Jurnal Teknik Informatika (Jutif)

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

Tue, 10 Jun 2025 00:00:00 +0000

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.

Prediction Of Clay Mining Production Value Using Linear Regression Model With Multi-Swarm Particle Swarm Optimization

Gusti Eka Yuliastuti, Muchamad Kurniawan, Dimas Pratikto, Mochamad Rizky Moneter — Tue, 10 Jun 2025 00:00:00 +0000

The progress of a nation or a country can be recognized from its income through various industries inside. Mining refers to one of the most advanced industries in Indonesia. The majority of mining in Indonesia is open-pit mining which is exposed directly to the sky. This study focuses on modeling data from rainfall, working hours, and production yields. It employed the Multi-Swarm Particle Swarm Optimization (MSPSO) algorithm to find multiple linear regression modeling by minimizing the Mean Squared Error (MSE) value. The value for the production results was then predicted using the existing multiple linear regression model. In terms of testing, the best model having an MSE of 288.0656 occurred at the parameters of Npop 180, acceleration coefficient 1 by 0.7, acceleration coefficient 2 by 0.7, acceleration coefficient 3 by 0.7, wmin 4, wmax 9 within 100 iterations.

Comparative Analysis Retrofit and Ktor Client Performance in Various Internet Speeds Internet on MSMEs Cashier Application

Muhamad Akbar Abdul Kholik, Dinar Nugroho Pratomo — Tue, 10 Jun 2025 00:00:00 +0000

MSMEs (Micro, Small, and Medium Enterprises) in Indonesia face uneven network infrastructure, with more than 20% of smartphone users having download speeds below 10 Mbps. This condition hampers the efficiency of data processing between client and server, while MSMEs need innovations such as digitization of bookkeeping to increase competitiveness. The selection of HTTP networking libraries such as Retrofit and Ktor Client is very important, because both play a role in the process of sending and receiving data from the server. This research aims to analyze the performance of both libraries in the Lulu POS application to determine the most optimal library in supporting MSME operations in various network conditions. The test is conducted in two scenarios: the first scenario uses text data and the second scenario uses text and image data. Each scenario has several test cases that will be tested at six different internet speeds. The results show that Retrofit excels in response time for text data with a performance improvement of 18.85% and network usage of 21.33%. Ktor Client is superior in scenarios involving text and image data, with a response time advantage of 7.20% and network usage of 0.08%. On the other hand, Retrofit is more efficient in memory usage in both scenarios, with an advantage of 16.49% in text data and 4.70% in text and image data. In conclusion, Retrofit is more stable for applications focusing on text data such as Lulu POS, while Ktor Client is more suitable for applications that manage images. These results make MSMEs get cashier applications with optimal libraries for various network conditions, so that operations are smoother and data management efficiency increases.

Optimising Bitcoin Price Forecasting Using Lstm, Gru, Prophet, Var, And Es Multi-Model Approaches

Tue, 10 Jun 2025 00:00:00 +0000

This study aims to optimize Bitcoin price forecasting by integrating several multi-model approaches, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Prophet, as well as risk analysis using Value at Risk (VaR) and Expected Shortfall (ES). The daily Bitcoin price data from the period of July 17, 2010, to June 28, 2024, obtained from Kaggle, were analyzed using accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), as they provide a more objective and reliable evaluation of prediction effectiveness. The results show that the LSTM model performed the best, with an MSE of 535,419.12, RMSE of 731.72, MAE of 310.72, and MAPE of 159.01. The GRU model produced similar evaluation values with an MSE of 558,868.06 and RMSE of 747.57. In contrast, Prophet demonstrated lower performance, with an MSE of 59,309,927.76 and RMSE of 7,701.29. The risk analysis indicated that at a 95% confidence level, VaR reached 61,676.43, while ES reached 61,737.58, reflecting additional risk in extreme conditions. This study provides valuable insights into the advantages of the LSTM and GRU models for Bitcoin price forecasting, while also emphasizing the importance of risk analysis in supporting cryptocurrency investment decisions.

