Perbandingan Kinerja Metode Random Forest dan Naive Bayes Untuk Analisis Sentiment Masyarakat Terhadap Childfre Pada Twiter.

Irianto, Giwa Salsabila (2024) Perbandingan Kinerja Metode Random Forest dan Naive Bayes Untuk Analisis Sentiment Masyarakat Terhadap Childfre Pada Twiter. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Fenomena childfree mulai menjadi suatu konsep pernikahan bagi masyarakat ketika suami dan istri memilih dan berkomitmen untuk tidak memiliki anak dalam pernikahannya. Faktor munculnya fenomena tersebut yaitu faktor ekonomi, ketidaksiapan mental dalam mengasuh dan membesarkan anak, tingkat kesibukan pasangan suami-istri dan bahkan isu permasalahan lingkungan sosial. Fenomena childfree masih menuai pro dan kontra karena adanya perbedaan pendapat dan sudut pandang mengenai kehadiran anak di dalam pernikahan. Pro dan kontra terhadap fenomena tersebut dapat dilihat pada media sosial Twitter. Fokus penelitian ini adalah pada analisis sentimen terhadap pandangan masyarakat mengenai childfree di Twitter, menggunakan metode analisis Random Forest dan Naïve Bayes. Dalam mendapatkan data sentimen masyarakat terhadap childfree di media sosial Twitter pada penelitian ini dilakukan menggunakan teknik scrapping berupa tweet dari masyarakat Indonesia mengenai childfree, selain itu tweet tersebut mengandung Bahasa Indonesia. Berdasarkan pengumpulan data tersebut diperoleh sebayak 1309 tweet. Hasil evaluasi menunjukkan bahwa Random Forest memiliki tingkat akurasi yang lebih tinggi (81.85%) dibandingkan dengan Naïve Bayes (78.75%) ketika Pos Tagger digunakan. Namun, tanpa Pos Tagger, Naïve Bayes mencapai akurasi lebih tinggi (69.72%) daripada Random Forest (67.33%). Studi ini memberikan wawasan tentang respons masyarakat terhadap fenomena childfree dan perbandingan kinerja dua metode analisis sentimen.

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