An Interpretable Neuro-Fuzzy Framework for Mental Health Indicator Classification with Contextual Representation and Metaheuristic Optimization

Social media text provides valuable information for analyzing mental health-related expressions. However, contextual language variation, class imbalance, and limited model interpretability remain important challenges in text classification. This study evaluates different text representations, class-imbalance handling strategies, and metaheuristic optimization within an Adaptive Neuro-Fuzzy Inference System (ANFIS) framework. A total of 19,429 Indonesian-language posts were retained after duplicate removal and label-conflict adjudication. The dataset was partitioned using a fixed group-aware split into 13,600 training, 2,914 validation, and 2,915 test records, covering four categories: Distress, Crisis, Protective, and Situational. The experiments examined lexical-statistical, distributed-semantic, and contextual representations using Plain ANFIS, class-balanced focal ANFIS (CBF-ANFIS), and its metaheuristic-optimized variants based on the Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA). Model performance was evaluated across three random seeds and compared with a TF-IDF-based Logistic Regression baseline and direct IndoBERT fine-tuning. Direct IndoBERT achieved the highest test Macro-F1 of 0.7685 ± 0.0046, followed by preliminary Plain ANFIS (0.6942 ± 0.0011) and final CBF-ANFIS (0.6897 ± 0.0054). Although WOA and CSA improved validation Macro-F1, their mean test Macro-F1 scores were slightly lower than that of CBF-ANFIS (0.6890 and 0.6859 versus 0.6897, respectively), indicating no consistent generalization benefit. Overall, direct IndoBERT provided superior classification performance, whereas the neuro-fuzzy framework offered a structured mechanism for tracing rule activation and inference in the latent feature space. Nevertheless, these rules do not provide direct linguistic or clinical interpretations.