ANALISIS SENTIMEN BERBASIS KNN DAN HYBRID MACHINE LEARNING UNTUK MENGEVALUASI OPINI MASYARAKAT LAMONGAN TERHADAP PROGRAM MBG - Munif Munif; Mustain Mustain; Rifki Rifki (Jurnal Informatika Teknologi dan Sains (Jinteks), 2025)
Nutritional problems and food insecurity remain critical challenges in Indonesia, especially in underprivileged areas. To address this, the Lamongan City Government launched the Free Nutritious Meal (MBG) program as a public welfare initiative. This study aims to evaluate public opinion on the MBG program using sentiment analysis based on a hybrid machine learning model combining K-Nearest Neighbor (KNN) and Naive Bayes algorithms. A total of 2,261 public comments were collected from social media, online surveys, and interviews. The data underwent preprocessing, feature extraction using TF-IDF, and dual-stage classification—first by topic (Menu, Impact, Schedule, Others) using Naive Bayes, then sentiment classification (positive, negative, neutral) using KNN. Evaluation metrics including accuracy, precision, recall, and F1-Score were applied. Results show that neutral sentiment was the most dominant (41.28%), followed by positive (32.01%) and negative (26.49%). The model achieved an overall accuracy of 87%, with the highest F1-Score of 0.91 in the positive sentiment category. These results demonstrate that the hybrid model effectively captures community perceptions and can support data-driven evaluation of local social programs.