Prediksi Harga Bawang Merah dengan Regresi Linear Berbasis Website di Ponorogo - farisi, salman al; Buntoro, Ghulam Asrofi; Prasetyo, Angga (Universitas Multi Data Palembang, 2025)
Predicting the price of shallots in Ponorogo Regency is crucial due to significant price fluctuations that impact farmers, traders, and consumers. This study aims to implement the Simple Linear Regression method to forecast shallot prices through a web-based system, assisting stakeholders in decision-making. Price data was obtained from the Basic Commodity Availability and Price Development Information System (SISKAPERBABO) for the period June 2024–May 2025. The analysis resulted in the regression equation Y = 20,466 + 1,393X, where Y represents the predicted price and X is the time variable. Accuracy evaluation using the Mean Absolute Percentage Error (MAPE) yielded a value of 21.7%, indicating a reasonably accurate prediction. User Acceptance Testing (UAT) scored 88.2%, demonstrating strong user approval. The website was developed using PHP, Laravel, and MySQL, featuring monthly price predictions and data visualization. This research is expected to serve as an effective price prediction tool for shallot farmers and traders in Ponorogo. Future improvements may include enhancing prediction models with machine learning algorithms and expanding to mobile platforms for broader accessibility.