ANALISIS SENTIMEN BERITA PEMILU 2024 MENGGUNAKAN KLASIFIKASI NAIVE BAYES

Muhammad Fauzan, - (2024) ANALISIS SENTIMEN BERITA PEMILU 2024 MENGGUNAKAN KLASIFIKASI NAIVE BAYES. S1 thesis, Universitas Pendidikan Indonesia.

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Official URL: https://repository.upi.edu/

Abstract

Pemilihan Umum (Pemilu) tahun 2024 merupakan sarana kedaulatan masyarakat untuk memilih Calon Presiden dan Wakil Presiden. Media daring juga menjadi sumber berita bagi masyarakat dalam mengonsumsi pemberitaan pemilu 2024. Peliputan berita yang dilakukan oleh media mengandung banyak sentimen seperti netral, positif, atau negatif. Penelitian ini bertujuan untuk menganalisis sentimen berita pemilu 2024 menggunakan Naive Bayes Classifier. Penelitian ini memanfaatkan metode web scraping untuk mengumpulkan data berita berdasarkan kata kunci pemilu 2024 dari media daring detik.com. Data berita yang diperoleh kemudian diproses melalui tahap praproses yang meliputi case folding, tokenisasi, stopword removal, dan stemming. Selanjutnya, data tersebut dilabeli sebagai positif, netral, atau negatif menggunakan library NLTK Sentiment Analyzer. Setelah pelabelan, data tersebut dianalisis sentimen menggunakan Algoritma Naive Bayes Classifier dengan dua skenario eksperimen. Skenario pertama adalah analisis sentimen menggunakan Naive Bayes Classifier tanpa tuning. Hasil dari eksperimen pertama menunjukkan akurasi model sebesar 57%. Skenario eksperimen kedua menerapkan hyperparameter alpha pada Algoritma Naive Bayes Classifier. Hasil dari skenario eksperimen kedua menunjukkan akurasi sebesar 65%. Selanjutnya, dilakukan analisis sentimen pada data berita untuk mengetahui sentimen berita pemilu 2024 dengan hasil sentimen netral sebesar 55,2%, sentimen positif sebesar 27,8%, dan sentimen negatif sebesar 17%. Dari hasil penelitian ini, dapat ditarik kesimpulan bahwa analisis sentimen pada berita pemilu 2024 cenderung netral. The 2024 General Election is a means for the public to exercise their right by electing the President and Vice President candidates. Online media also serves as a news source for the public to consume news about the 2024 election. News coverage by media contains various sentiments such as neutral, positive, or negative. This study aims to analyze the sentiment of news articles about the 2024 election using the Naive Bayes Classifier. This research use web scraping methods to collect news data based on the keyword "Pemilu 2024" from the online media site detik.com. The collected news data is then preprocessed, including case folding, tokenization, stopword removal, and stemming. The preprocessed data is then labeled as positive, neutral, or negative using the NLTK Sentiment Analyzer library. Following that labeling, sentiment analysis is performed using the Naive Bayes Classifier algorithm with two experimental scenarios. The first scenario is sentiment analysis using the Naive Bayes Classifier without tuning. The results from the first experiment show a model accuracy of 57%. The second experimental scenario involves applying the alpha hyperparameter to the Naive Bayes Classifier algorithm. Results from the second experiment show an accuracy of 65%. Subsequently, sentiment analysis was conducted on the news data to determine the sentiment of the 2024 election news, resulting in 55.2% neutral sentiment, 27.8% positive sentiment, and 17% negative sentiment. From these findings, it can be concluded that the sentiment analysis of the 2024 election news tends to be neutral.

Item Type: Thesis (S1)
Additional Information: https://scholar.google.com/citations?user=uS_S4jUAAAAJ ID SINTA Dosen Pembimbing: Eddy Prasetyo Nugroho: 5990993 Rizky Rachman Judhie P: 5993953
Uncontrolled Keywords: Analisis Sentimen, Berita Pemilu 2024, Naive Bayes Classifier, Text Mining, Natural Language Processing Sentiment Analysis, 2024 Election News, Naive Bayes Classifier, Text Mining, Natural Language Processing
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer
Depositing User: Muhammad Fauzan
Date Deposited: 27 Sep 2024 01:54
Last Modified: 27 Sep 2024 01:54
URI: http://repository.upi.edu/id/eprint/126377

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