DEVELOPMENT OF WEBGIS FOR VISUALIZING TOMATO LAND SUITABILITY CLASSIFICATION BASED ON RAINFALL INTERPOLATION

Abstract

Rainfall in Indonesia is often limited to tabular formats from scattered stations, hindering effective spatial analysis for agricultural planning—particularly for water-sensitive crops like tomatoes. This study aims to develop a WebGIS system to visualize rainfall distribution and analyze land suitability for tomato cultivation in West Java. The methodology involves data acquisition via web scraping using Python, spatial interpolation to convert point data into continuous raster surfaces, and the application of zonal statistics based on FAO agroclimatic criteria for a 90–120-day growing season. The system utilizes a client-side architecture with Leaflet.js and asynchronous data loading to ensure efficiency. Results reveal distinct spatial rainfall patterns and categorized land suitability classes (S1, S2, S3, and N). The developed WebGIS provides an interactive platform with dynamic filtering, serving as an effective decision-support tool for farmers and stakeholders to identify optimal planting locations and schedules.