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International Journal of Computer Technology and Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481899     EISSN : 30481961     DOI : 10.62951
Core Subject : Science,
Computer Science & IT
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. The scope of the this Journal covers the fields of Computer Technology and Science. This journal is a means of publication and a place to share research and development work in the field of technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science" : 5 Documents clear
Optimization Of Big Data Processing Using Distributed Computing In Cloud Environments Rahul Dev Singh; Vikram Kumar Gupta; Priya Anjali Patel
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.58

Abstract

The growth of big data has driven the need for efficient data processing methods, especially in cloud computing environments. This study evaluates distributed computing frameworks like Apache Hadoop and Apache Spark for optimizing big data processing. By analyzing different configurations, we demonstrate how distributed systems can significantly reduce processing time and improve resource utilization, making them ideal for handling complex datasets in cloud environments.
Sentiment Analysis Of Social Media Data Using Deep Learning Techniques Salsabila Septiani; Nabila Putri; Dara Jessica; Arya Saputra
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.59

Abstract

Social media platforms contain vast amounts of data that can reveal public sentiment on various topics. This research explores the application of deep learning techniques, particularly convolutional neural networks (CNN) and recurrent neural networks (RNN), to analyze sentiment within social media text. The results indicate that these models achieve high accuracy in sentiment classification, making them valuable tools for companies seeking to understand public opinion.
Enhancing Cybersecurity In Smart Cities Through IoT Device Management Siti Aminah Binti Ismail; Ahmad Faizal Bin Mohd Ali
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.62

Abstract

The rise of smart cities brings increased interconnectivity, but also new security vulnerabilities, especially among IoT devices. This study investigates methods for improving cybersecurity in smart cities by implementing IoT device management protocols. We examine approaches such as network segmentation and secure authentication to mitigate common threats, thus providing a safer environment for urban digital infrastructure.
Automated Detection Of Network Intrusions Using Machine Learning in Real-Time Systems Aulia Novi; Ryan Satria
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.63

Abstract

Network intrusion detection is crucial for maintaining the integrity of real-time systems. This paper evaluates various machine learning algorithms, including support vector machines (SVM) and decision trees, for real-time intrusion detection. Through extensive testing on simulated datasets, the study highlights the advantages of automated detection in reducing response times and enhancing network security.
The Role Of Quantum Computing in Optimizing Machine Learning Algorithms Nattapong Chaiyathorn; Pimchanok Anuwat
International Journal of Computer Technology and Science Vol. 1 No. 2 (2024): April : International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v1i2.64

Abstract

Quantum computing has the potential to revolutionize machine learning by offering exponential speed-up for specific algorithms. This study explores the theoretical and practical implications of using quantum computing to optimize machine learning models, such as in training neural networks. The findings provide insights into the possible improvements in computational efficiency, particularly for large datasets and complex models.

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