Pengembangan Microservice INA-AGRO untuk Penyajian Data Spasial Hasil Interpolasi Data Cuaca dengan Integrasi GeoServer dan Redis

Abstract

Kesesuaian lahan merupakan faktor penting dalam mendukung produktivitas komoditas hortikultura dan pangan utama di Indonesia. Penentuan kesesuaian lahan dipengaruhi oleh parameter cuaca, terutama suhu dan curah hujan. Sistem INA-Agro membutuhkan penyajian data cuaca yang mampu menampilkan variasi spasial suhu dan curah hujan secara lebih kontinu untuk mendukung analisis kesesuaian lahan. Penelitian ini bertujuan merancang dan mengimplementasikan layanan microservice untuk proses interpolasi spasial data cuaca dan mengintegrasikan GeoServer dan Redis dalam penyajian hasil interpolasi. Data cuaca diperoleh dari Visual Crossing Weather API, kemudian diproses menjadi akumulasi curah hujan 30 hari dan rata-rata suhu 30 hari. Data tersebut diinterpolasi menggunakan metode Inverse Distance Weighting (IDW) dan disajikan dalam bentuk raster GeoTIFF beresolusi 500 m × 500 m. Sistem dikembangkan melalui beberapa layanan terpisah, meliputi pembangkitan titik, pengambilan data cuaca, interpolasi, publikasi layer, dan caching tile peta. Hasil pengujian menunjukkan bahwa seluruh fungsi utama berjalan sesuai skenario pengujian. Pada pengujian performa multi-layer, skenario distribusi beban merata menghasilkan waktu respons sekitar 2,8 detik, sedangkan distribusi beban tidak merata mencapai sekitar 0,5 detik setelah cache terbentuk. Integrasi GeoServer, MapProxy, dan Redis mampu mendukung penyajian peta hasil interpolasi secara lebih efisien dan modular.

Land suitability is an important factor in supporting the productivity of major horticultural and food commodities in Indonesia. The determination of land suitability is influenced by weather parameters, particularly temperature and rainfall. The INA-Agro system requires weather data visualization capable of representing the spatial variation of temperature and rainfall more continuously to support land suitability analysis. This study aims to design and implement microservices for the spatial interpolation of weather data and to integrate GeoServer and Redis for presenting the interpolation results. Weather data were obtained from the Visual Crossing Weather API and processed into 30-day cumulative rainfall and 30-day average temperature. The data were interpolated using the Inverse Distance Weighting (IDW) method and presented as GeoTIFF rasters with a spatial resolution of 500 m × 500 m. The system was developed as several separate services, including point generation, weather data retrieval, interpolation, layer publication, and map tile caching. The test results showed that all major system functions operated successfully according to the specified test scenarios. In the multi-layer performance test, the evenly distributed load scenario produced a response time of approximately 2.8 seconds, whereas the unevenly distributed load scenario achieved approximately 0.5 seconds after the cache had been established. The integration of GeoServer, MapProxy, and Redis supported a more efficient and modular presentation of interpolated maps.