Classification of Science, Technology and Medicine (STM) Domains with PSO and NBC

Classification of Science, Technology and Medicine (STM) Domains with PSO and NBC

• By Junianto, Erfian; Shobary, Mayya Nurbayanti; Rachman, Rizal; Warnilah, Ai Ilah; Simpony, Bambang Kelana;
• Seminar Internasional Bereputasi
• 113
• 122-127

Science, Technology, and Medicine (STM) is a field of
research that has a characteristic in each document. These
characteristics are different from most documents that are used as a
corpus in mining text research. Documents derived from Newswire
are more dominant in previous research. However, in this study will
try to classify documents from STM field. Complex technical terms,
symbols, position information, and the number of citations would
be a challenge itself. Previous studies have used the Naive Bayes
Classifier (NBC) classification method. There are also those who
apply Particle Swarm Optimization to assist its classification. From
the Newswire field generated a fairly high accuracy Therefore, it
would be applied to the optimization method with PSO and combine
it with NBC method. This study produced accuracy value in
classification model without using PSO equal to 82,73%. While in
the classification model using PSO, the accuracy value is 87.27%.
This shows that the use of PSO optimization is very influential on
the classification.  

• Katakunci STM, Classification, Naive Bayes Classifier, Particle Swarm Optimization.
• URL https://ieeexplore.ieee.org/abstract/document/8674271/
• Penerbit IEEE (Institute Of Electrical and Electronical Engineering,

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