Analisis Sentimen Berbasis Aspek Program Koperasi Desa Merah Putih Menggunakan IndoBERT - gea, yuris mardayani; Berutu, Sunneng Sandino; Jatmika, Jatmika (Algoritme Jurnal Mahasiswa Teknik Informatika, 2026)
The Koperasi Desa Merah Putih program is a strategic initiative requiring evaluation through public perception monitoring. This study employs Aspect-Based Sentiment Analysis (ABSA) using the IndoBERT transformer model via a two-stage approach: aspect-opinion extraction using BIO labeling (Token Classification) and sentiment polarity determination (Sequence Classification). A dataset of 12,013 entries from Platform X underwent systematic preprocessing and was trained using an 80:20 stratified split to ensure label balance. Model performance, evaluated through accuracy, precision, recall, and F1-score, demonstrated high reliability with 79% accuracy. Collectively, the analysis identified 4,917 neutral, 3,961 negative, and 3,135 positive opinions. Specifically, the "Economy" aspect recorded 1,673 positive opinions, reflecting public optimism regarding the program's economic impact. These results confirm that Deep Learning-based approaches provide granular insights into policy effectiveness, serving as an accurate decision-support instrument for cooperative program managers at the village level to improve policy implementation based on data-driven evidence.