Enhancing Cyberbullying Detection with a CNN-GRU Hybrid Model, Word2Vec, and Attention Mechanism

Kaysa Azzahra Adriana, Erwin Budi Setiawan — Tue, 10 Jun 2025 00:00:00 +0000

Cyberbullying is an act of violence commonly committed on online platforms such as social media X, often causing psychological effects for victims. Despite prevention efforts, traditional methods for detecting cyberbullying show limited effectiveness due to the complexity of language and diversity of expressions, leading to suboptimal performance. This study aims to enhance detection accuracy by applying Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) with an attention mechanism to analyze textual data from tweets. The model uses Term Frequency-Inverse Document Frequency (TF-IDF) for extracting important words and Word2Vec for expanding text representation. A total of 30,084 labeled datasets from tweets on social media X were utilized. Results indicate the hybrid CNN-GRU model with attention achieved the highest accuracy of 80.96%, outperforming stand-alone CNN and GRU models. Additionally, TF-IDF and Word2Vec significantly improved model performance, with the CNN-GRU combination proving most effective for detecting cyberbullying. This study contributes to computer science by proposing a novel approach that integrates CNN, GRU, and attention mechanisms with advanced feature extraction techniques, providing a more reliable detection system for online platforms. It also highlights the potential for integrating multimodal data to further enhance future performance.

Comparative Analysis of Augmentation and Filtering Methods in VGG19 and DenseNet121 for Breast Cancer Classification

I Kadek Seneng, Putu Desiana Wulaning Ayu, Roy Rudolf Huizen — Tue, 10 Jun 2025 00:00:00 +0000

Breast cancer is one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Mammography plays a crucial role in early detection, yet challenges in manual interpretation have led to the adoption of Convolutional Neural Networks (CNNs) to improve classification accuracy. This study evaluates the performance of Visual Geometry Group (VGG19) and Densely Connected Convolutional Networks (DenseNet121) in mammogram classification. It examines the impact of data augmentation and image enhancement techniques, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), Median Filtering, and Discrete Wavelet Transform (DWT), as well as the influence of varying epochs and learning rates. A novel approach is introduced by assessing data augmentation effectiveness and exploring model adaptations, such as layer incorporation and freezing during training. Classification performance is enhanced through fine-tuning strategies combined with image enhancement techniques, reducing reliance on data augmentation. These findings contribute to medical imaging and computer science by demonstrating how CNN modifications and enhancement methods improve mammogram classification, providing insights for developing robust deep learning-based diagnostic models. The highest performance was achieved using VGG19 with DWT, a learning rate of 0.0001, and 20 epochs, yielding 98.04% accuracy, 98.11% precision, 98% recall, and a 97.99% F1-score. Data augmentation did not consistently enhance results, particularly in clean datasets. Increasing epochs from 10 to 20 improved accuracy, but performance declined at 30 epochs. The confusion matrix showed high accuracy for Benign (100%) and Cancer (99.5%), with more misclassifications in the Normal class (94.5%).

An Efficient Model for Waste Image Classification Using EfficientNet-B0

Teofilus Kurniawan, Khadijah, Retno Kusumaningrum — Tue, 10 Jun 2025 00:00:00 +0000

Waste management remains a significant challenge, particularly in developing countries. To address this issue, artificial intelligence can be leveraged to develop a waste image classifier that facilitates automatic waste sorting. Previous studies have explored the use of Convolutional Neural Networks (CNNs) for waste image classification. However, CNNs typically require a large number of parameters, leading to increased computational time. For practical applications, a waste image classifier must not only achieve high accuracy but also operate efficiently. Therefore, this study aims to develop an accurate and computationally efficient waste image classification model using EfficientNet-B0. EfficientNet-B0 is a CNN architecture designed to achieve high accuracy while maintaining an efficient number of parameters. This study utilized the publicly available TrashNet dataset and investigated the impact of image augmentation in addressing imbalance data issues. The highest performance was achieved by the model trained on the unbalanced dataset with the addition of a Dense(32) layer, a dropout rate of 0.3, and a learning rate of 1e-4. This configuration achieved an accuracy of 0.885 and an F1-score of 0.87. These results indicate that the inclusion of a Dense(32) layer prior to the output layer consistently improves model performance, whereas image augmentation does not yield a significant enhancement. Furthermore, our proposed model achieved the highest accuracy while maintaining a significantly lower number of parameters compared to other CNN architectures with comparable accuracy, such as ResNet-50 and Xception. The resulting waste classification model can then be further implemented to build an automatic waste sorter.

