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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
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Search results for , issue "Vol 6 No 1 (2024): September" : 7 Documents clear
Hybrid Machine Learning to Evaluate the Incidence of Toddler Stunting through Integration of Multi-source Satellite Imagery and Official Statistics in East Nusa Tenggara Province Suhendra Widi Prayoga; Setia Pramana
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.v6i1.112

Abstract

Stunting is a serious health problem that impacts the quality of life of children under five. In 2023, East Nusa Tenggara recorded the second highest prevalence of stunting in Indonesia, influenced by health, socio-economic and environmental factors. In terms of the environment, remote sensing technology can be utilised to monitor environmental factors that contribute to stunting, such as vegetation conditions, access to clean water, and soil conditions. This study aims to evaluate the incidence of stunting among children under five using a hybrid machine learning approach, combining predictive modeling and cluster analysis. The results indicate thStunting is a serious health problem that impacts the quality of life of children under five. In 2023, East Nusa Tenggara recorded the second highest prevalence of stunting in Indonesia, influenced by health, socio-economic and environmental factors. In terms of the environment, remote sensing technology can be utilised to monitor environmental factors that contribute to stunting, such as vegetation conditions, access to clean water, and soil conditions. This study aims to evaluate the incidence of stunting among children under five using a hybrid machine learning approach, combining predictive modeling and cluster analysis. The results indicate that eXtreme Gradient Boosting Regressor (XGBR) is the best model for estimating stunting prevalence, with a Root Mean Squared Error (RMSE) of 3.2076 and an value of 0.7223. Meanwhile, for clustering results, K-Means Clustering is identified as the most effective method for grouping districts/cities based on socioeconomic and environmental factors. The clustering process produced two groups, such as vulnerable (Cluster 1) and highly vulnerable (Cluster 2), with connectivity, Dunn Index, and silhouette coefficient values of 29.290, 0.6931, and 0.4509, respectively. These findings are expected to serve as a basis for policymakers in formulating targeted interventions to reduce stunting rates, particularly in highly vulnerable areas. at eXtreme Gradient Boosting Regressor (XGBR) is the best model for estimating stunting prevalence, with a Root Mean Squared Error (RMSE) of 3.2076 and an value of 0.7223. Meanwhile, for clustering results, K-Means Clustering is identified as the most effective method for grouping districts/cities based on socioeconomic and environmental factors. The clustering process produced two groups, such as vulnerable (Cluster 1) and highly vulnerable (Cluster 2), with connectivity, Dunn Index, and silhouette coefficient values of 29.290, 0.6931, and 0.4509, respectively. These findings are expected to serve as a basis for policymakers in formulating targeted interventions to reduce stunting rates, particularly in highly vulnerable areas.
Sentiment Analysis of Presidential and Vice Presidential Candidates Using FastText CNN Zulkarnaen, Fahri Izzuddin; Puspita Sari, Anggraini
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.v6i1.121

Abstract

The Internet has become a platform for storing and accessing a wide range of public information. People tend to use social media to express opinions on products, political policies, and politicians, both as individuals and as parties. Sentiment analysis of presidential and vice-presidential candidates aims to understand public perspectives on each candidate. Based on sentiment analysis of the Indonesian public on social media, potential candidates for the presidential election, which is still over a year away, can be identified. This study categorizes sentiments into four classes: happy, love, sad, and angry. The model employs FastText embeddings with a Convolutional Neural Network (CNN). The best performance achieved was an F1-score of 0.9510. The findings indicate that Ganjar Pranowo leads as a presidential candidate, while Erick Thohir stands out as the leading vice-presidential candidate.
Simulation of an IoT Based Smart System for Monitoring Hand Hygiene of Medical Personnel in Hospitals Tunggadewi, Elsyea Adia; Dina Nur nahdliyah, Sisca; Prayitno, Hadi
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.v6i1.125

Abstract

Improving hand hygiene of medical personnel in hospitals is one of the main steps in preventing nosocomial infections which can threaten patient safety. However, based on WHO data, only around 40% of medical personnel consistently comply with hand hygiene protocols. Obstacles such as lack of supervision and technological limitations also worsen this condition. Therefore, this research proposes the development of an Internet of Things (IoT) based system that can monitor the hand hygiene of medical personnel in real-time, as an effort to increase compliance and support more effective hand hygiene management in the hospital environment. This research aims to design an intelligent IoT system that is able to detect and record the hand hygiene activities of medical personnel automatically, based on predetermined hand hygiene protocols. This system will also be equipped with data analysis features to help hospital management evaluate the level of medical personnel's compliance with hygiene standards. By utilizing sensor technology, this system is expected to be able to provide innovative, efficient and integrated solutions, so that it can support more optimal hygiene management in health facilities. The research method consists of three main stages. The first stage is a needs analysis and literature study to understand relevant hand hygiene standards and IoT technologies. The second stage involves system design, including virtual microcontroller-based hardware modeling, sensor simulation, and software development in a simulation environment. The third stage is system simulation and performance testing in a virtual hospital environment to evaluate its effectiveness. An iterative approach is applied to ensure the simulation results comply with theoretical user needs and applicable standards.
Comparative Analysis of Android 10 and Android 14 in the Eventeer Application Aris Pratama; Wijanarko, Rendi Panca; Suryantari, Putu Anggi
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.v6i1.127

Abstract

In this era of rapid technological advancement, event management systems have undergone significant transformation towards mobile-based models. One prominent application in this domain is Eventeer, offering features such as in-app chat, event scheduling, learning media, and event feeds for user interaction. This study aims to compare the performance of Android operating system versions 10 and 14 when running the Eventeer application. Performance measurements include memory usage, CPU utilization, and total storage used. Testing was conducted using Android Profiler in Android Studio to obtain accurate and comprehensive data. Results indicate that Android 14 exhibits higher memory usage compared to Android 10, while CPU usage tends to be more efficient on Android 14. The application size and storage usage did not show significant differences between the two Android versions. These findings provide valuable insights for developers and users on how the performance of the Eventeer application may be influenced by the Android OS version used. The study recommends further research encompassing broader test aspects such as battery consumption and network usage. Thus, future research can provide deeper insights into the factors affecting the performance of Android operating systems on mobile applications like Eventeer.
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.
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.
Feature Engineering Optimization on the Performance of XGBoost, Random Forest, and Support Vector Regression Algoritms in House Price Prediction Trenggono, Brahmantio Widyo; Diyasa, I Gede Susrama Mas; Rahajoe, Ani Dijah
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.v6i1.149

Abstract

As the years go by, the ever-increasing movement of house prices has become an important factor in investment decisions and financial planning to curb inflation. However, fluctuations or increases in house prices can be caused by various factors that can affect the value of house price predictions. This study aims to analyze the influence of optimization and the relationship between feature engineering and modeling in house price predictions. The research stages include data preprocessing, logarithmic transformation, feature engineering, data splitting, and optimization in determining parameters during tuning. Model performance is evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Determination coefficient (R-Squared) metrics. The results show that the Support Vector Regression algorithm produces the best performance with a MAE value of 274 million, an RMSE of 780 million, a MAPE of 7%, and an R-Squared of 98%. This research is expected to serve as a reference for future studies on regression model optimization, particularly in decision-making for more accurate house price predictions.

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