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All Journal Jurnal Riset Informatika
Ihsan Aulia Rahman
Universitas Nusa Mandiri
Computer Science & IT

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TWITTER SENTIMENT ANALYSIS ON THE 2024 PRESIDENTIAL DISPUTE DECISION USING NAÏVE BAYES AND SVM Ihsan Aulia Rahman; Nanang Ruhyana
Jurnal Riset Informatika Vol. 7 No. 1 (2024): December 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i1.355

Abstract

Public sentiment regarding the 2024 presidential election dispute decision was analyzed through the Twitter platform. The method employed was Naïve Bayes, implemented using RapidMiner software. The dataset consisted of thousands of tweets collected during the presidential election dispute period. Each tweet was classified into three sentiment categories: positive, negative, and neutral. The text mining process involved data cleaning, tokenization, and the application of natural language processing (NLP) techniques for feature extraction. The results of the analysis revealed the distribution of sentiments among Twitter users and changes in sentiment trends over specific periods. This research is expected to provide insights into public perceptions and sentiment patterns related to the presidential election dispute decision
Implementation of the Association Method in the Analysis of Sales Data From Manufacturing Companies Fachri Amsury; Nanang Ruhyana; Andry Agung Riyadi; Ihsan Aulia Rahman
Jurnal Riset Informatika Vol. 5 No. 1 (2022): December 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1038.868 KB) | DOI: 10.34288/jri.v5i1.201

Abstract

The company produces sales data every day. Over time, the data increases, and the amount becomes very large, and the data is only stored without understanding the benefits that exist from these data due to limitations in proper knowledge in analyzing the data, especially transaction data. Sale. In order to overcome these problems, a study focused on reprocessing sales transaction data in 2018 with a data mining technique approach using the Knowledge Discovery in Database concept using the association method and apriori algorithm and a supporting application, namely RapidMiner. This study aims to help companies find customer buying habits or patterns based on 2018 sales transaction data. The results of this study produce 316 association rules where the best rules are generated on record 309 with PRO 889 & PRO 868 PRO 869 rules.
Co-Authors Andry Agung Riyadi Fachri Amsury Nanang Ruhyana