NURRIZKY, FADLI (2024) KOMPARASI ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) TENTANG POPULARITAS CALON PRESIDEN PADA PILPRES 2024. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study aims to compare the effectiveness of two classification algorithms, Naive Bayes and Support Vector Machine, in analyzing the popularity of presidential candidates for the 2024 Presidential Election. The popularity of presidential candidates plays a crucial role in campaign strategies and political decision-making in the modern political era. This research utilizes data from social media, encompassing public sentiment towards presidential candidates and related political issues. The research results indicate that SVM achieves an accuracy rate of 97%, while Naive Bayes achieves 95%, demonstrating the superiority of SVM in predicting the popularity of presidential candidates. In conclusion, the selection of the appropriate algorithm for analyzing complex political data has a significant impact, and the high accuracy rates of both algorithms provide valuable guidance for political decision-makers and campaign teams in preparation for the 2024 Presidential Election. Keywords: Naive Bayes, Support Vector Machine, presidential candidate popularity, 2024 Pilpres, accuracy rate, sentiment analysis. Studi ini bertujuan membandingkan dua algoritma klasifikasi, Naive Bayes serta Support Vector Machine, dalam menganalisis popularitas calon presiden untuk Pemilihan Presiden (Pilpres) 2024. Popularitas calon presiden memiliki peran krusial dalam strategi kampanye dan pengambilan keputusan politik di era politik modern. Studi ini menggunakan data dari media sosial yang mencakup sentimen publik terhadap calon presiden dan isu-isu politik terkait. Hasil studi menunjukkan bahwa SVM mencapai tingkat akurasi 97%, sedangkan Naive Bayes mencapai 95%, menunjukkan bahwa SVM unggul dalam memprediksi popularitas calon presiden. Kesimpulannya, pemilihan algoritma yang tepat dalam menganalisis data politik kompleks memiliki dampak signifikan, dan tingkat akurasi yang tinggi dari kedua algoritma ini memberikan panduan berharga bagi pengambil keputusan politik dan tim kampanye dalam persiapan Pilpres 2024. Kata Kunci : Naive Bayes, Support Vector Machine, popularitas calon presiden, Pilpres 2024, tingkat akurasi, analisis sentimen.
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