cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
ijconsist@upnjatim.ac.id
Phone
+6281999471017
Journal Mail Official
ijconsist@upnjatim.ac.id
Editorial Address
https://ijconsist.org/index.php/ijconsist/about/editorialTeam
Location
Kota surabaya,
Jawa timur
INDONESIA
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 91 Documents
 6   7   8   9   10 
Control and Monitoring System of IOT-Based Orchid Culvivation Tunggadewi, Elsyea Adia; Imansyah, Muhammad Hizbullah
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.v5i1.120

Abstract

Abstract—This study focuses on orchid plants, specifically the Phalaenopsis species, commonly known as the moth orchid, which is popular in Indonesia due to its beauty and stable flower prices. Orchid cultivation requires special attention to environmental factors such as light intensity, temperature, air humidity, and soil moisture. To enhance the efficiency of plant care, this research designs an Internet of Things (IoT)-based control and monitoring system. The system utilizes DHT22 sensors to monitor temperature and air humidity, BH1750 sensors to measure light intensity, and soil moisture sensors to measure soil moisture. The data collected by these sensors are transmitted in real-time to a NodeMCU ESP32 connected to the Firebase platform. Users can monitor and control plant conditions through an Android application linked to Firebase. Testing indicates that the sensors used provide accurate results in monitoring soil moisture. The water pump control system based on soil moisture and air humidity has proven effective, with consistent responses to environmental changes. This system is expected to assist cultivators in improving water usage efficiency and plant condition monitoring. The implementation of IoT technology in orchid cultivation can be an innovative solution to enhance the quality and quantity of ornamental plant production in the future.
Comparison of C4.5 Decision Tree and Naive Bayes Algorithms for Classification of Nutritional Status in Stunting Toddlers Ishak Febrianto; Anggraini Puspita Sari
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.v5i1.122

Abstract

Stunting is a condition where growth and development of children under 5 years of age is impaired due to chronic malnutrition. Data mining with classification techniques on the nutritional status of stunting toddlers can be performed to help identify toddlers experiencing stunting and provide objective measurements of their nutritional status. There are several classification methods, but this research will compare the performance of the C4.5 decision tree algorithm, which is included in the decision tree approach, and naive Bayes, which uses a probability-based approach of class occurrence in classifying nutritional status of stunting toddlers, with discretization performed in the preprocessing stage. The data used in this research was obtained from Jagir Health Center, Surabaya, in the form of secondary data on toddler nutrition in 2021, totaling 2,801 records. The labeling of stunting or normal in the dataset uses the reference of child anthropometric standards in Indonesia as stated in the Republic of Indonesia Minister of Health Regulation number 2 of 2020. The best method based on the AUC (Area Under the Curve) value was the C4.5 decision tree with a value of 86% (good classification), while naive Bayes achieved 77% (fair classification) using a 70:30 training and testing data ratio.
Nutritional Status; Infants and Toddlers; LightGBM; Posyandu; Nutrition Prediction; Doko Village; Monitoring Application Muhammad Thoriqulhaq; Idhom, Mohammad; Maulida, Kartika
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.v5i1.136

Abstract

This study aims to implement a nutritional status index for infants and toddlers in Doko Village, Kediri Regency, using the LightGBM algorithm. Child health issues in Indonesia, particularly stunting, are a serious concern due to chronic malnutrition, recurring infections, and insufficient psychological stimulation during early developmental stages. Doko Village was selected as the research location due to significant challenges related to child nutrition in the area. The LightGBM algorithm was chosen for its ability to process large and imbalanced datasets while providing accurate predictions. The data used in this study comes from weight and height measurements of children at the local Posyandu. The main objective of this research is to develop a predictive model that can help healthcare workers identify children at risk of malnutrition, enabling more precise interventions. Additionally, this study developed a web-based application to monitor nutritional status in real-time, which is expected to improve the quality of life for children in Doko Village and nearby areas facing similar challenges.
Machine Learning for Password Strength Classification Using Length and Entropy Tanjung Arswendo Yudha; Reyhan, Muhamad; Mutmainnah, Dianisa; Hakiem, Nashrul
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.v5i1.139

Abstract

Password security is a critical cybersecurity challenge due to the prevalence of user-generated weak credentials, so automated evaluation methods are needed. This paper develops a Random Forest classification model to predict password strength based on two main features, namely password length and Shannon entropy, trained on a large-scale public dataset. The model achieved a classification accuracy of 91.5% on the test data, where feature importance analysis identified entropy as the most significant predictor. The resulting high-accuracy model is suitable for integration into real-time password strength feedback systems and provides a quantitative basis for formulating stronger security policies.
Implementation of A* Algorithm and Contraction Hierarchies for Delivery Route Optimization (Case Study: CV. Almaed.id) Gunawan, Boy Erdyansyah; Idhom, Mohammad; Akbar , Fawwaz Ali; Riyantoko, Prismahardi Aji
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.v5i2.140

