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How to cite (IEEE):
A. Rudiastuti, Y. Lumban-Gaol, F. Silalahi, Y. Prihanto, & W. Pranowo
"Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia," JOIV : International Journal on Informatics Visualization, vol. 6, no. 1, , pp. 74-82, Mar. 2022.
https://doi.org/10.30630/joiv.6.1.873
How to cite (APA):
Rudiastuti, A., Lumban-Gaol, Y., Silalahi, F., Prihanto, Y., & Pranowo, W.
(2022).
Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia.
JOIV : International Journal on Informatics Visualization, 6(1), 74-82.
https://doi.org/10.30630/joiv.6.1.873
How to cite (Chicago):
Rudiastuti, Aninda W., Lumban-Gaol, Yustisi, Silalahi, Florence E. S., Prihanto, Yosef, AND Pranowo, Widodo S..
"Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia" JOIV : International Journal on Informatics Visualization [Online], Volume 6 Number 1 (31 March 2022)
https://doi.org/10.30630/joiv.6.1.873
How to cite (Vancouver):
Rudiastuti A, Lumban-Gaol Y, Silalahi F, Prihanto Y, & Pranowo W .
Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia.
JOIV : International Journal on Informatics Visualization [Online]. 2022 Mar;
6(1):74-82.
https://doi.org/10.30630/joiv.6.1.873
How to cite (Harvard):
Rudiastuti, A., Lumban-Gaol, Y., Silalahi, F., Prihanto, Y., & Pranowo, W.
,2022.
Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia.
JOIV : International Journal on Informatics Visualization, [Online] 6(1), pp. 74-82.
https://doi.org/10.30630/joiv.6.1.873
How to cite (MLA8):
Rudiastuti, Aninda W., Yustisi Lumban-Gaol, Florence E. S. Silalahi, Yosef Prihanto, & Widodo S. Pranowo.
"Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia." JOIV : International Journal on Informatics Visualization [Online], 6.1 (2022): 74-82. Web. 3 Oct. 2023
, https://doi.org/10.30630/joiv.6.1.873
BibTex Citation Data :
@article{JOIV873,
author = {Aninda W. Rudiastuti and Yustisi Lumban-Gaol and Florence E. S. Silalahi and Yosef Prihanto and Widodo S. Pranowo},
title = {Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia},
journal = {JOIV : International Journal on Informatics Visualization},
volume = {6},
number = {1},
year = {2022},
keywords = {Random forest; machine learning; Google Earth Engine (GEE); settlement; sentinel; Land Use/Land Cover (LULC).},
abstract = {Measuring the status of achievement of the SDGs is the task and concern of many countries in the world, including Indonesia. Indicators for achieving the SDGs enclose three main pillars, namely environmental, economic, and social. The updated land use/land cover information is needed for environmental pillars. One imperative land cover information is built-up land, which acts as a detector for expanding urban areas and measuring SDGs' target indicators. Indonesia's cultural diversity affects the distribution pattern of built-up land, especially settlements. This is a challenge in the up-to-date and rapid mapping of built-up land. This research aims to analyze the ability and transferability of the Random Forest model for built-up areas and settlements using Google Earth Engine (GEE) in Banyumas, Cilacap, and Tegal. Around 19 predictors from multi-sources satellites are integrated to identify four land cover classes. Discussion on predictor composition to improve model accuracy also carried on. The results showed that the algorithm separated four land cover classes, with the highest accuracy for separating water bodies and other classes (vegetation and open land), OA above 90%. Machine confusion regarding the separation between housing classes and other buildings was still found (F1 score 0.67 - 0.69). Applying the model to the other two areas resulted in a similar statistical trend to the trained model. However, the classification method developed in this paper can assist in the rapid description of land cover if up-to-date data from official sources are not available.},
issn = {2549-9904}, pages = {74--82}, doi = {10.30630/joiv.6.1.873},
url = {http://joiv.org/index.php/joiv/article/view/873}
}
Refworks Citation Data :
@article{{JOIV}{873}, author = {Rudiastuti, A., Lumban-Gaol, Y., Silalahi, F., Prihanto, Y., Pranowo, W.}, title = {Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {1}, year = {2022}, doi = {10.30630/joiv.6.1.873}, url = {} }Refbacks
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JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.