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Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts

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@article{IJASEIT14582,
   author = {Inggit Puji Astuti and Angga Yudaputra and Dipta Sumeru Rinandio and Ade Yusuf Yuswandi},
   title = {Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {6},
   year = {2021},
   pages = {2328--2333},
   keywords = {Biogeographical distribution; Capparis micracantha; Ex-situ conservation; GLM; SVM.},
   abstract = {Capparis micracantha is a flowering plant species with a wide range of distributions. It is often used as traditional medicine by local people. This species is often overlooked due to very limited information that reveals about its conservation aspects. This study aims to update the distribution records and predict the potential current distribution of this species. Some new occurrence records were obtained through a direct field survey, plant inventory database, and reliable scientific papers. In order to gain more information about current distribution, Species Distribution Modelling (SDM) was applied to predict the potential current distribution of this species in Indonesia.  Two algorithms of machine learning (SVM and GLM) were applied to produce predictive distribution maps. The models were built from occurrence records data and environmental variables (climate and topography). Area Under Curve (AUC) was used to evaluate the model prediction. The AUC value of those models >0.80 means those models have a good performance. The AUC value of those models is stated in SVM (0.86) and GLM (0.824). SVM and GLM have almost similar resulting predictive maps of current distribution. The predictive maps would be useful to give information about the regions in Indonesia that have similar environment characteristics and climate conditions where the species are observably present. This species has also been conserved through ex-situ conservation strategies, but the seedling growth of this species still remains a challenge.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14582},
   doi = {10.18517/ijaseit.11.6.14582}
}

EndNote

%A Astuti, Inggit Puji
%A Yudaputra, Angga
%A Rinandio, Dipta Sumeru
%A Yuswandi, Ade Yusuf
%D 2021
%T Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts
%B 2021
%9 Biogeographical distribution; Capparis micracantha; Ex-situ conservation; GLM; SVM.
%! Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts
%K Biogeographical distribution; Capparis micracantha; Ex-situ conservation; GLM; SVM.
%X Capparis micracantha is a flowering plant species with a wide range of distributions. It is often used as traditional medicine by local people. This species is often overlooked due to very limited information that reveals about its conservation aspects. This study aims to update the distribution records and predict the potential current distribution of this species. Some new occurrence records were obtained through a direct field survey, plant inventory database, and reliable scientific papers. In order to gain more information about current distribution, Species Distribution Modelling (SDM) was applied to predict the potential current distribution of this species in Indonesia.  Two algorithms of machine learning (SVM and GLM) were applied to produce predictive distribution maps. The models were built from occurrence records data and environmental variables (climate and topography). Area Under Curve (AUC) was used to evaluate the model prediction. The AUC value of those models >0.80 means those models have a good performance. The AUC value of those models is stated in SVM (0.86) and GLM (0.824). SVM and GLM have almost similar resulting predictive maps of current distribution. The predictive maps would be useful to give information about the regions in Indonesia that have similar environment characteristics and climate conditions where the species are observably present. This species has also been conserved through ex-situ conservation strategies, but the seedling growth of this species still remains a challenge.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14582
%R doi:10.18517/ijaseit.11.6.14582
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 6
%@ 2088-5334

IEEE

Inggit Puji Astuti,Angga Yudaputra,Dipta Sumeru Rinandio and Ade Yusuf Yuswandi,"Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 6, pp. 2328-2333, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.6.14582.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Astuti, Inggit Puji
AU  - Yudaputra, Angga
AU  - Rinandio, Dipta Sumeru
AU  - Yuswandi, Ade Yusuf
PY  - 2021
TI  - Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 6
Y2  - 2021
SP  - 2328
EP  - 2333
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Biogeographical distribution; Capparis micracantha; Ex-situ conservation; GLM; SVM.
N2  - Capparis micracantha is a flowering plant species with a wide range of distributions. It is often used as traditional medicine by local people. This species is often overlooked due to very limited information that reveals about its conservation aspects. This study aims to update the distribution records and predict the potential current distribution of this species. Some new occurrence records were obtained through a direct field survey, plant inventory database, and reliable scientific papers. In order to gain more information about current distribution, Species Distribution Modelling (SDM) was applied to predict the potential current distribution of this species in Indonesia.  Two algorithms of machine learning (SVM and GLM) were applied to produce predictive distribution maps. The models were built from occurrence records data and environmental variables (climate and topography). Area Under Curve (AUC) was used to evaluate the model prediction. The AUC value of those models >0.80 means those models have a good performance. The AUC value of those models is stated in SVM (0.86) and GLM (0.824). SVM and GLM have almost similar resulting predictive maps of current distribution. The predictive maps would be useful to give information about the regions in Indonesia that have similar environment characteristics and climate conditions where the species are observably present. This species has also been conserved through ex-situ conservation strategies, but the seedling growth of this species still remains a challenge.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14582
DO  - 10.18517/ijaseit.11.6.14582

RefWorks

RT Journal Article
ID 14582
A1 Astuti, Inggit Puji
A1 Yudaputra, Angga
A1 Rinandio, Dipta Sumeru
A1 Yuswandi, Ade Yusuf
T1 Biogeographical Distribution Model of Flowering Plant Capparis micracantha Using Support Vector Machine (SVM) and Generalized Linear Model (GLM) and its Ex-situ Conservation Efforts
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 6
YR 2021
SP 2328
OP 2333
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Biogeographical distribution; Capparis micracantha; Ex-situ conservation; GLM; SVM.
AB Capparis micracantha is a flowering plant species with a wide range of distributions. It is often used as traditional medicine by local people. This species is often overlooked due to very limited information that reveals about its conservation aspects. This study aims to update the distribution records and predict the potential current distribution of this species. Some new occurrence records were obtained through a direct field survey, plant inventory database, and reliable scientific papers. In order to gain more information about current distribution, Species Distribution Modelling (SDM) was applied to predict the potential current distribution of this species in Indonesia.  Two algorithms of machine learning (SVM and GLM) were applied to produce predictive distribution maps. The models were built from occurrence records data and environmental variables (climate and topography). Area Under Curve (AUC) was used to evaluate the model prediction. The AUC value of those models >0.80 means those models have a good performance. The AUC value of those models is stated in SVM (0.86) and GLM (0.824). SVM and GLM have almost similar resulting predictive maps of current distribution. The predictive maps would be useful to give information about the regions in Indonesia that have similar environment characteristics and climate conditions where the species are observably present. This species has also been conserved through ex-situ conservation strategies, but the seedling growth of this species still remains a challenge.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14582
DO  - 10.18517/ijaseit.11.6.14582