Analysis of SVM Algorithm And SMOTE Technique on Public Sentiment With Google Maps Reviews Towards The Quality of Education at LP3I Across Indonesia

Maulian Saputra; Muhammad Iqbal; Khairul

Authors

Maulian Saputra Universitas Pembangunan Panca Budi
Muhammad Iqbal
Khairul

Keywords:

Sentiment Analysis, Support Vector Machine, SMOTE, Google Maps Reviews, LP3I Education

Abstract

This study analyzes public sentiment toward LP3I’s education quality in Indonesia using the Support Vector Machine (SVM) algorithm and the Synthetic Minority Over-sampling Technique (SMOTE). Reviews from Google Maps were collected, preprocessed, translated, and labeled using the Natural Language Toolkit (NLTK). The classification model was tested with and without SMOTE to address class imbalance. The results show that applying SMOTE improves the model’s ability to detect neutral and negative sentiments, increasing accuracy from 86.93% to 88.44% and significantly enhancing recall for minority classes. However, while SMOTE helps create a more balanced classification, it also introduces a potential risk of overfitting due to synthetic data generation. Overall, the integration of SVM and SMOTE improves sentiment classification performance, providing valuable insights for LP3I to enhance its academic and administrative services based on public perception.

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