FORECASTING CLIMATE CHANGE PATTERNS TO IMPROVING RICE HARVEST USING SVR FOR ACHIEVING GREEN ECONOMY

CARLES JULIANDY and KELVIN KELVIN and APRIYANTO HALIM and SIO JURNALIS PIPIN and FRANS MIKAEL SINAGA and WULAN SRI LESTARI (2025) FORECASTING CLIMATE CHANGE PATTERNS TO IMPROVING RICE HARVEST USING SVR FOR ACHIEVING GREEN ECONOMY. INDONESIAN JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING (IJAIDM), 8 (1). pp. 1-10. ISSN 2614-6150

Full text not available from this repository.

Abstract

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy.

Item Type: Article
Divisions: Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) > Artikel > Fakultas Informatika
Depositing User: Merpita Saragih
Date Deposited: 20 Mar 2025 09:20
Last Modified: 20 Mar 2025 09:20
URI: https://repository.mikroskil.ac.id/id/eprint/3905

Actions (login required)

View Item
View Item