A Classification System for Jamu Efficacy Based on Formula Using Support Vector Machine and K-Means Algorithm as a Feature Selection

被引：0

作者：

Puspita, M. N. ^{[1
]}

Kusuma, W. A. ^{[1
]}

Kustiyo, A. ^{[1
]}

Heryanto, R. ^{[2
]}

机构：

[1] Bogor Agr Univ, Fac Matemat & Nat Sci, Dept Comp Sci, Bogor, Indonesia

[2] Bogor Agr Univ, Fac Matemat & Nat Sci, Dept Chem, Bogor, Indonesia

来源：

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) | 2015年

关键词：

classification; feature selection; jamu; k-means; machine learning; support vector machine;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research that using SVM method without feature selection. This study used variances to evaluate the results of clustering. The total of 3138 data herbs and 465 plant species were grouped into 100 clusters with the variance of 0.0094. The managed group succesfully reduced the data dimension into 3047 of jamu sample and 236 species of herbs and plants as features. The result of SVM classification using feature selection yielded the accuracy of 71.5%.

引用

页码：215 / 220

页数：6

共 50 条

[21] A K-means Text Clustering Algorithm Based on Subject Feature Vector
Duo, Ji
Zhang, Peng
Hao, Liu
JOURNAL OF WEB ENGINEERING, 2021, 20 (06): : 1935 - 1946
[22] Classification and Feature Selection Method for Medical Datasets by Brain Storm Optimization Algorithm and Support Vector Machine
Tuba, Eva
Strumberger, Ivana
Bezdan, Timea
Bacanin, Nebojsa
Tuba, Milan
7TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT (ITQM 2019): INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT BASED ON ARTIFICIAL INTELLIGENCE, 2019, 162 : 307 - 315
[23] Support vector-based feature selection using Fisher's linear discriminant and Support Vector Machine
Youn, Eunseog
Koenig, Lars
Jeong, Myong K.
Baek, Seung H.
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (09) : 6148 - 6156
[24] Detecting diseases in Chilli Plants Using K-Means Segmented Support Vector Machine
Wahab, Abdul Hafiz Bin Abdul
Zahari, Rahimi
Lim, Tiong Hoo
2019 3RD INTERNATIONAL CONFERENCE ON IMAGING, SIGNAL PROCESSING AND COMMUNICATION (ICISPC), 2019, : 57 - 61
[25] A Cheap Feature Selection Approach for the K-Means Algorithm
Capo, Marco
Perez, Aritz
Lozano, Jose A.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (05) : 2195 - 2208
[26] Collaborative filtering system based on classification and extended K-means algorithm
Wu Y.K.
Tang Z.H.
Advances in Information Sciences and Service Sciences, 2011, 3 (07): : 187 - 194
[27] Optimization Approach for Feature Selection and Classification with Support Vector Machine
Chidambaram, S.
Srinivasagan, K. G.
COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 1, CIDM 2015, 2016, 410 : 103 - 111
[28] A Feature Selection Newton Method for Support Vector Machine Classification
Glenn M. Fung
O.L. Mangasarian
Computational Optimization and Applications, 2004, 28 : 185 - 202
[29] The Hybrid Feature Selection k-means Method for Arabic Webpage Classification
Alghamdi, Hanan
Selamat, Ali
JURNAL TEKNOLOGI, 2014, 70 (05):
[30] A feature selection Newton method for support vector machine classification
Fung, GM
Mangasarian, OL
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2004, 28 (02) : 185 - 202

← 1 2 3 4 5 →