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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Instal : Jurnal Komputer
Hutagalung , Fatma Sari
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Analysis Voting Sentiment In The 2024 Election Via Twitter Text Mining And K-Means Classification Approach Lubis, Ahmad Raihan; Hutagalung , Fatma Sari
Bahasa Indonesia Vol 16 No 03 (2024): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v16i03.228

Abstract

This research examines opinion sentiment regarding (1) voters in the 2024 election using data analysis from the social media Twitter. (2) Using a text mining and classification approach, (1) this research extracts valuable information from tweets containing keywords related to the 2024 election. The data collection process is carried out using scraping techniques, where tweets are collected within a certain period of time to ensure complete representation. After the data is collected, (2) preprocessing is carried out to clean and prepare the text, which includes steps such as tokenaize, stopword and labeling. (1) Sentiment analysis is then used to categorize tweets into positive, negative or neutral sentiment. (2) The K-Means algorithm is used to collect opinion data to help identify patterns and trends in public perception of political candidates and issues. (1) Analysis results shows that there is a significant distribution of opinions between different candidates and issues, thus revealing the complex dynamics of public opinion. (2)These results provide policymakers, political candidates, and researchers with an in-depth understanding of how public opinion is formed and how it can be influenced during election campaigns. Additionally, this research highlights the great potential of applying text mining technologies and algorithms
Optimization of Feature Extraction in Images Using Variants of Decomposition Algorithms Hutagalung , Fatma Sari; Siregar, Farid Akbar; Al-Khowarizmi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.12705

Abstract

This research aims to optimize the feature extraction process in digital images using two decomposition algorithms, namely Haar and Riyad. Feature extraction is an important step in digital image processing, used to extract significant information from images for applications such as pattern recognition, medical image analysis, and surveillance systems. Haar and Riyad algorithms are tested on three types of images: grayscale, color, and texture. Results show that Haar's algorithm excels in processing speed with an average time of 121.67 ms, making it ideal for real-time applications. In contrast, the Riyad algorithm showed higher feature detection accuracy, achieving an average of 93.33% on complex images, despite requiring a longer processing time of 154 ms. This research shows that the selection of a feature extraction algorithm should consider the type of image and the application needs. Haar's algorithm is suitable for real-time surveillance applications, while Riyad is more suitable for in-depth analysis such as on medical images. The significant contribution of this research is that it provides insight into the trade-off between speed and accuracy, and opens up opportunities to develop hybrid methods that combine the advantages of both algorithms to create more efficient and effective image processing solutions.
Co-Authors Al Khowarizmi Farid Akbar Siregar Lubis, Ahmad Raihan