Machine Learning Models for Metabolic Syndrome Identification with Explainable AI

Egga Asoka, Egga Asoka, Fathoni, Anggina Primanita, Indra Griha Tofik Isa — Tue, 10 Jun 2025 00:00:00 +0000

Metabolic syndrome (MetS) is a cluster of interrelated risk factors, including hypertension, dyslipidemia, central obesity, and insulin resistance, significantly increasing the likelihood of cardiovascular diseases and type 2 diabetes. Early identification of hypertension, a key component of MetS, is essential for timely intervention and effective disease management. This research aims to develop a hybrid machine learning model that integrates XGBoost classification with K-Means clustering to enhance or strengthening of hypertension prediction and identify distinct patient subgroups based on metabolic risk factors. The dataset consists of 1,878 patient records with metabolic parameters such as systolic and diastolic blood pressure, fasting glucose, cholesterol levels, and anthropometric measurements. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed XGBoost model achieved an outstanding classification performance with 98% accuracy, 98% precision, 98% recall, 98% F1-score, and an ROC-AUC of 1.00. K-Means clustering further identified five distinct patient subgroups with varying metabolic risk profiles. The findings underscore the potential of machine learning-driven decision support systems in improving hypertension diagnosis and MetS management.

Optimizing Indonesian Banking Stock Predictions with DBSCAN and LSTM

Tue, 10 Jun 2025 00:00:00 +0000

Investing in the stock market is challenged by high volatility, which often leads to inaccurate price predictions. Prediction models often struggle to handle the fluctuation phenomenon and produce unstable forecasts. This study aims to predict stock prices in three banks, namely PT Bank Central Asia Tbk (BBCA), PT Bank Rakyat Indonesia (Persero) Tbk (BBRI), and PT Bank Mandiri (Persero) Tbk (BMRI) using Long Short-Term Memory (LSTM) with the integration of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for anomaly detection. DBSCAN is applied with an epsilon (ε) of 0.5 and a minimum of 5 samples using Euclidean distance. The LSTM model consists of two hidden layers with 50 units, optimized using Adam, and applying the Mean Squared Error (MSE) loss function. The results show that DBSCAN improves prediction accuracy under several conditions. For BBCA stock, the lowest MSE was 0.003 at the 2nd fold with DBSCAN compared to 0.006 without DBSCAN. For BMRI stock achieved an MSE of 0.003 at the 4th fold with DBSCAN, while the 5th fold without DBSCAN obtained 0.000. For BBRI stock showed the best MSE of 0.003 at the 2nd fold with DBSCAN and the 5th fold without DBSCAN. These results show that the integration of DBSCAN can improve prediction especially when extreme price fluctuations occur. This research contributes to the development of stock price prediction methods that can be one of the benchmarks for investors before making decisions so that they do not experience losses.

Deep Reinforcement Learning for Autonomous System Optimization in Indonesia: A Systematic Literature Review

Dedi Yusuf, Eko Supraptono, Agus Suryanto — Tue, 10 Jun 2025 00:00:00 +0000

Background: The development of artificial intelligence (AI) technology, including Deep Reinforcement Learning (DRL), has brought significant changes in various industrial sectors, especially in autonomous systems. DRL combines the capabilities of Deep Learning (DL) in processing complex data with those of Reinforcement Learning (RL) in making adaptive decisions through interaction with the environment. However, the application of DRL in autonomous systems still faces several challenges, such as training stability, model generalization, and high data and computing resource requirements. Methods: This study uses the Systematic Literature Review (SLR) method to identify, evaluate, and analyze the latest developments in DRL for autonomous system optimization. The SLR was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, which consists of four main stages: identification, screening, eligibility, and inclusion of research articles. Data were collected through literature searches in leading scientific journal databases such as IEEE Xplore, MDPI, ACM Digital Library, ScienceDirect (Elsevier), SpringerLink, arXiv, Scopus, and Web of Science. Results: This study found that DRL has been widely adopted in various industrial sectors, including transportation, industrial robotics, and traffic management. The integration of DRL with other technologies such as Computer Vision, IoT, and Edge Computing further enhances its capability to handle uncertain and dynamic environments. Therefore, this study is crucial in providing a comprehensive understanding of the potential, challenges, and future directions of DRL development in autonomous systems, in order to foster more adaptive, efficient, and reliable technological innovations.

Digital Forensic Chatbot Using DeepSeek LLM and NER for Automated Electronic Evidence Investigation

Nuurun Najmi Qonita, Maya Rini Handayani, Khothibul Umam — Tue, 10 Jun 2025 00:00:00 +0000

The growing complexity of cybercrime necessitates efficient and accurate digital forensic tools for analyzing electronic evidence. This research presents an intelligent digital forensic chatbot powered by DeepSeek Large Language Model (LLM) and Named Entity Recognition (NER), designed to automate the analysis of various digital evidence, including system logs, emails, and image metadata. The chatbot is deployed on the Telegram platform, providing real-time interaction with investigators. The metric results show that the chatbot achieves a precision of 83.52%, a recall of 88.03%, and an F1-score of 85.71%. These results demonstrate the chatbot's effectiveness in accurately detecting forensic entities, significantly improving investigation efficiency. This study contributes to digital forensics by integrating LLM and NER for enhanced evidence analysis, offering a scalable and adaptive solution for automated cybercrime investigations. Future research may explore integrating anomaly detection and blockchain-based evidence integrity.

