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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 77 Documents
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Prediction of Birth Rates Using the Naive Bayes Algorithm in the North Sumatra Region Yudha Kartika, Dhian Satria; putra, brillyan; Wibawa Syahalam, Aji Qolbu
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.109

Abstract

The birth rate constitutes a significant metric within the field of demographics. It serves as a crucial element influencing the strategic planning and development of a region, particularly in provinces characterized by notably large populations, such as North Sumatra. The process of forecasting an optimal birth rate necessitates the involvement of multiple agencies and services to effectively devise policies pertaining to health, education, and enhanced infrastructure for the future. This study employs modeling techniques utilizing the Naive Bayes algorithm. This particular algorithm represents a probabilistic classification method within the realm of data mining, aimed at predicting birth rates across all districts and municipalities in North Sumatra, leveraging demographic and socio-economic datasets commencing from the year 2022. The dataset encompasses variables such as population statistics, demographics of women of reproductive age, levels of educational attainment, accessibility to health services, and incidences of poverty, all of which were sourced from the Central Statistics Agency (BPS) over a five-year timeframe. The research methodology is executed through several phases, including data preprocessing, feature selection, partitioning of training and test datasets, and a validation testing process to affirm the reliability of the proposed model. The dataset is partitioned into training and test components utilizing a distribution ratio of 70:30. The outcomes of the proposed model's testing are computed, employing a confusion matrix to derive metrics such as accuracy, precision, recall, and F1 scores. The results yield an accuracy value of 85%, a precision of 87%, and an F1 score of 86%. These findings indicate a favorable outcome in regional mapping and reflect an appropriate birth rate. Subsequently, the results are visualized within a geographic information system (GIS) to elucidate the spatial patterns of the predicted birth rate, thereby facilitating local government interventions in specific areas.
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.
Geospatial Data Visualization with Spatio Temporal Analysis method: The Effect of Hotel Accommodation Distribution on Poverty Level in East Java Province Nisrina, Nasywa Agra; Wati, Seftin Fitri Ana; Rahma, Faradhiya Aulia; Pratama, Arista; Erifiandi, Edwin
IJCONSIST JOURNALS Vol 6 No 1 (2024): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i1.111

Abstract

Tourism accommodation distribution influences regional poverty rates, making accurate data essential for analysis. This study aims to create a data visualization on poverty rates and tourism accommodation, especially hotels, in East Java Province to examine the relationship between the two through geospatial data visualization. By using the spatio-temporal analysis method starting from data preprocessing, data integration, data identification, and data visualization, this process allows comprehensive mapping and analysis of the available data. The resulting data visualization will be very useful for stakeholders in identifying and understanding the relationship between the availability of tourism facilities and economic welfare. Through this visual representation, stakeholders can clearly see how the distribution of tourism accommodations, such as hotels, can affect poverty levels in different regions. The statistical analysis using an F-test regression model confirms a significant relationship, with an F-value of 7.19 and a p-value of 0.003, indicating that an increase in hotel accommodations is associated with a decrease in poverty levels. This is due to the potential opening of jobs in the tourism sector for local residents in each district or city area.
Implementation of Distributed Database in Waste Bank Application Using NOSQL Sugiarto; Setya Wijaya, Riko; Indah Harya, Gyska; Glory Mei Stephany, Sherafim
IJCONSIST JOURNALS Vol 5 No 2 (2024): 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.v5i2.117

Abstract

This paper presents the implementation of a NoSQL-based distributed database for a waste bank application aimed at improving the efficiency, scalability, and reliability of waste management systems. Traditional relational databases often struggle with handling large volumes of transactional data in real-time, particularly in expanding waste bank networks. The proposed solution utilizes MongoDB to address these challenges by distributing data across multiple servers, ensuring enhanced performance and scalability. The study focuses on managing user information, waste deposit transactions, and real-time updates. Testing was conducted using real-world data from Indonesia, involving 500 users and 15,000 transactions. The system demonstrated strong scalability, processing up to 5,000 transactions per minute with an average response time of 50ms. Additionally, real-time updates were delivered with a latency of 100ms, maintaining high user engagement. Results show that the NoSQL-based system offers significant improvements in handling large datasets, ensuring data consistency, and maintaining system uptime of 99.8%. This approach is well-suited for modern waste bank applications, particularly in diverse environments like Indonesia.
Enhanced Decision Making Using Multi Factor Evaluation Process for Innovative Product Selection Subagyo, Ibnu Rivansyah; Taqwa Prasetyaningrum, Putri
IJCONSIST JOURNALS Vol 5 No 2 (2024): 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.v5i2.118

Abstract

The process of selecting innovative products can be complex and challenging due to the multiple factors involved. This study explores an enhanced decision-making approach using the Multi-Factor Evaluation Process (MFEP) to assist in selecting the most suitable product among alternatives. The MFEP methodology evaluates products based on various criteria and assigns weightings to each factor according to its significance. In this research, three innovative products—SkyWater, HORNET, and BPP-4D—are evaluated. The evaluation considers critical performance indicators and calculates a final score for each product. The results indicate that SkyWater has the highest evaluation score, followed by HORNET and BPP-4D, providing insights into their relative suitability for recommendation. This paper demonstrates the effectiveness of the MFEP in facilitating objective decision-making in the selection of innovative products.
Prototype of Hydroponic Lettuce Cultivation: IoT-Based Automatic Irrigation System for Growth Enhancement and Sustainability Millati, Fina Amru Millati; Sari, Anggraini Puspita; Nadirco, Daniel Gloryo; Binti Hasim, Norhaslinda
IJCONSIST JOURNALS Vol 6 No 1 (2024): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i1.123

Abstract

Lettuce (Lactuca sativa) is a vegetable commonly grown in temperate and tropical climates. In organic cultivation, lettuce has high economic value due to its high mineral content, including iodine, copper, iron, phosphorus, and others. Hydroponics is an agricultural cultivation method that uses water as the planting medium. This method offers advantages such as space efficiency and more. Currently, monitoring water in hydroponic plant cultivation is conducted periodically by growers who physically visit the cultivation site, including the reservoir tank. This process can be quite inconvenient because growers cannot predict when moisture levels will drop. One solution to this problem is to implement remote, real-time water monitoring for hydroponic lettuce cultivation using Internet of Things (IoT) devices. Therefore, a system design for IoT-based remote monitoring of moisture levels is required, allowing growers to monitor the condition of hydroponic lettuce without needing to visit the cultivation site. The research findings indicate that when the moisture level is dry, the system will display a value of 100, prompting users to receive a notification for irrigation. If the value is below 100, the system indicates that the planting medium is sufficiently moist.
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.

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