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Nurul Izza Afkharinah - IEEE Xplore Author Profile

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Remote sensing and machine learning (ML) technologies that have developed very rapidly allow data retrieval and processing for monitoring paddy field conditions with minimal human intervention. One of the machine learning algorithms that is widely used for paddy growth stage classification is Random Forest. This study rebuilt the Random Forest model with datasets derived from a combination of Area...Show More
Indonesia is an agricultural country with one of its main agricultural commodities being rice. One of the areas that is the center of rice cultivation in Indonesia, especially on Java Island, is the Karawang Regency. There are several methods of measuring rice production data, one of which is the ASF statistical method. Apart from using these statistical methods, there are several studies showing ...Show More
The agricultural sector has a significant contribution to the achievement of the second Sustainable Development Goals (SDGs), which are zero hunger, ensuring food security, enhancing nutrition, and fostering sustainable agriculture. This goal is in line with the regulation issued by the Indonesian Government No. 68 of 2002 regarding food security. To achieve that goal, it is necessary to have time...Show More
Paddy is an important and strategic commodity to supply food needs in Indonesia. Karawang Regency in West Java Province is well known as rice producer due to its natural resources and farming system. This study aims to estimate the distribution of paddy growth stage in Karawang Regency based on the classification results using the Gradient Boosting algorithm. The classification was carried out to ...Show More
Classification And Regression Trees (CART) is one of the classic and simple algorithm in predictive modeling machine learning. This study aims to compare the result of paddy growth stage estimates based on CART model of Sentinel-1A Synthetic Aperture Radar (SAR) data and Cropping Calendar (KATAM). The construction of the CART model utilises real data field from Area Frame Sampling (Kerangka Sampli...Show More
This study tried to estimate distribution and area of land cover by focusing on the area of the paddy growing phase using the Random Forest model on Sentinel 1A data and Area Frame Sample (Kerangka Sampling Area or KSA) observation data as reference data. Sentinel-1 data from Karawang Regency, West Java was taken monthly for a one-year period starting from January 2020 to December 2020. It was fou...Show More