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International Journal Of Computer, Network Security and Information System (IJCONSIST)
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 26863480     DOI : https://doi.org/10.33005/ijconsist.v3i1
Core Subject : Science,
Computer Science & IT
Focus and Scope The Journal covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 7 Documents
 1 
Search results for , issue "Vol 6 No 2 (2025): March" : 7 Documents clear
Application of Gray Level Co-Occurrence Matrix (GLCM) for Abdominal Wave Image Classification: A Comparative Study of LVQ, KNN, and SVM Putri Taqwa Prasetyaningrum; Ibnu Rivansyah Subagyo
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.126

Abstract

Medical image classification is a crucial research area in medical imaging analysis to support clinical diagnosis. In this study, we implemented the Gray Level Co-Occurrence Matrix (GLCM) method to extract texture features from abdominal wave images and enhance classification accuracy. Three machine learning classification methods—Learning Vector Quantization (LVQ), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—were employed and compared based on their classification performance. The experimental results show that the KNN method achieved the highest accuracy of 96.83%, followed by SVM with 95.24%, and LVQ with 84.13%. These findings indicate that KNN is the most effective classification method for abdominal wave images among those tested. This study highlights the significance of texture feature extraction using GLCM in improving medical image classification accuracy. The results of this study can contribute to the advancement of digital healthcare technologies, particularly in gastrointestinal disorder detection and digestive health monitoring. Future research should explore hybrid deep learning approaches and larger datasets to further enhance classification accuracy and model robustness.
Smart Shipping Route Optimization for Fuel Efficiency Using Big Data Analytics Ariyono Setiawan; Widyansih, Upik; Bin Abdul Hadi , Abdul Razak
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.129

Abstract

This research aims to optimize shipping routes by applying big data analytics to improve fuel efficiency. By leveraging real-time and historical data, the study identified the most efficient routes to minimize fuel consumption without sacrificing operational effectiveness. Based on maritime logistics theory, big data analytics, and fuel efficiency, this research combines route optimization models, weather forecasts, and ship performance analysis to support navigation decision-making. In addition, the impact of IMO MARPOL Annex VI regulations, especially EEDI and SEEMP, is also considered in efforts to optimize energy efficiency. The method used is a mixed approach, which combines quantitative analysis of AIS data, weather reports, and fuel consumption records with machine learning algorithms for route optimization. Pearson's correlation analysis evaluates the relationship between speed, distance, travel time, and fuel consumption. Case studies are used to validate the developed model. The results showed that fuel consumption was greatly affected by the speed of the ship, with higher speeds increasing fuel consumption. A negative correlation was found between travel time and daily fuel consumption, suggesting that slower cruising can improve efficiency. The study emphasizes the importance of real-time data processing in route adjustments based on weather, congestion, and energy efficiency. This research offers an innovative, data-driven approach to route planning, different from traditional methods that rely on static charts and experience. The integration of big data in maritime logistics can reduce emissions, reduce costs, and improve operational sustainability.
Performance of Contrast Adjustment Techniques on The Face Recognition Method with Test Data Under Varying Lighting Conditions Nugroho, Budi; Maulana, Hendra; Yuniarti, Anny
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.130

Abstract

In the face recognition process influenced by lighting, the application of the image enhancement process at the preprocessing stage plays an important role in normalizing image contrast so that the quality of the input image becomes better. This step is expected to improve face recognition performance. In this study, we implement a lighting-influenced face recognition method, namely Robust Regression, and test several image enhancement techniques in the preprocessing phase to determine their effects on face recognition performance under different image lighting conditions, including Contrast-limited Adaptive Histogram Equalization (CLAHE), Histogram Equalization (Histeq), and Image Intensity Adjustment (Imadjust). HE uses a global technique that adjusts the overall intensity of the image. CLAHE uses a local technique that adjusts the intensity of pixels based on their surrounding areas. Meanwhile, the Imadjust function adjusts the intensity of image pixels based on the specified minimum and maximum values. The experiment is conducted using the AR Face Database which contains images affected by lighting factors. Lighting conditions include several categories, namely low, medium, high, and very high (extreme) lighting conditions. The experimental scenario is carried out by comparing the results of face recognition using several preprocessing techniques on each test data. The experimental results show that image enhancement techniques improve the performance of face recognition. The face recognition approach that adds the CLAHE technique to the preprocessing shows the highest performance of 95.87%. Meanwhile, the face recognition approach that adds the Imadjust technique to the preprocessing shows the lowest performance of 84.38%.
Comparing Structured Prompts for Denoising Noisy Certificate Text Dimas Saputra; I Gede Susrama Mas Diyasa; Eva Yulia Puspaningrum; Wan Suryani Wan Awang
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.133

Abstract

This study addresses the challenge of noisy text resulting from Optical Character Recognition (OCR) on certificates, which hinders effective classification in Recognition of Prior Learning (RPL) contexts. To mitigate this issue, researchers propose the use of prompt-based denoising leveraging a Large Language Model (LLM), specifically the Gemini model, to refine the extracted text prior to classification. The methodology integrates OCR via PyTesseract, LLM-driven denoising using structured prompts (CSIR, CLEAR, and CO-STAR), and a BERT-base-uncased model for classification. Synonym replacement is also applied for data augmentation. Performance evaluation is conducted using accuracy, validation accuracy, confusion matrix, and classification reports. The results demonstrate a substantial improvement in classification performance. The baseline scenario achieved an accuracy of 82.14%, whereas the best-performing prompt structure, CO-STAR, reached 98.81%, marking an increase of over 15 percentage points. Similar trends were observed across all evaluation metrics, with CO-STAR delivering the highest precision, recall, and F1-score values. In conclusion, incorporating LLM-driven denoising through effective prompt strategies enhances the quality of OCR-extracted text and significantly boosts classification outcomes in certificate-based applications.
The Trade-off between Energy-Accuracy in the IoT-based Activity Monitoring System Sri Indrawanti, Annisaa; Mandyartha, Eka Prakarsa
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.131

Abstract

Activity monitoring system is used in many fields such patient’s activity monitoring system for self-quarantine in their home. The IoT- based activity monitoring system uses the limited resources (e.g., bandwidth, battery and memory) for monitoring the user’s activity. The limited resources (such as battery) provide the limited lifetime battery in activity monitoring system. By resource efficiency, it will extend the battery lifetime. Resource efficiency is achieved by adaptively reporting user activity depending on the level of the user’s activity emergency. But, when the user’s activity reporting data is based on the emergency level, then it reduces the data detail and its activity recognition accuracy. So, we develop energy-savings techniques for user’s activity reporting and analyze the effect of energy-savings techniques to the accuracy of activity recognition using different methods. The results show the energy-savings techniques can save battery life up to 8%, bandwidth up to 146,5 bytes/sec and memory up to 2,8% compared to non-energy saving technique. But the energy-saving techniques give less accuracy in the four different activity recognition methods up to 11% in average.
Internet Of Things Based Car Parking Monitoring Device Integrated with WEB Server Alvin Indra Pratama; Tunggadewi, Elsyea Adia; Winarno
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.119

Abstract

The rapid increase in the number of vehicles has intensified the need for an efficient and well-managed parking system. Conventional parking systems, which rely heavily on human attendants, often suffer from the absence of real-time monitoring, resulting in congestion and operational inefficiencies. This research proposes an Internet of Things (IoT)-based Smart Parking System employing the HC-SR04 ultrasonic sensor to detect vehicle presence in each parking slot. The system is integrated with a web server that enables real-time monitoring and provides recommendations for parking slot based on proximity to entrance. The implementation utilizes ESP32 microcontrollers to process sensor data and communicate with the web server. All sensor readings are stored in a MySQL database and accessed through a PHP-based web application. Experimental evaluation demonstrates a 100% accuracy rate in slot detection, response time, and recommendation performance. The proposed system significantly enhances parking management efficiency and minimizes fuel consumption by reducing the time required to locate available parking spaces.
Implementation of the Naive Bayes Method for Stunting Classification in Children Under Five Years Old (Balita). Sulthan Ahmad, Ferdiansyah; Muhammad Farhan , Maulana; Akmal Aliffandhi , Anwar; Muhammad Rifki Bahrul , Ulum; I Gede Susrama , Diyasa; Vinza Hedi, Satria
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.157

Abstract

The issue of stunting in Indonesia has become a serious concern, drawing significant attention from the government. To address this problem, the government has set a target to reduce the stunting prevalence rate to 14% by 2024. As an initial step in supporting this goal, the present study aims to classify the nutritional status of children under five years old using the Naive Bayes method. The objective of this research is to evaluate the performance of the Naive Bayes algorithm in classifying the nutritional status of children under five, with a focus on body weight, height, and exclusive breastfeeding intake as predictors of stunting. The research process includes several stages, namely problem formulation, data collection, data preprocessing, data splitting, model construction, model training, model evaluation, and result analysis. The findings of this study indicate that the Naive Bayes method achieved an accuracy of 70% in classifying stunting among children under the age of five, with an F1-score evaluation of 70%.

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