Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

1.81

P-Index

This Author published in this journals

All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding KOMMIT TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Kefarmasian Indonesia Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Science Annals of Mathematical Modeling

Zuherman Rustam

Universitas Indonesia

Author-ID : 2404855

Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

Published : 21 Documents Claim Missing Document

Claim Missing Document

Articles

Title

New feature selection based on kernel Zuherman Rustam; Sri Hartini
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Feature selection is an essential issue in machine learning. It discards the unnecessary or redundant features in the dataset. This paper introduced the new feature selection based on kernel function using 16 the real-world datasets from UCI data repository, and k-means clustering was utilized as the classifier using radial basis function (RBF) and polynomial kernel function. After sorting the features using the new feature selection, 75 percent of it was examined and evaluated using 10-fold cross-validation, then the accuracy, F1-Score, and running time were compared. From the experiments, it was concluded that the performance of the new feature selection based on RBF kernel function varied according to the value of the kernel parameter, opposite with the polynomial kernel function. Moreover, the new feature selection based on RBF has a faster running time compared to the polynomial kernel function. Besides, the proposed method has higher accuracy and F1-Score until 40 percent difference in several datasets compared to the commonly used feature selection techniques such as Fisher score, Chi-Square test, and Laplacian score. Therefore, this method can be considered to use for feature selection

Cerebral infarction classification using multiple support vector machine with information gain feature selection Zuherman Rustam; Arfiani Arfiani; Jacub Pandelaki
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Stroke ranks the third leading cause of death in the world after heart disease and cancer. It also occupies the first position as a disease that causes both mild and severe disability. The most common type of stroke is cerebral infarction, which increases every year in Indonesia. This disease does not only occur in the elderly, but in young and productive people which makes early detection very important. Although there are varied of medical methods used to classify cerebral infarction, this study uses a multiple support vector machine with information gain feature selection (MSVM-IG). MSVM-IG is a modification among IG Feature Selection and SVM, where SVM conducted doubly in the process of classification which utilizes the support vector as a new dataset. The data obtained from Cipto Mangunkusumo Hospital, Jakarta. Based on the results, the proposed method was able to achieve an accuracy value of 81%, therefore, this method can be considered to use for better classification result.

Twin support vector machine using kernel function for colorectal cancer detection Zuherman Rustam; Fildzah Zhafarina; Jane Eva Aurelia; Yasirly Amalia
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3179

Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artiﬁcial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.

Co-Authors Ajeng Maharani Putri Aldi Purwanto Arfiani Arfiani Belawati Widjaja Benyamin Kusumoputro Bustamam, Alhadi Chelvian Aroef Devvi Sarwinda Fildzah Zhafarina Glori Stephani Saragih Glori Stephani Saragih Hamimah Alatas Ilsya Wirasati Ilsya Wirasati Jane Eva Aurelia Jane Eva Aurelia María Jesús Segovia Vargas Nadya Asanul Husna Nyoman A. Wibawa Pandelaki, Jacub Rivan Pratama Yuda Selly A. A. K Sri Hartini Sri Hartini Suryadi MT Widjaja, Belawati Wiwit Widhianto Yasirly Amalia Yuda Rivan ZUHELMI AZIZ

Title Search

Found 3 Documents Search Journal : Bulletin of Electrical Engineering and Informatics

Abstract

Abstract

Abstract

Title

Found 3 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics