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All Journal Jurnal Teknologi dan Manajemen Informatika Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Mantik Journal Dynamic SainT BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Dedikasi Jurnal Pengabdian kepada Masyarakat Journal of Innovation Research and Knowledge INFINITY : UKI TORAJA JOURNAL OF INFORMATION TECHNOLOGY J-Icon : Jurnal Komputer dan Informatika TONGKONAN: Jurnal Pengabdian Masyarakat The Indonesian Journal of Computer Science
Juprianus Rusman
Program Studi Teknik Informatika, Universitas Kristen Indonesia Toraja, Tana Toraja, Sulawesi Selatan
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Transportation Veterinary Other

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Classification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural NetworkClassification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural Network Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rusman, Juprianus; Upa, Samrius; Utama, Muhammad Surya Alif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4781

Abstract

Occupational Safety and Health (OSH) is a critical aspect in high-risk work environments, where the consistent use of Personal Protective Equipment (PPE) plays a vital role in preventing workplace accidents. However, non-compliance with PPE regulations remains a significant issue, contributing to a high number of work-related injuries in Indonesia. This study proposes an automated detection and classification system for PPE usage, specifically helmets and vests, using the Backpropagation algorithm in artificial neural networks. A total of 100 images were utilized, equally divided between complete and incomplete PPE usage. The dataset was split into 60% training and 40% testing. Image segmentation was performed using HSV color space conversion and thresholding, followed by RGB color feature extraction. The Backpropagation algorithm was then employed for classification. Experimental results show an average accuracy of 90%, with precision, recall, and F-measure all reaching 0.9. Despite some misclassifications due to color similarity between helmets and head coverings, the model demonstrated robust performance with relatively low computational requirements. This study contributes to the field of computer vision and intelligent safety systems by demonstrating the practical effectiveness of lightweight ANN architectures for PPE detection in real-time industrial scenarios, thereby highlighting the potential of backpropagation as an adaptive and practical alternative to more complex deep learning approaches for real-time PPE detection in occupational safety monitoring systems.
Pest Detection on Green Mustard Plants Using Convolutional Neural Network Algorithm Arifin, Nurhikma; Rachmini, Siti Aulia; Rusman, Juprianus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.30953

Abstract

The productivity of mustard greens is vulnerable to pests and diseases that can threaten the yield and quality of the harvest. This study aims to detect pests on green mustard plants using the Convolutional Neural Network (CNN) method. The dataset used in this research consists of 450 images, with 225 images of pest-infested mustard greens and 225 images of healthy mustard greens. These 450 datasets are divided into 400 training data and 50 testing data. The testing was conducted fifteen times using CNN architectures with 2, 3 and 4 convolutional layers, having filter numbers of (64,32) (64, 32, 16) and (64, 32, 16, 8) respectively, and learning rates ranging from 0.1 to 0.00001 with the Adam optimizer. Based on the testing results of the learning rate and the number of layers, it was found that a learning rate of 0.001 provided the best performance with the highest accuracy and the lowest loss, especially in the model with 3 layers (64, 32, 16), which achieved an accuracy of 94% and a loss of 24.92%. A learning rate that is too high (0.1) or too low (0.00001) results in poor performance and instability, with low accuracy and high loss. Therefore, selecting the appropriate learning rate is crucial to achieving optimal results in model training.
LEAF DISEASE DETECTION IN TOMATO PLANTS USING XCEPTION MODEL IN CONVOLUTIONAL NEURAL NETWORK METHOD Arifin, Nurhikma; Maratuttahirah; Juprianus Rusman; Muhammad Furqan Rasyid
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1926

Abstract

This study aims to detect leaf diseases in tomato plants by applying the Xception model in the Convolutional Neural Network (CNN) method. The study categorizes tomato conditions into three main categories: Early Blight, Late Blight, and Healthy. Early Blight is generally infected by specific pathogens that cause spots and damage in the early stages of plant growth, while Late Blight is infected by pathogens in the later stages of the growing season. Meanwhile, the healthy category indicates normal conditions without disease symptoms. The dataset used consists of 300 tomato images, with each category having 100 images. In the model training phase using the fit method in TensorFlow, 17 epochs were performed to teach the model to recognize patterns in tomato leaf disease images in the training dataset. The model testing results on 30 tomato leaf images showed an accuracy rate of 85.84%. This result indicates a positive indication that the developed CNN model performs well in detecting and classifying tomato leaf conditions. Thus, this research can contribute to improving the understanding and management of leaf diseases in tomato plants to support more productive and sustainable agriculture.
Co-Authors Adha, Muhammad Sofwan Alexander Te’dangan Amarya Sula Amelia, Nerlys Arifin, Nurhikma Damayanti, Irene Devi Eko Suripto Pasinggi Ferayanti Boas Gallaran Garonga, Melki Gidion A.N. Pongdatu Gidion Aryo Nugraha Pongdatu Haryati, Berlian Zetikarya Insani, Chairi Nur Irene Devi Damayanti Maratuttahirah Matarru, Sandri Merliana Michael, Aryo Milasari Milasari Muhammad Furqan Rasyid Nerlys Amelia Nofrianto Pasae Padang, Semuel Yacobus Pallu, Seltiani parea russan rangan Patandung, Suryaningsih Pongdatu, Gideon A.N. Rachmini, Siti Aulia Reni Lolotandung Romiger Rongko, Yunita Toding Samrius Upa’ Sandri Matarru Srivan Palelleng Trivena, Trivena Upa, Samrius Utama, Muhammad Surya Alif