ANALISIS SENTIMEN TERHADAP IBU KOTA NUSANTARA MENGGUNAKAN OPINI PENGGUNA TWITTER METODE NAÏVE BAYES

Mahendra Adlarijal, . (2025) ANALISIS SENTIMEN TERHADAP IBU KOTA NUSANTARA MENGGUNAKAN OPINI PENGGUNA TWITTER METODE NAÏVE BAYES. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Pemindahan Ibu Kota Negara Indonesia dari Jakarta ke Ibu Kota Nusantara (IKN) telah menjadi isu nasional yang memunculkan beragam reaksi di masyarakat, terutama di media sosial seperti Twitter. Penelitian ini bertujuan untuk menganalisis sentimen masyarakat Indonesia terhadap pemindahan ibu kota dengan menggunakan algoritma Naïve Bayes. Data yang digunakan berupa 1.014 tweet berbahasa Indonesia yang dikumpulkan melalui Twitter API dengan kata kunci "Ibu Kota Nusantara". Data kemudian melalui tahap pre-processing seperti cleaning, case folding, normalization, stopwords removal, stemming, dan tokenization. Selanjutnya, tweet diberi label sentimen positif atau negatif secara manual sebelum dilakukan klasifikasi dengan metode Naïve Bayes. Evaluasi model dilakukan dengan confusion matrix dan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mampu mengklasifikasikan sentimen publik dengan akurasi yang cukup baik. Penelitian ini diharapkan dapat memberikan wawasan kepada pemerintah dan publik terkait persepsi masyarakat terhadap kebijakan pemindahan ibu kota. **** The relocation of Indonesia’s capital city from Jakarta to Nusantara (IKN) has become a national issue that has generated various public responses, particularly on social media platforms such as Twitter. This study aims to analyze public sentiment toward the capital relocation policy using the Naïve Bayes algorithm. The dataset consists of 1,014 Indonesian-language tweets collected via the Twitter API using the keyword “Ibu Kota Nusantara.” The data underwent several pre processing stages, including cleaning, case folding, normalization, stopword removal, stemming, and tokenization. Tweets were manually labeled as either positive or negative sentiments before being classified using the Naïve Bayes method. The model was evaluated using a confusion matrix and performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the Naïve Bayes algorithm is capable of classifying public sentiment with reasonably good accuracy. This research is expected to provide insights for both the government and the public regarding societal perceptions of the capital relocation policy.

Item Type:	Thesis (Sarjana)
Additional Information:	1.) Muhammad Ficky Duskarnaen, M.Sc ; 2.) Murien Nugraheni, S.T., M.Cs.
Subjects:	Teknologi dan Ilmu Terapan > Teknik Komputer
Divisions:	FT > S1 Pendidikan Teknik Informatika Komputer
Depositing User:	Mahendra Adlarijal .
Date Deposited:	14 Aug 2025 02:33
Last Modified:	14 Aug 2025 02:33
URI:	http://repository.unj.ac.id/id/eprint/60580

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