Sentiment Analysis on Tabungan Perumahan Rakyat (TAPERA) Program by using Support Vector Machine (SVM)

Abstract

This study aims to analyze public sentiment towards the Housing Savings Program (TAPERA) using the Support Vector Machine (SVM) algorithm. The dataset comprises 16,061 reviews about TAPERA which was gathered from web scrapping and YouTube API. The sentiment analysis results indicate that 99.8% of the reviews are negative, while only 0.2% are positive. The SVM model applied in this study achieved a very high accuracy rate of 99.81%. This indicates that the model is highly effective in classifying sentiments, particularly in identifying negative sentiments. The resulting confusion matrix shows the model's excellent performance in detecting negative sentiments, with no False Positives (FP) and a very high number of True Negatives (TN). However, the model exhibits weaknesses in detecting positive sentiments, as indicated by the presence of several False Negatives (FN) and the absence of True Positives (TP). The findings of this study suggest that the public generally holds a very negative view of the TAPERA program. This insight is crucial for program administrators to consider as they evaluate and improve the program based on negative feedback received from the public. Overall, this research provides important insights into public perceptions of TAPERA and underscores the need for better modeling for more representative sentiment analysis. These findings can serve as a basis for policymakers in designing more effective communication strategies and program improvements to increase public acceptance of TAPERA.

Keywords

• sentiment analysis
• support vector machine (svm)
• tapera