Analysis of Sentiment the Issue of Fuel Increase: based on Twitter using the Random Forest Classifier, Naïve Bayes, and Support Vector Machine Methods
MUHAMAD RIKBAL IKHSANI
Informasi Umum
Kode
23.04.2727
Klasifikasi
006.31 - Machine Learning
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Machine - Learning, Fuel Systems,
Dilihat
263 kali
Informasi Lainnya
Abstraksi
<p><strong>Sentiment analysis is a technique used to analyze the subjectivity of opinions expressed in the text. In this research, we evaluate sentiment classification methods for analyzing public opinion about fuel inflation on Twitter, including Naive Bayes, Support Vector Machine (SVM), and Random Forest. Our results show that the SVM and Random Forest methods produced the highest accuracy rates of 78%, while Naive Bayes achieved an accuracy rate of 70%. Based on these findings, the SVM and Random Forest methods are good choices for sentiment analysis on public opinion about fuel price increases on Twitter.</strong></p>
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | MUHAMAD RIKBAL IKHSANI |
| Jenis | Perorangan |
| Penyunting | Bambang Ari Wahyudi, Irma Palupi |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika (International Class) |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
