Comparative Study of Fuzzy Inference System and Adaptive Neuro-Fuzzy Inference System in Public Sentiment Analysis of Kabinet Merah Putih

Abstract

Purpose: This study aims to compare two fuzzy logic-based approaches, namely the Fuzzy Inference System (FIS) and the Adaptive Neuro-Fuzzy Inference System (ANFIS), in analyzing public sentiment toward the Kabinet Merah Putih.

Methods: A dataset of 1,197 tweets was collected from Twitter (X) between October 2024 and April 2025 using specific keywords. After preprocessing and polarity measurement with TextBlob, the sentiment values were mapped into seven categories: strongly negative, negative, weakly negative, neutral, weakly positive, positive, and strongly positive. The classification was performed using both FIS and ANFIS. Evaluation metrics included accuracy, precision, recall, F1-score, and error rate (MSE and RMSE).

Result: Experimental results show that FIS achieved an overall accuracy of 79.2%, performing well on majority classes but failing to identify several minority classes. In contrast, ANFIS obtained an accuracy of 92.5% with very low error (MSE = 0.0341, RMSE = 0.1848), demonstrating strong capability in classifying majority and several minority categories. Overall, ANFIS outperformed FIS, proving more effective in capturing sentiment patterns and aligning with the actual distribution of public opinion..

Novelty: This study offers novelty by explicitly comparing the performance of FIS and ANFIS in multi-level sentiment analysis of Indonesian social media data, an approach that has not been explored in prior research.