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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 5 No 2 (2024): March" : 7 Documents clear
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.
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.

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