IMPLEMENTATION OF MULTI-CLASS GRADIENT BOOSTING TO CLASSIFY ANIMAL SPECIES IN ZOOS

IMPLEMENTASI MULTI-CLASS GRADIENT BOOSTING UNTUK MENGKLASIFIKASIKAN JENIS HEWAN PADA KEBUN BINATANG

  • Sri Diantika Universitas Bina Sarana Informatika
  • Hiya Nalatissifa Universitas Bina Sarana Informatika
  • Riki Supriyadi Universitas Nusa Mandiri
  • Nurlaelatul Maulidah Universitas Bina Sarana Informatika
  • Ahmad Fauzi Universitas Bina Sarana Informatika
Abstract views: 181 , PDF downloads: 282
Keywords: Klasifikasi, Jenis Hewan, Multi-class, Gradient Boosting, Algortitma

Abstract

Animals are one of the living things that have various types. Grouping types of animals based on similarities and differences in characteristics owned is one of the important activities carried out To make it easier to compare, recognize, study certain types of animals and be able to find out kinship relationships between animals, So if a new type of animal is found that does not yet have a name, it will be easier for us to give a name to the animal based on the type and based on the group. In research on the classification of animal species in zoos that have multi-class, the best classification is obtained by applying gradient boosting parameters with n_estimators of 50, max_depth 3, sub-sample of 1.0, learning rate of 0.1, and using criterion friedman Mse. And by implementing Split validation or division between training data by 80% for training data and 20% for testing data. The results stated that the proposed model was better than some other models that had also been tested with an accuracy value of 93.75%, recal of 94%, precision of 96% and MSE to measure the average magnitude of error in a series of classifications of 12.5%, the smaller the MSE value, the better it would be in classifying.

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Published
2023-06-03
How to Cite
[1]
Sri Diantika, Hiya Nalatissifa, Riki Supriyadi, Nurlaelatul Maulidah, and Ahmad Fauzi, “IMPLEMENTATION OF MULTI-CLASS GRADIENT BOOSTING TO CLASSIFY ANIMAL SPECIES IN ZOOS: IMPLEMENTASI MULTI-CLASS GRADIENT BOOSTING UNTUK MENGKLASIFIKASIKAN JENIS HEWAN PADA KEBUN BINATANG”, antivirus, vol. 17, no. 1, pp. 32-40, Jun. 2023.
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Articles