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All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Mei Sarah Siregar
Universitas Islam Negeri Sumatera Utara
Decision Sciences, Operations Research & Management Education Mathematics

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PENERAPAN METODE MARKOV CHAIN ANALYSIS DALAM MEMPREDIKSI TINGKAT ELEKTABILITAS CALON PRESIDEN 2024 MELALUI TAGAR SOSIAL MEDIA DAN GOOGLE TRENDS Mei Sarah Siregar; Rina Widyasari
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 2 (2023): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v4i2.389

Abstract

To summarize the findings of the research on the prediction of electability for potential presidential candidates in the 2024 election using the Markov chain method, the study found the following results: For social media hashtags: Anies Baswedan: 0.404817, Ganjar Pranowo: 0.218493, Prabowo Subianto: 0.158993, Erick Thohir: 0.217697 For Google Trends: Anies Baswedan: 0.473078, Ganjar Pranowo: 0.328017, Prabowo Subianto: 0.128324, Erick Thohir: 0.070581 These results indicate that Anies Baswedan has the highest predicted electability based on both social media hashtags and Google Trends. Ganjar Pranowo ranks second in both categories. However, in terms of social media hashtags, Erick Thohir has a significantly higher electability than Prabowo Subianto, while in Google Trends, Prabowo Subianto has a slightly higher electability than Erick Thohir. It is important to note that these predictions may change over time as more data becomes available. Therefore, continuous calculations using the Markov chain method are necessary to update the predictions of presidential candidates' electability
Co-Authors Rina Widyasari