TEXTBLOB-BASED SENTIMENT ANALYSIS OF TABUNGAN PERUMAHAN RAKYAT (TAPERA) POLICY: A PUBLIC PERCEPTION STUDY - Faturohman, Muhammad Iqbal; Arifin, Miftahol (Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro, 2025)
TAPERA (Tabungan Perumahan Rakyat) has generated considerable debate in Indonesia. Intended to assist low-income communities with affordable housing, the program has faced criticism due to policy inconsistencies and mandatory monthly contributions, which some argue impose significant financial burdens on participants. Concerns about the transparency and accountability of fund management further complicate its reception. This study utilizes sentiment analysis with the TextBlob library within Google Colab to evaluate public opinions on TAPERA. The research involved collecting data from YouTube comments via the API, followed by data preprocessing through six stages: Data Cleaning, Stopword Removal, Tokenization, Normalization, Stemming, and Translation. Sentiments were then analyzed using TextBlob, with the final dataset comprising 2,228 comments. The sentiment analysis revealed that 76% of the comments were negative, 23% positive, and 1% neutral. The predominance of negative sentiment indicates widespread dissatisfaction, suggesting issues with TAPERA’s effectiveness, coverage, and implementation. Negative feedback reflects broader concerns about transparency and communication. These findings highlight the need for continuous evaluation and transparent communication to enhance TAPERA’s effectiveness and address public concerns. Sentiment analysis proves to be a valuable tool for gauging public perception and guiding policy refinements, ensuring that the program aligns more closely with community needs and expectations.