Open AccessApplication of Support Vector Machine (SVM) Algorithm in Classification of Low-Cape Communities in Lampung Timur
Author(s) -
Ahmad Ari Aldino,
Asep Handaya Saputra,
Andi Nurkholis,
Setiawansyah Setiawansyah
Publication year - 2021
Publication title -
building of informatics, technology and science
Language(s) - English
Resource type - Journals
eISSN - 2685-3310
pISSN - 2684-8910
DOI - 10.47065/bits.v3i3.1041
Subject(s) - support vector machine , artificial intelligence , computer science , test (biology) , test data , precision and recall , f1 score , machine learning , data mining , pattern recognition (psychology) , paleontology , biology , programming language
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%