Sentiment analysis on movie reviews using Information gain and K-nearest neighbor
NOVELTY OCTAVIANI FAOMASI DAELI
Informasi Umum
Kode
19.04.1534
Klasifikasi
C -
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Academic Status
Dilihat
383 kali
Informasi Lainnya
Abstraksi
The huge resources need effectiveness and efficiency, it can be processed by machine learning. There have been many studies conducted using machine learning method and produced quite good performance in sentiment analysis. Some machine learning methods that are often used in general are Naive bayes (NB), K-nearest neighbor (KNN), Support vector machine (SVM), and Random forest methods. Mostly, KNN did not achieve better performance than other machine learning methods in sentiment analysis. In this study, the Polarity v2.0 from Cornell movie review dataset will be used to test KNN with Information gain features selection in order to achieve good performance. The purpose of this research are to nd the optimum K for KNN and compare KNN with other methods. KNN with the help of Information gain feature selection becomes the best performance method with 96.8% accuracy compared to the NB, SVM, and Random forest while the optimum K is 3.
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Koleksi & Sirkulasi
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Pengarang
| Nama | NOVELTY OCTAVIANI FAOMASI DAELI |
| Jenis | Perorangan |
| Penyunting | ADIWIJAYA |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom |
| Kota | Bandung |
| Tahun | 2019 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
