Comparison of Sentiment Analysis from Twitter Data Collection with Naïve Bayes, Decision Tree, and k-Nearest Neighbor Methods

Erwin Apriliyanto; Yayu Sri Rahayu

doi:10.30646/sinus.v22i2.833

Comparison of Sentiment Analysis from Twitter Data Collection with Naïve Bayes, Decision Tree, and k-Nearest Neighbor Methods

Erwin Apriliyanto, Yayu Sri Rahayu

Abstract

Dalam konteks pesatnya perkembangan pengguna media sosial di Indonesia, khususnya Twitter, data yang dihasilkan memberikan informasi berharga untuk penelitian dan pengambilan keputusan. Penelitian ini bertujuan untuk mengklasifikasikan tweet berbahasa Indonesia ke dalam kategori positif, negatif, dan netral. Hasil pengujian menunjukkan bahwa metode Decision Tree memiliki rata-rata presisi kelas yang lebih baik dibandingkan dengan K-nearest neighbour (K-NN) dan Naïve Bayes. Algoritma K-NN memiliki rata-rata presisi kelas sebesar 54.60%, Decision Tree mencapai 72.85%, dan Naïve Bayes sebesar 47.66%. Selain itu, penggunaan Decision Tree menghasilkan presisi yang tinggi untuk kelas Negatif (90,00%) dan kelas Positif (81,82%) .

Keywords

Sentiment Analysis, Twitter, Naïve Bayes, Decision Tree, k-Nearest Neighbor

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DOI: http://dx.doi.org/10.30646/sinus.v22i2.833

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