a batch size of 32 and a learning rate of 0.001. The results demonstrate exceptional performance, achieving an accuracy of 99.5%, precision of 98.3%, recall of 100%, and an F1-score of 99.1%, as validated by a confusion matrix that highlights the model’s classification reliability. This automated system significantly enhances the efficiency and accuracy of corn quality assessment, offering a scalable solution to replace outdated manual techniques. By providing a reliable tool for quality differentiation, this study supports Indonesia’s agricultural and livestock industries, with potential for broader application in optimizing crop management and ensuring food security under varying environmental conditions.
Vehicle Detection and Tracking using Coarse-to-Fine Module and Spatial Pyramid Pooling–Fast with Deep Sort Saputri, Anita Nur Widdia; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.118

Abstract

Semarang City, a rapidly growing urban area in Indonesia, faces significant traffic challenges stemming from the widespread use of motorcycles, an inefficient public transportation system, and accelerated urban development. These factors contribute to congestion and complicate traffic management efforts. To address this issue and enhance monitoring capabilities, this study develops an automatic vehicle detection system utilizing the YOLOv8 algorithm, applied to CCTV footage obtained from TILIK SEMAR, a local traffic surveillance initiative. The research methodology encompasses several key stages: data collection from real-world traffic scenarios, meticulous annotation of vehicle types, model training using the YOLOv8 framework, and performance evaluation conducted at two distinct locations in Semarang—Banyumanik and Thamrin Pandanaran. The trained model achieved an impressive average accuracy, measured as mean Average Precision (mAP50), exceeding 97%, with a rapid processing time of 4.2 milliseconds per image, making it suitable for real-time applications. Among vehicle categories, the highest detection accuracies were recorded for buses at 99.3% and box trucks at 99.5%, reflecting the model’s robustness for larger vehicles. However, motorcycles presented a challenge, with a lower mAP50-95 score of 64.3%, attributed to variations in shape, size, and lighting conditions. Overall, the system successfully identified 96.77% of 3,036 vehicles across the test dataset, demonstrating strong generalization across diverse traffic conditions. These findings validate YOLOv8 as an effective tool for real-time traffic monitoring in urban settings. Future enhancements will focus on expanding dataset diversity and improving performance under challenging environmental factors, such as adverse weather or low-light scenarios, to further refine the system’s reliability.
Vehicle Detection on The Traffic Using Detection Transformer (DETR) Algorithm Khoiriyah, Rofiatul; Hendrawan, Aria
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.4

Abstract

Object detection is a computer vision technique aimed at detecting and identifying objects in images or videos. In recent years, with advancements in Machine Learning and Deep Learning, object detection has made significant progress in various fields such as healthcare, security, and transportation. The DETR algorithm is a novel approach in object detection that combines transformer architecture with attention techniques to address object detection challenges. This research applies the DETR algorithm with ResNet backbone for vehicle detection on the roads, involving 6 object classes: Car, Truck, Bus, Motorcycle, Pickup Car, and Truck Box. Four training experiments were conducted: DETR-ResNet50, DETR-ResNet101, DETR-DC5-ResNet50, and DETR-DC5-ResNet101. The implementation results show that DETR-DC5 improves the accuracy of vehicle detection. DETR-DC5 with ResNet-101 achieved the highest score for AP50, which is 0.957. However, it should be noted that DETR-DC5 with ResNet-50 managed to maintain overall AP stability, with a lower parameter of 35.5. The model's outcomes in this study can be effectively applied for vehicle detection on the roads.
Traffic Vehicle Detection Using Faster R-CNN: A Comparative Analysis of Backbone Architectures Hakim, Luqman; Hendrawan, Aria; Khoiriyah, Rofiatul
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.5

Abstract

Object detection is a crucial task in computer vision, where advanced deep learning models have shown significant improvements over traditional methods. In this study, the Faster R-CNN algorithm is applied to a traffic dataset containing six vehicle categories: Bus, Car, Motorcycle, Pick Up Car, Truck, and Truck Box. The novelty of the research lies in the comparison of four backbone architectures ResNet50, ResNet50V2, MobileNetV3 Large, and MobileNetV3 Large 320 evaluated for their performance in vehicle detection at IoU thresholds of 0.5 and 0.75. The results reveal that ResNet50 provided the best overall performance, achieving mAP scores of 0.966 at IoU 0.5 and 0.887 at IoU 0.75, offering a balanced trade-off between precision and recall. ResNet50V2 and MobileNetV3 Large also performed well, with mAP scores of 0.945 and 0.870 for ResNet50V2, and 0.969 and 0.843 for MobileNetV3 Large, respectively. However, MobileNetV3 Large 320 showed the lowest detection performance, with mAP scores of 0.857 at IoU 0.5 and 0.551 at IoU 0.75. These findings provide useful insights into the suitability of different architectures for vehicle detection tasks, particularly in traffic surveillance applications.
Co-Authors Aria Hendrawan, Aria Luqman Hakim Maulida, Innayah Saputri, Anita Nur Widdia