Hybrid Neural Network-Based Road Damage Detection Using CNN-RNN and CNN-MLP Models

Ani Dijah Rahajoe, Muhammad Suriansyah, Angelo A. Beltran Jr — Tue, 10 Jun 2025 00:00:00 +0000

Currently, there are many applications of image processing in various fields. One of them is the recognition of paved road images. Detection through images helps in handling infrastructure development roads. With the advancement of technology, especially in the field of deep learning, the process of detecting road damage can be done automatically and more efficiently. The road damage detection system can be integrated into the smart city system to monitor infrastructure conditions in real time. This study will use a combined deep learning algorithm between Convolutional Neural Network- Recurrent Neural Network (CNN-RNN) and as a comparison using Convolutional Neural Network- MultiLayer Perceptrons (CNN-MLP). The study aims to analyze the accuracy of using the CNN-RNN and CNN-MLP algorithms for detecting paved roads that have categories of undamaged roads, damaged roads, and damaged roads with holes. The detection of paved roads has complex details so an algorithm that has good performance with high accuracy is needed. The results of the study showed that the CNN-RNN hybrid had a better accuracy of 96.59 percent than the CNN-MLP hybrid model of 95.9 percent.

Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers

Dillyana Tugas Setiyawan, Berlilana, Azhari Shouni Barkah — Tue, 10 Jun 2025 00:00:00 +0000

The variation in disease distribution patterns across community health centers in Banjarnegara Regency necessitates a precise segmentation analysis to support effective allocation of healthcare resources. This study aims to compare the effectiveness of three clustering methods DBSCAN, OPTICS, and Agglomerative Clustering in grouping Puskesmas based on the type and number of diseases they manage. The evaluation methods used include the Silhouette Score and the Davies-Bouldin Index, which assess the quality of the clustering results. The analysis indicates that Agglomerative Clustering produces the most stable cluster structures, reflected in its highest Silhouette Score, compared to DBSCAN and OPTICS, which tend to yield more noise and less optimal clustering quality. These findings suggest that hierarchical clustering approaches are more effective in the context of healthcare service distribution data at the primary care level. The results of this study are expected to serve as a foundation for the formulation of data-driven and region-based health policies, particularly in designing more targeted interventions and optimizing the distribution of healthcare services.

Comparative Analysis of Hybrid Intelligent Algorithms for Microsleep Detection and Prevention

Arvina Rizqi Nurul'aini, Rizky Ajie Aprilianto, Feddy Setio Pribadi — Tue, 10 Jun 2025 00:00:00 +0000

Microsleep is a critical factor contributing to traffic accidents, posing significant risks to road safety. Research by the AAA Foundation for Traffic Safety found that 328,000 sleep-related driving accidents happen annually in the United States, underscoring the widespread and dangerous nature of drowsy driving. These incidents often occur without warning, making them especially hazardous and difficult to prevent through conventional means alone. This research aims to improve the accuracy of microsleep detection by developing a hybrid intelligent algorithms. It compares three intelligent algorithms: Fuzzy Logic (FL), representing scheme A; Fuzzy Logic combined with Artificial Neural Networks (FL-ANN), representing scheme B; and a combination of Fuzzy Logic, ANN, and Decision Trees (FL-ANN-DT), representing scheme C. These methods were evaluated using performance metrics such as MSE, MAE, RMSE, R², and response time. The results indicate that Scheme C (FL-ANN-DT) significantly outperforms the other approaches, achieving an MSE of 5.3617e-32, MAE of 4.3823e-17, R² of 1.0, and an RMSE close to zero, demonstrating near-perfect accuracy. Compared to previous models, this hybrid approach enhances prediction precision while maintaining real-time feasibility. The findings highlight the potential of FL-ANN-DT as an advanced microsleep detection system, contributing to improved road safety and real-time monitoring applications. This system can serve as a proactive safety layer in driver assistance technologies, reducing the risk of fatigue-related accidents and potentially saving lives.