Open AccessKlasifikasi Data Antroprometri Individu Menggunakan Algoritma Naïve Bayes Classifier
Author(s) -
Johnson Sihombing
Publication year - 2021
Publication title -
bios
Language(s) - English
Resource type - Journals
ISSN - 2722-0850
DOI - 10.37148/bios.v2i1.15
Subject(s) - naive bayes classifier , computer science , classifier (uml) , python (programming language) , artificial intelligence , bayes classifier , bayes' theorem , anthropometry , decision system , machine learning , natural language processing , pattern recognition (psychology) , mathematics , operations research , bayesian probability , medicine , programming language , support vector machine
With the development of advances in computer technology today, most companies and organizations need a decision support system based on information systems, where the information is generally stored in the form of documents / text that is not structured. In this regard, a system for text management that is integrated with the decision support system is needed. One of them is the use of text data classification for anthropometric case studies of several samples. Anthropometry is a measurement of a person's body dimensions. The object of research is gender, first name, and height of a person. The research aims to determine the ratio of the number and height probability level of the number of men and women based on the input into an application using the Naïve Bayes Classifier method. The implementation design uses the Python programming language. The results showed that the height classification data frequency of women was more than the height classification data for men. And the number of height probability of a woman's body is greater than the number of height probability of a man's body.