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p-Index From 2021 - 2026
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All Journal ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal
Salsabila Dwi Fitri
Jambi University, Jambi, Indonesia
Computer Science & IT Control & Systems Engineering

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The Role of Data Pre-Processing Techniques and Classification Algorithms on the Accuracy of Sentiment Analysis in Social Media: A Literature Review Salsabila Dwi Fitri; Yorasakhi Ananta
ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal Vol. 1 No. 1 (2025): Artificial Intelligence and Robotic Journal (July - Desember 2025)
Publisher : Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/artificial.v1i1.4

Abstract

The development of digital technology and the explosion of data on social media have increased the need for accurate sentiment analysis to understand public opinion. This article aims to systematically review the role of data pre-processing techniques and classification algorithms in improving the accuracy of sentiment analysis in social media. Through the Systematic Literature Review (SLR) approach, more than 30 scientific articles from trusted sources were reviewed between 2018 and 2024. The results of the study show that effective pre-processing such as tokenization, stemming, and stop word removal significantly improve the quality of input data, while algorithms such as SVM, Random Forest, and deep learning provide the best performance in sentiment classification. This article is expected to be a conceptual reference for further research and the development of a more precise sentiment analysis system.
Contribution of Big Data and Cloud Computing Integration to Large-Scale Data Analytics Process Efficiency: A Literature Review Yorasakhi Ananta; Salsabila Dwi Fitri
ARTIFICIAL RESEARCH: Artificial Intelligence and Robotic Journal Vol. 1 No. 1 (2025): Artificial Intelligence and Robotic Journal (July - Desember 2025)
Publisher : Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/artificial.v1i1.5

Abstract

This article explores the contribution of Big Data and Cloud Computing integration to the efficiency of large-scale data analytics processes. Big Data technology provides the ability to manage large volumes, velocity, and variety of data, while Cloud Computing offers an elastic and scalable platform for data storage and processing. This study shows that the synergy between these two technologies improves the speed, accuracy, and efficiency of data processing, enabling organizations to make data-driven decisions faster and more precisely. The results of the reviewed literature show that the use of Cloud Computing reduces infrastructure costs and accelerates big data processing, while Big Data provides deeper insights into hidden trends and patterns. Overall, this article confirms that the integration of Big Data and Cloud Computing plays a significant role in improving the efficiency of data analytics, as well as providing a competitive advantage for organizations that can properly utilize both technologies.
Co-Authors Yorasakhi Ananta