Implementation of Moving Average and Weighted Moving Average for Forecasting Palm Oil Harvest and Income in a Web-Based GIS System

Elvia Andriyani; Bambang Agus Herlambang; Khoiriya Lathifa

doi:10.52436/1.jutif.2026.7.3.5754

Authors

Elvia Andriyani Informatics, Faculty of engineering and informatics, Universitas PGRI Semarang, Indonesia
Bambang Agus Herlambang Informatics, Faculty of engineering and informatics, Universitas PGRI Semarang, Indonesia
Khoiriya Lathifa Informatics, Faculty of engineering and informatics, Universitas PGRI Semarang, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.3.5754

Keywords:

Palm Oil, Forecasting, Weighted Moving Average, GIS, Web-based System

Abstract

Independent palm oil farmers face significant challenges in financial management due to inefficient manual recording, fluctuating harvest yields, and volatile Fresh Fruit Bunch (FFB) prices. This study aims to develop a web-based harvest and income recording system integrated with a Geographic Information System (GIS) and forecasting methods to support decision-making. The system is developed using a Research and Development (R&D) approach by comparing Moving Average and a dynamically weighted Moving Average that adapts to price fluctuations for predicting future net income. Model performance is evaluated using Mean Absolute Percentage Error (MAPE) and validated with the Diebold–Mariano test, while system usability is assessed through User Acceptance Testing (UAT). The results show that the dynamically weighted Moving Average achieves a prediction accuracy of 93.08% (MAPE 6.92%), slightly outperforming the standard Moving Average (93.03%), although no statistically significant difference is found based on the Diebold–Mariano test. The system also obtains a “Very Good” usability rating with a UAT score of 95.11%. These findings demonstrate that the proposed approach provides a practical and adaptive forecasting mechanism integrated within a spatial financial management system, contributing to improved decision support and offering methodological value in time-series forecasting for agricultural informatics.

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