SUHERMANJA, ARWAA ALTHIFAL (2024) ANALISIS SENTIMEN KEPUASAN PELAYANAN SEABANK PADA GOOGLE PLAYSTORE MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN RANDOM FOREST. S1 thesis, Universitas Mercu Buana-Menteng.
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Abstract
Studi ini bertujuan untuk menganalisis sentimen kepuasan terhadap pelayanan aplikasi SeaBank di Google Play Store menggunakan dua algoritma klasifikasi, yaitu Naive Bayes dan Random Forest. Evaluasi dilakukan dengan membagi data ulasan dalam rasio 70:30, 80:20, dan 90:10 untuk pelatihan dan pengujian. Hasil menunjukkan bahwa kedua algoritma memberikan kinerja yang baik, dengan Naive Bayes menunjukkan akurasi yang konsisten tinggi di berbagai rasio pembagian data. Namun, Random Forest menunjukkan performa yang lebih seimbang dan konsisten di berbagai rasio, serta mampu menangkap pola dalam data dengan lebih baik. Dengan demikian, untuk tugas analisis sentimen ini, Random Forest menjadi pilihan yang lebih unggul karena mampu menghasilkan prediksi yang lebih akurat dan seimbang, terutama pada rasio pembagian data yang lebih tinggi. This study aims to analyze the sentiment of satisfaction towards the SeaBank application service on the Google Play Store using two classification algorithms, namely Naive Bayes and Random Forest. Evaluation was conducted by dividing review data into ratios of 70:30, 80:20, and 90:10 for training and testing. The results show that both algorithms perform well, with Naive Bayes demonstrating consistently high accuracy across various data distribution ratios. However, Random Forest exhibits more balanced and consistent performance across different ratios, and is able to capture patterns in the data more effectively. Thus, for this sentiment analysis task, Random Forest emerges as the superior choice due to its ability to generate more accurate and balanced predictions, especially in higher data distribution ratios.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41520010196 |
| Uncontrolled Keywords: | Analisis sentimen, SeaBank, Naive Bayes, Random Forest Sentiment analysis, SeaBank, Naive Bayes, Random Forest |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | NAYLA AURA RAYANI |
| Date Deposited: | 29 Jun 2024 03:33 |
| Last Modified: | 29 Jun 2024 03:33 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/89279 |
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