Please use this identifier to cite or link to this item:
https://repositori.uma.ac.id/handle/123456789/26435
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DC Field | Value | Language |
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dc.contributor.advisor | Muliono, Rizki | - |
dc.contributor.author | Fikri, Ridho Ahmad | - |
dc.date.accessioned | 2025-01-24T06:19:12Z | - |
dc.date.available | 2025-01-24T06:19:12Z | - |
dc.date.issued | 2024-10 | - |
dc.identifier.uri | https://repositori.uma.ac.id/handle/123456789/26435 | - |
dc.description | 14 Halaman | en_US |
dc.description.abstract | Media sosial telah memberikan pengaruh besar terhadap kehidupan masyarakat modern, menjadi platform utama untuk berbagi informasi dan pendapat. Salah satu fenomena yang menarik perhatian adalah kasus viral seorang polisi wanita, Putri Cikita, yang mendapat julukan "Duta Sopan Santun" karena aksinya dalam sebuah video. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap kasus tersebut di media sosial Twitter menggunakan metode Naive Bayes Classifier (NBC). Metode penelitian ini adalah kuantitatif deskriptif dengan analisis sentimen berbasis Text Mining menggunakan Python dan Google Colab. Data yang digunakan adalah 2000 tweet berbahasa Indonesia yang diambil dari Agustus hingga November 2024 dengan kata kunci "Duta Sopan Santun" dan "Putri Cikita". Tahapan penelitian mencakup pengumpulan data, praproses data (case folding, tokenizing, filtering, stemming), dan pelabelan sentimen dengan kelas positif, negatif, dan netral. Hasil analisis menunjukkan 11,55% tweet bersentimen positif, 68,40% netral, dan 20,05% negatif. Metode Naive Bayes terbukti efektif dalam mengklasifikasikan sentimen data teks. Penelitian ini memberikan wawasan tentang persepsi masyarakat terhadap peristiwa viral dan menyarankan pentingnya pengelolaan citra publik di era digital. Social media has had a significant impact on modern society, serving as a primary platform for sharing information and opinions. One intriguing phenomenon is the viral case of a female police officer, Putri Cikita, who earned the title "Ambassador of Courtesy" due to her actions in a video. This study aims to analyze public sentimentregarding this case on Twitter using the Naive Bayes Classifier (NBC) method. The research adopts a quantitative descriptive approach with sentiment analysis based on Text Mining, utilizing Python and Google Colab. The dataset consists of 2,000 Indonesian-language tweets collected from August to November 2024 using the keywords "Ambassador of Courtesy" and "Putri Cikita." The research stages include data collection, data preprocessing (case folding, tokenizing, filtering, stemming), and sentiment labeling into positive, negative, and neutral classes. The analysis results reveal that 11.55% of tweets express positive sentiment, 68.40% are neutral, and 20.05% are negative. The Naive Bayes method proves effective in classifying textual sentiment data. This research provides insights into public perceptions of viral events and underscores the importance of public image management in the digital era. | en_US |
dc.language.iso | id | en_US |
dc.publisher | Universitas Medan Area | en_US |
dc.relation.ispartofseries | NPM;178160096 | - |
dc.subject | analisis sentimen | en_US |
dc.subject | media sosial | en_US |
dc.subject | Naive Bayes Classifier | en_US |
dc.subject | Duta Sopan Santun | en_US |
dc.subject | text Mining | en_US |
dc.title | Sentimen terhadap Duta Sopan Santun di Media Sosial: Studi Kasus dengan Metode Naive Bayes | en_US |
dc.title.alternative | Sentiment towards Social Media Politeness Ambassadors: A Case Study using the Naive Bayes Method | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | SP - Informatic Engineering |
Files in This Item:
File | Description | Size | Format | |
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178160096 - Ridho Ahmad Fikri Fulltext.pdf | Cover, Abstract, Chapter I, II, III, IV & V, Bibliography | 1.31 MB | Adobe PDF | View/Open |
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