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All Journal Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Artificial Intelligence and Data Mining Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Eksplora Informatika Martabe : Jurnal Pengabdian Kepada Masyarakat Informatik : Jurnal Ilmu Komputer To Maega: Jurnal Pengabdian Masyarakat Jurnal Penelitian Enjiniring Fakultas Teknik Unhas SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Journal of Computer and Information System (J-CIS) Jurnal Minfo Polgan (JMP) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Sintaks Logika (JSilog) Ceddi Journal of Education Jurnal Pengabdian Pada Masyarakat Inspiration: Jurnal Teknologi Informasi dan Komunikasi Nobel Community Services Journal
Arifin, Nurhikma
Universitas Sulawesi Barat
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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.
IoT-Enabled Real-Time Monitoring and Tsukamoto Fuzzy Classification of Mandar River Water Quality via Web Integration for Sustainable Resource Management Insani, Chairi Nur; Arifin, Nurhikma
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5249

Abstract

This study presents the design and implementation of a real-time water quality monitoring system that utilizes pH, Total Dissolved Solids (TDS), and turbidity sensors, integrated with an ESP32 microcontroller. Sensor data are processed using the Tsukamoto fuzzy logic method to classify river water suitability into two categories: Suitable and Not Suitable. This approach effectively addresses imprecise and uncertain data, thereby producing more reliable classifications compared to conventional threshold-based methods. System validation was conducted through field testing over seven consecutive days at four different times of the day (morning, midday, afternoon, and evening), with results demonstrating stable performance. Recorded pH values ranged from 7.02 to 9.96, TDS values from 140 to 176 ppm, and turbidity levels between 4.00 and 5.15 NTU, indicating that the Mandar River remains within safe limits for daily use. The novelty of this study lies in the direct implementation of the Tsukamoto fuzzy logic method on a resource-constrained IoT device (ESP32), enabling edge-level classification with low latency and without full reliance on cloud computing. The system is designed to maintain decision reliability even under fluctuating sensor data, thus offering a practical and integrated solution for real-time monitoring. The main contribution of this work to computer science is the demonstration of lightweight embedded intelligent algorithms capable of running on constrained devices, the reinforcement of Explainable AI through transparent linguistic rules, and the integration of IoT with edge computing to support sustainable resource management in real-time.
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.
HORTICULTURE SMART FARMING FOR ENHANCED EFFICIENCY IN INDUSTRY 4.0 PERFORMANCE Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2728

Abstract

Chili peppers and papayas are important horticultural commodities in Indonesia with high economic value. To enhance productivity and efficiency in cultivating these crops, the application of Smart Farming technology is crucial. This study evaluates the use of image processing and artificial intelligence in the pre-harvest and post-harvest processes for chili peppers and papayas. For the pre-harvest process, data from 50 images of ripe chili peppers on the plant were used. The counting of ripe chilies was performed using HSV color segmentation with two masking processes, resulting in an average accuracy of 82.58%. In the post-harvest phase, 30 images of papayas, consisting of 10 images for each ripeness category—unripe, half-ripe, and ripe—were used. Papaya ripeness classification was carried out using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel and parameters C = 10 and γ = 10-3, achieving perfect classification accuracy of 100% for all categories. This study underscores the significant potential of Industry 4.0 technologies in enhancing agricultural practices and efficiency in the horticultural sector, providing important contributions to optimizing chili pepper and papaya production.
Comparison of SVM and Gradient Boosting with PCA for Website Phising Detection Syam, Nur Aini; Arifin, Nurhikma; Firgiawan, Wawan; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4344

Abstract

The increasing use of the internet has led to a rise in phishing attacks, posing a threat to user data security. This study compares the performance of the Support Vector Machine (SVM) and Gradient Boosting algorithms, integrated with Principal Component Analysis (PCA) for dimensionality reduction, in classifying phishing websites. The dataset consists of 11,054 samples classified into two categories: phishing (1) and non-phishing (-1), with three data partition scenarios for training and testing: 70:30, 80:20, and 90:10. Experimental results indicate that SVM outperforms Gradient Boosting in terms of accuracy and recall, particularly in detecting phishing websites. In the 80:20 and 70:30 data partition scenarios, the SVM model achieved an accuracy of 96% to 97% and had a higher recall for phishing websites, making it more sensitive to phishing detection. However, Gradient Boosting demonstrated consistent performance with an accuracy of around 94%, providing a balanced result between precision and recall for both classes. Therefore, the SVM model is superior for phishing detection tasks requiring high sensitivity to phishing websites, while Gradient Boosting remains a viable alternative when a more balanced performance between phishing and non-phishing sites is needed. The study concludes that both algorithms can be effectively used for phishing detection, with potential improvements through further experiments and hyperparameter tuning.
Corn Leaf Diseases Classification Using CNN with GLCM, HSV, and L*a*b* Features Johari, Putri Fausyah; Arifin, Nurhikma; Muzaki, Muzaki; Utama, Muhammad Surya Alif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4345

