JAENUDDIN, NURUL AENI (2025) PERBANDINGAN METODE NAIVE BAYES DAN LOGISTIC REGRESSION DALAM ANALISIS SENTIMEN MASYARAKAT TERHADAP BOIKOT RESTORAN MCDONALD'S. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This study aims to analyze public sentiment toward the boycott of McDonald's restaurants by comparing the Naive Bayes and Logistic Regression models. Confusion matrix analysis and model performance evaluation show that the Naive Bayes model outperforms the Logistic Regression model significantly. The Naive Bayes model achieved an accuracy of 96.66% with precision, recall, and F1 scores in the range of 96-97% for both positive and negative classes. In contrast, the Logistic Regression model only achieved an accuracy of 77.88% with lower performance metrics. Additionally, the Naive Bayes model exhibited stable and balanced performance distribution across both classes, whereas the Logistic Regression model, although consistent, still lagged behind. The results suggest that the dataset's characteristics, with its clear and well-defined feature distribution, align well with the independence assumption of the Naive Bayes model. This allows Naive Bayes to effectively leverage the dataset's properties, resulting in superior performance compared to Logistic Regression. Keywords: Sentiment Analysis, Naive Bayes, Logistic Regression, McDonald's Boycott Penelitian ini bertujuan untuk menganalisis sentimen masyarakat terhadap boikot restoran McDonald's dengan membandingkan model Naive Bayes dan Logistic Regression. Analisis matriks kebingungan dan evaluasi performa model menunjukkan bahwa model Naive Bayes mengungguli model Logistic Regression secara signifikan. Model Naive Bayes mencapai akurasi 96,66% dengan presisi, recall, dan skor F1 dalam kisaran 96-97% untuk kedua kelas, positif dan negatif. Sebaliknya, model Logistic Regression hanya mencapai akurasi 77,88% dengan metrik performa yang lebih rendah. Selain itu, model Naive Bayes menunjukkan distribusi performa yang stabil dan seimbang di kedua kelas, sementara model Logistic Regression, meskipun konsisten, masih tertinggal jauh. Hasil penelitian menunjukkan bahwa karakteristik dataset, dengan distribusi fitur yang jelas dan terdefinisi dengan baik, sangat cocok dengan asumsi independensi fitur pada model Naive Bayes. Hal ini memungkinkan Naive Bayes untuk memanfaatkan sifat-sifat dataset secara efektif, sehingga menghasilkan performa yang unggul dibandingkan Logistic Regression. Kata Kunci: Analisis Sentimen, Boikot McDonald's, Naive Bayes, Logistic Regression
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 25 072 |
| NIM/NIDN Creators: | 41521120052 |
| Uncontrolled Keywords: | Analisis Sentimen, Boikot McDonald's, Naive Bayes, Logistic Regression |
| Subjects: | 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 05 Jun 2025 08:51 |
| Last Modified: | 05 Jun 2025 08:51 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/95732 |
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