Similarity-AF-Comparison of Machine Learning Performance using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Fadlil, Abdul and Riadi, Imam and Julda D.E Purwadi Putra, Izzan Similarity-AF-Comparison of Machine Learning Performance using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns. Revue d'Intelligence Artificielle, 37 (2). pp. 379-385.

Text Similarity-AF-Comparison of Machine Learning Performance using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns.pdf Download (2MB)

Abstract

Batik is a work of art from Indonesia that has many types and pattern. One of the batik producing areas is Surakarta, the famous pattern in this area are Sawat, Sementrante, and
Satriomanah. The problem that arises is the difficulty of distinguishing the three existing pattern because they have a high level of similarity. Therefore, this research aims to solve
these problems using NB and RF methods. As a feature extraction, a Gray Level Co�occurrence Matrix is used as a texture feature extraction. The research phase includes methods for dataset collection, preprocessing, feature extraction, and classification. These two methods, RF and NB, can be used as methods for batik fabric classification. The most
accurate result obtained by the RF method was 97.91% accurate in dataset A, while the NB method was 96.66% accurate on the same dataset. According to the research results, it is found that the RF method outperforms the NB method in classifying the types of batik patterns.

Item Type:	Artikel Umum
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	Drs. Abdul Fadlil, M.T., Ph.D.
Date Deposited:	20 Jun 2023 04:17
Last Modified:	20 Jun 2023 04:17
URI:	http://eprints.uad.ac.id/id/eprint/43451

Actions (login required)

View Item