Abstract

Corn leaf diseases can damage plants and reduce crop yields, thus affecting the quality and quantity of corn production. This study aims to classify corn leaf diseases using the Convolutional Neural Network (CNN) method with different color features, namely Gray Level Co-Occurrence Matrix (GLCM), HSV, and L*a*b*. The dataset consists of 1,739 corn leaf images, which are divided into four disease classes: Blight, Common Rust, Gray Spot, and Healthy. The data is split into training and testing sets using an 80:20 ratio. Two testing scenarios were conducted: individual feature evaluation and feature combination. The results show that in the first scenario, the L*a*b* feature provides the best accuracy at 91.75%, followed by the HSV feature with an accuracy of 90.29%, and GLCM with an accuracy of 78.40%. In the second scenario, the combination of HSV and L*a*b* features results in the highest accuracy of 92.48%, indicating that combining color and brightness information can improve the model's performance. The combination of GLCM and L*a*b* features results in an accuracy of 91.75%, while the combination of GLCM and HSV results in an accuracy of 90.29%. These findings demonstrate that integrating HSV and L*a*b features enhances CNN performance in corn leaf disease classification, outperforming individual feature- based approaches, thus contributing to more effective AI-based agricultural disease diagnosis.
Comparative Analysis of CNN, SVM, Decision Tree, Random Forest, and KNN for Maize Leaf Disease Detection Using Color and Texture Feature Extraction Arifin, Nurhikma; Insani, Chairi Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5128

Abstract

Corn (Zea mays L.) is an important agricultural commodity in Indonesia, serving as the second staple food after rice and playing a crucial role in supporting national food security. However, corn production is frequently threatened by sudden outbreaks of pests and diseases, making accurate early detection essential to maintaining yield stability. This study aims to detect maize leaf diseases using five classification algorithms: Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and Convolutional Neural Network (CNN). These algorithms were tested using a combination of texture and color features, including Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), Hue-Saturation-Value (HSV), and L*a*b*. The dataset used consists of 2,048 maize leaf images classified into four categories: Blight, Common Rust, Gray Leaf Spot, and Healthy, with 512 images per class. Each class was divided into training and testing sets to train and evaluate the classification models. The results show that CNN achieved the highest accuracy of 93.93% when using a complete combination of color and texture features. Meanwhile, SVM also demonstrated high performance, achieving the same accuracy (93.93%) using only the combination of color features (HSV and Lab*). Random Forest and Decision Tree performed best when using color features alone, with accuracies of 89.81% and 87.14%, respectively. These findings indicate that color features have a dominant influence on classification accuracy, and that combining color and texture features can significantly enhance model performance, particularly in CNN architectures. This study contributes to the development of early disease detection systems in precision agriculture.
Co-Authors Al Farisiy, Salman Amalia Chairy Amirullah, Indrabayu Anaguna, Nursyam Andi M Yusuf Areni, Intan Sari Astinawaty, Astinawaty Chairi Nur Insani Dian Megah Sari Dian Megah Sari Dian Megah Sari Farid Wajidi Fauziah Fauziah Firgiawan, Wawan Furqan Rasyid, Muhammad Heliawaty Hamrul Heri, Adi Iin Indriani Indra Indra Indra Indrabayu Indrabayu Insani, Chairi Nur Intan Sari Areni Ismail Johari, Putri Fausyah Juprianus Rusman Maratuttahirah Maulidia, Ulfa Megah Sari, Dian Milasari Milasari Muh Rafli Rasyid Muhammad Furqan Rasyid Muhammad Furqan Rasyid Musyrifah Muzaki Muzaki Nilam Cahya Nur Insani, Chairi Nurdina Rasjid Nurfitrianingsih Nurfitrianingsih Nurmadinah, Nurmadinah Prihastinur Rachmini, Siti Aulia Ramadani, Suci Awalia Rasyid, Muh. Rafli Sari, Dian Megah Siti Aulia Rachmini Sulfayanti Sulpiana Syam, Nur Aini Upa, Samrius Utama, Muhammad Surya Alif virna, virna afrilianty Wawan Firgiawan Yusuf, Andi M