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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Abdul Halim
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Sentimen Masyarakat Di Twitter Terhadap Prabowo Subianto Sebagai Bakal Calon Presiden 2024 Menggunakan M-KNN Abdul Halim; Yusra Yusra; Muhammad Fikry; Muhammad Irsyad; Elvia Budianita
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4054

Abstract

Presidential elections are held every five years and each presidential candidate will get support from several political parties to run for candidacy in the election. In a multi-party system, the number of parties participating in the election is very large, so that the perspectives of voters on political actors, including presidential candidates who will advance in the 2024 elections, are varied. The survey results from Polling Indonesia (SPIN) conducted from 7 to 16 October 2022 show that Prabowo Subianto has the highest electability with a score of 31.6%, based on a national leadership survey. In this study, a test was carried out by classifying tweet data from the public collected on the Twitter application from January to December 2022 using the Modified k-Nearest Neighbor method to analyze public sentiment regarding the upcoming election. Data collected as many as 2,100 data with positive and negative categories related to "Presidential Candidate" and "Prabowo Subianto" and the implementation of the Modified k-Nearest Neighbor classification was carried out using Google Colab. Based on the results of the confusion matrix test from the Modified k-Nearest Neighbor classification with three comparisons made (ie comparisons 70%:30%, 80%:20% dan 90%:10%) and using K=3, 5, 7, 9, 11 when testing a comparison of 90:10 at K=3 the highest accuracy results were obtained with a value of 93,3%.
Co-Authors Elvia Budianita Muhammad Fikry Muhammad Irsyad Yusra Yusra