Skripsi

KOMPARASI KINERJA MODEL NAIVE BAYES, SVM, DAN RANDOM FOREST DALAM KLASIFIKASI SENTIMEN ULASAN PADA APLIKASI GOOGLE GEMINI

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The increasing number of reviews for the Google Gemini app on Google Play Store reflects a variety of user opinions regarding the performance of this AI-based application. To identify sentiment patterns, this study conducted a comparative study of three classification algorithms: Support Vector Machine (SVM), Naive Bayes, and Random Forest, using 14,479 raw reviews collected through scraping. These reviews then underwent several pre-processing steps, including case folding, text cleaning, tokenization, normalization, stopword removal, and stemming. After being labeled based on ratings, the dataset formed a highly imbalanced class distribution, consisting of 11,252 positive reviews and 1,571 negative reviews, and was then divided using the Hold-Out method with a training ratio of 80% and a testing ratio of 20%. The evaluation showed that SVM achieved the best performance, yielding an accuracy of 91%, precision of 93%, recall of 97%, and an F1-score of 95%, outperforming Random Forest and Naïve Bayes, which achieved an accuracy of 90% each. Overall, these results highlight SVM as the most effective algorithm for classifying sentiment in Google Gemini reviews, while the dominance of positive feedback indicates a relatively high level of user satisfaction, although model performance on the minority (negative) class remains a challenge due to data imbalance.

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Detail Information

Series Title

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Call Number

T1945642026

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xi, 29 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Informasi
Pembelajaran Mesin

Specific Detail Info

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Statement of Responsibility

TUTI

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