Penerapan Retrieval-Augmented Generation, Analisis Pengaduan Berbasis LLM, dan Peramalan Indikator Sosial Ekonomi pada Portal BAPPEDA Kota Pariaman

The BAPPEDA portal of Pariaman City previously served planning documents only as downloadable files, stored public complaints without processing, and offered no socioeconomic indicator data. During an internship at the Communication and Informatics Office of Pariaman City (1–31 July 2026), three artificial intelligence models were integrated into the portal through incremental development. First, a Retrieval-Augmented Generation (RAG) question-answering model combines gemini-embedding-001 vectors, cosine similarity, a similarity threshold, and Gemini 2.5 Flash with schema-constrained JSON output. Second, a zero-shot Large Language Model (LLM) analyses each complaint in the background and recommends category fit, sentiment, and urgency without sending the complainant’s identity. Third, linear regression, ARIMA, and Prophet forecast the Human Development Index (HDI), poverty rate, open unemployment rate, and Gini ratio of Pariaman City; the best method per indicator is selected by backtesting, with linear regression as a fallback. An evaluation with 40 test questions moved the threshold from 0.65 to 0.74, which gave the highest balanced accuracy (62.9%) and raised out-of-scope rejection from 14.3% to 64.3%, while recall@5 fell from 92.3% to 61.5%. Backtesting showed that linear regression was most accurate for HDI (MAPE 0.44%), whereas ARIMA was best for the other three indicators (MAPE 3.80–8.19%). All 18 automated feature tests passed. The models turn the portal into an interactive service that supports information retrieval, complaint prioritisation, and development planning.