Application of Support Vector Machine (SVM) Algorithm in Classification of Low-Cape Communities in Lampung Timur


  • Ahmad Ari Aldino * Mail Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Alvin Saputra Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Andi Nurkholis Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Setiawansyah Setiawansyah Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • (*) Corresponding Author
Keywords: Classification; Support Vector Machine (SVM); Linear Kernel

Abstract

Classification is a technique for grouping and categorizing specific standards as material for compiling information, making conclusions, or making decisions. This paper discusses data classification for underprivileged communities in Tanjung Inten, Purbolinggo, East Lampung using the Support Vector Machine (SVM) algorithm, then grouped into two label classes, namely the less fortunate and capable label classes. From the data that has been collected, 1154 data. The data goes through processing, scoring, labeling, and testing, producing two classes of results, namely less fortunate and capable. From the test data using the Support Vector Machine (SVM) method, the accuracy score is 97%, the precision score is 97%, the Recall score is 100%, and the F1-Score is 98%. This test resulted in a proportion of classification with the capable label is 87% and less fortunate label is 13%

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Article History
Submitted: 2021-12-16
Published: 2021-12-31
Abstract View: 10 times
PDF Download: 3 times
How to Cite
Aldino, A., Saputra, A., Nurkholis, A., & Setiawansyah, S. (2021). Application of Support Vector Machine (SVM) Algorithm in Classification of Low-Cape Communities in Lampung Timur. Building of Informatics, Technology and Science (BITS), 3(3), 325-330. https://doi.org/10.47065/bits.v3i3.1041
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