IMPLEMENTATION OF CORN PRODUCTION EXPERIENCE 2025-2030 IN INDONESIA USING POM QM APPLICATION

Abstract

Corn is one of the agricultural products used as a staple food by the Indonesian population. Forecasting is essential to support decision-making by farmers and the government in efforts to continuously improve production. The available data will be analyzed using the POM-QM for Windows Version 5 software, employing several methods: Moving Average, Weighted Moving Average, Linear Regression, Exponential Smoothing, and Exponential Smoothing with Trend. These methods are evaluated based on three error measurements: Mean Squared Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE). The analysis results show that the most effective method for forecasting corn production is the Weighted Moving Average method, as it yields the lowest MAD, MSE, and MAPE values compared to the other methods. Therefore, the Weighted Moving Average method is used to forecast corn production in Indonesia from 2025 to 2030, helping inform management decisions for farmers, business actors, and government policymakers.

Key words: forecasting, corn production, POM QM.