Optimizing Word2Vec Dimensions for Sentiment Analysis of Photomath Reviews Using Random Forest and SVM - Dalam bentuk buku karya ilmiah
DIVA AZTY VARISSA AZIS
Informasi Umum
Kode
25.04.460
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Machine Learning
Dilihat
94 kali
Informasi Lainnya
Abstraksi
Technology in the Industrial Revolution 4.0 era supports modern learning through apps like Photomath, simplifying<br /> math problem-solving for users. However, diverse user reviews highlight the need for sentiment analysis to evaluate app quality.<br /> This research analyzes 9,059 reviews of Photomath collected from the Google Play Store using Python. Word2Vec is used in<br /> the study to compare Random Forest and Support Vector Machine (SVM) classifiers for feature extraction. To ensure clean<br /> and consistent data, preprocessing techniques such as stemming, tokenization, and stopword removal were used. Text with rich<br /> semantic aspects was mathematically represented using Word2Vec. The findings show that SVM using an RBF kernel<br /> performed better than Random Forest, with an F1-score of 88.5%, 88.5% accuracy, 88.7% precision, and 88.5% recall.<br /> Performance was effectively improved by combining 300-dimensional Word2Vec with stemming algorithms. While Random<br /> Forest achieved slightly lo
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | DIVA AZTY VARISSA AZIS |
| Jenis | Perorangan |
| Penyunting | Yuliant Sibaroni |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2025 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