Abstract

In the digital era, manufacturing companies like CV. Almaed.id are required to have an efficient distribution system to compete in the furniture industry. This study proposes the application of the A* algorithm and Contraction Hierarchies (CH) to optimize product delivery routes. This system utilizes road network data from OpenStreetMap and calculates geographic distances using the Haversine method. Implementation results show that the combination of A*, CH, and Haversine can accelerate route calculation and reduce operational costs compared to manual methods.
Sentiment Analysis on Generation Z News Article using Support Vectore Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) Kartini, Kartini; Hindrayani, Kartika Maulida; Puspasari, Betty Dewi
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.v5i2.141

Abstract

The development of digital media has increased the volume of news articles discussing various issues, including those involving Generation Z. Understanding public perception of these news items can be achieved by applying a crucial approach, namely sentiment analysis. This study aims to classify sentiment in news articles about Generation Z using the Support Vector Machine (SVM) algorithm. The main challenge in sentiment analysis is data class imbalance, where the amount of positive and negative sentiment data is often unbalanced. Therefore, the Synthetic Minority Over-sampling Technique (SMOTE) is used to address this problem by balancing the class distribution before model training. The datasets used were collected from various online news portals and analyzed through text preprocessing, feature extraction using Bag of Word, and SVM model training. The evaluation results show that the application of SMOTE significantly improves the model's performance in classifying sentiment, with improvements in accuracy, precision, recall, and F1-score compared to the model without data imbalance handling. This study demonstrates that the combination of SVM and SMOTE is effective in conducting sentiment analysis on Generation Z news articles. The accuracy shows 84% with 83% precision and 76% recall.
This Detection of Hate Speech in Social Media Using Machine Learning Akbar, Amin Kurniawan; Ridha, Afif Nabil; Muthmainnah, Ami Chandra; Irsyad, Muhammad; Hakiem, Nashrul; Broer, Rizal
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.v5i2.142

Abstract

This paper addresses the critical issue of hate speech detection in social media, a growing concern given the widespread use of online platforms for communication and information dissemination. The proliferation of hate speech contributes to online harassment, discrimination, and the propagation of harmful ideologies, posing significant societal challenges. This study proposes a machine learning-based approach for identifying and classifying hate speech across various social media datasets. We leverage a comprehensive collection of parsed datasets, including those related to aggression, attack, toxicity, and specific instances from Twitter (general, racism, sexism), YouTube, and Kaggle. The methodology involves data preprocessing, feature extraction, and the application of machine learning algorithms to effectively distinguish hate speech from benign content. Our findings aim to contribute to the development of robust automated systems for content moderation, fostering safer and more inclusive online environments.
A Survey On Causal Consistency Implementation In Geo-Replicated Cases Syukron, Muhamad; Nisa, Chilyatun; Aziz, Abdul; Ijtihadie, Royyana Muslim
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.v5i2.145

Abstract

Distributed storage systems are a fundamental component of large-scale internet services. To meet the in-creasing needs of users regarding availability and latency, the design of data storage systems has developed into data replication techniques, one of which is geo-replication. Causal consistency is an attractive method for storing geo-replicated data because it is at the crucial point between ease of programming and resulting performance. This method also enables high availability and low latency. However, when implemented into cloud storage, there are limitations regarding throughput and costs. We surveyed several models using methods related to causal consistency in geo-replication cases designed by previous researchers. The mod-els used were derived from papers on causal consistency in geo-replication cases published within the last five years. In this study, we compared the performance of previously designed models based on their performance results. The results of this study are grouping models based on throughput and latency performance obtained.
Image Synthesis for Sperm Dataset Augmentation using WGAN-GP Hajjar Ayu Cahyani Kuswardhani; I Gede Susrama Mas Diyasa; Mohammad Idhom
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.v5i2.146

Abstract

This research explores the efficacy and limitations of applying a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to generate synthetic human sperm microscopy images for data augmentation. We assessed the WGAN-GP's performance on a complex, heterogeneous dataset where images contained multiple object types. Despite achieving stable training convergence, the model's output quality was suboptimal, as evidenced by a high Fréchet Inception Distance (FID) score of 134 and qualitative signs of partial mode collapse. The generator struggled to capture the complete morphological diversity of the sperm cells. A second experiment using a dataset pre-sorted into distinct classes (Normal, Abnormal, Non-Sperm) yielded a marked improvement. This approach led to substantially lower FID scores (59.19, 74.92, and 83.56) and exhibited more robust training dynamics. Our findings underscore a critical conclusion: the success of WGAN-GP in this domain is fundamentally tied to the simplicity of the data distribution. We recommend that future efforts leverage class-conditioned models, simplified data structures, and refined generator architectures to achieve high-precision augmentation for medical imaging tasks.
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.

Page 9 of 10 | Total Record : 91

 6   7   8   9   10 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol 7 No 1 (2025): September Vol 6 No 2 (2025): March Vol 6 No 1 (2024): September Vol 5 No 2 (2024): March Vol 5 No 1 (2023): September Vol 4 No 2 (2023): March Vol 4 No 1 (2022): September Vol 3 No 2 (2022): March Vol 2 No 02 (2021): March Vol 3 No 1 (2021): September Vol 2 No 1 (2020): September Vol 1 No 2 (2020): March Vol 1 No 1 (2019): September More Issue