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All Journal Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Conference on Innovation and Application of Science and Technology (CIASTECH) Infotekmesin Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Rainstek : Jurnal Terapan Sains dan Teknologi Journal of Mechanical and Electrical Technology Jurnal Bina Komputer Jurnal INFOTEL
Irawan, Novta Danyel
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences

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Optimalisasi Sistem Pelanggaraan Lalu lintas Kendaraan Besar Menggunakan Algoritma Mobilnet-SSD dan Pengolahan Citra K-Nearest Neighbors (KNN) Okiandri, Diki; Irawan, Novta Danyel; Ardian, Yusriel; Nurdin, Shafiq; Siswoko, Muhammad Urfun Nuurrazzaq
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2743

Abstract

This study is motivated by the high rate of traffic violations involving large vehicles such as trucks and buses, particularly on city protocol roads where these types of vehicles are prohibited. Most existing e-ticketing (e-Tilang) systems are still unable to automatically detect large vehicles in real-time and simultaneously read their license plates accurately. This limitation represents a research gap that this study aims to address. The objective of this research is to develop an integrated system that automatically detects violations committed by large vehicles and reads their license plates. The system employs the MobileNet-SSD algorithm for detecting large vehicles from traffic video data and applies the K-Nearest Neighbors (KNN) method for license plate character recognition. Based on tests conducted on five traffic videos under various conditions (morning, afternoon, night), the system achieved a detection accuracy of 90% for large vehicles and an 80% accuracy in license plate recognition. The system performed optimally during daylight hours but showed reduced accuracy at night due to limited lighting conditions. The integration of these two methods has proven to be effective and feasible for real-time law enforcement systems, especially in areas with limited computational resources.
Comparative Performance Analysis of Modified VGG16 and Slim-CNN for Arabica Coffee Bean Defect Classification Ardian, Yusriel; Astawa, I Nyoman gede Arya; Irawan, Novta Danyel; Pradnyana, I Putu Bagus Arya; Sulistyo, Agung
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3244.85-96

Abstract

Defect detection in Arabica coffee beans is a critical aspect of quality control, particularly for export-oriented commodities that require consistent visual standards and uniform quality across production batches. Black and partial-black defects are known to significantly affect market value, quality perception, and sensory characteristics. Meanwhile, manual inspection processes remain vulnerable to evaluator subjectivity and inter-operator inconsistency.This study aims to conduct a comparative analysis between a Modified VGG16 architecture and Slim-CNN for detecting these two defect categories using a deep learning-based Convolutional Neural Network (CNN) approach. The dataset consists of 4,080 high-resolution images of Arabica green coffee beans captured using a 24.2 MP macro camera under controlled lighting conditions to minimize shadows and visual distortion. To preserve the natural characteristics of the defects, minimal data augmentation was applied using cropping and 15-degree rotation techniques. The Modified VGG16 architecture was simplified by reducing the complexity of the fully connected layers, integrating batch normalization, and applying dropout to enhance training stability and computational efficiency. Slim-CNN was employed as a lightweight comparative model with fewer parameters and lower memory requirements, making it suitable for resource-constrained deployment scenarios. Four training schemes were evaluated using variations in learning rate and epoch number to assess configuration impacts on performance. Experimental results show that Modified VGG16 achieved the highest test accuracy of 86.7% at a learning rate of 0.001 with 3 epochs, demonstrating a strong balance between training and validation accuracy. Slim-CNN exhibited shorter training time and lower computational complexity, although with slightly lower classification accuracy compared to Modified VGG16. These findings highlight a trade-off between classification performance and computational efficiency in selecting CNN architectures for coffee bean defect detection. Although the results demonstrate potential for industrial automatic classification systems, further validation using larger datasets and more comprehensive evaluation schemes is required to improve model generalization. This study contributes to the development of a more measurable, adaptive, and efficient deep learning-based coffee quality inspection system to support agro-export industry requirements.
Co-Authors Arianti Kusumawardhani Djoko Adi Susilo Dwiyanto, Felix Andika I D, Riski Nur I Nyoman Gede Arya Astawa Muhammad Athoillah Okiandri, Diki Onny Setyawati Pradnyana, I Putu Bagus Arya Purnama, Ida Bagus Irawan Riski Nur Istiqomah Dinnullah Shafiq Nurdin Shafiq Nurdin Siswoko, Muhammad Urfun Nuurrazzaq Sulistyo, Agung Sutoko, Sutoko Wijono Wijono Wijono Yordano Dwi Yunan Bramasta Yunus E. P., Amak Yusriel Ardian