Zuherman Rustam

EDUCATION

PhD in Computer Science

RESEARCH INTERESTS

Computatioinal Intelligence , Data Mining , Machine Learning , Big Data

Scopus Publications

Twin support vector machine using kernel function for colorectal cancer detection
Zuherman Rustam, Fildzah Zhafarina, Jane Eva Aurelia, and Yasirly Amalia
Bulletin of Electrical Engineering and Informatics, ISSN: 20893191, eISSN: 23029285, Pages: 3121-3126, Published: December 2021 Institute of Advanced Engineering and Science
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.


Twin Support Vector Machines for Thalassemia Classification
Alva Andhika Sa'id, Zuherman Rustam, Fevi Novkaniza, Qisthina Syifa Setiawan, Faisa Maulidina, and Velery Virgina Putri Wibowo
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021, Pages: 160-164, Published: 29 September 2021 IEEE
Thalassemia is one of the incurable blood disorders inherited from parents with its history. This disease causes abnormality in the blood cells, specifically the protein composition such as hemoglobin. Furthermore, it has spread out across the Mediterranean Sea and through Indonesia due to the migration of people. Early detection to diagnose thalassemia is necessary to prevent the disease from spreading to another generation. This study aims to analyze the impact of machine learning in medical diagnosis, and its disease detection methods based on clinical history. Several previous studies have been incorporated into early screening for diagnosis of thalassemia with machine learning technique based on classification problem, and it showed great performance evaluation beyond 90% accuracy. In addition, the data used was laboratory results of blood check obtained from Harapan Kita Children and Women's Hospital, Jakarta, Indonesia. Twin Support Vector Machines (TSVM) is used in this study as one of the machine learning developed techniques inspired by Support Vector Machines (SVM), as this technique purposed to find the nonparallel hyperplanes to solve binary classification problem. This was conducted through three commonly used kernels from several previous studies, including Linear, Polynomial, and Radial Basis Function (RBF). The results showed that RBF TSVM gave the best results with 99.32%, 99.75% and 99.24% average of accuracy, precision, and F1 score, respectively. However, Polynomial TSVM, as the lowest results had 99.79% average of recall. In this context, the TSVM role is recommended for future studies to facilitate medical diagnosis based on the clinical history of other diseases.


Lung cancer classification using fuzzy C-means and fuzzy Kernel C-means based on CT scan image
IAES International Journal of Artificial Intelligence, ISSN: 20894872, eISSN: 22528938, Pages: 291-297, Published: June 2021



Comparison support vector machines and K-nearest neighbors in classifying Ischemic stroke by using convolutional neural networks as a feature extraction
Glori Saragih and Zuherman Rustam
ACM International Conference Proceeding Series, Published: 1 April 2021 ACM
The paper introduces the hybrid method of Convolutional Neural Network (CNN) and machine learning methods as a classifier, that is Support Vector Machines and K-Nearest Neighbors for classifying the ischemic stroke based on CT scan images. CNN is used as a feature extraction and the machine learning methods used to replace the fully connected layers in CNN. The proposed method is used to reduce computation time and improve accuracy in classifying image data, because we know that deep learning is not efficient for small amounts of data, where the data we use is only 93 CT scan images obtained from Cipto Mangunkusumo General Hospital (RSCM), Indonesia. The architecture of CNN used in this research consists of 5 layers convolutional layers, ReLU, MaxPooling, batch normalization and dropout. The elapsed time required for CNN is 7.631490 seconds. The output of feature extraction is used as an input for SVM and KNN. SVM with linear kernel can correctly classify ischemic stroke, with 100% accuracy in the training model and 96% accuracy in testing model with a test size of 60%. KNN classify ischemic stroke, with 97.3% (#neighbors = 5) accuracy in training model with a test size of 60% and 90% (#neighbors = 10, 15, 25) accuracy in the testing model with a test size of 10%. Based on these results, the SVM produces the higher accuracy compared to KNN in classifying ischemic stroke using CNN as feature extraction based on CT scan images with a computation time of only 8.0973 seconds.


An approach for COVID-19 detection using deep convolutional features on chest X-ray images
Journal of Theoretical and Applied Information Technology, ISSN: 19928645, eISSN: 18173195, Pages: 1452-1460, Published: 31 March 2021



Comparison of Colorectal Cancer Classification between K-Nearest Neighbors (K-NN) and Neural Network
F Zhafarina, Z Rustam, Y Amalia, and I Wirasati
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1821, Published: 29 March 2021 IOP Publishing



Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification
F Maulidina, Z Rustam, S Hartini, V V P Wibowo, I Wirasati, and W Sadewo
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1821, Published: 29 March 2021 IOP Publishing



Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine
V V P Wibowo, Z Rustam, S Hartini, F Maulidina, I Wirasati, and W Sadewo
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1821, Published: 29 March 2021 IOP Publishing



Improvement in automated diagnosis of soft tissues tumors using machine learning
El Arbi Abdellaoui Alaoui, Stephane Cedric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan, and Said Agoujil
Big Data Mining and Analytics, ISSN: 20960654, Pages: 33-46, Published: March 2021 Tsinghua University Press



Classification of sinusitis using kernel three-way c-means
S Hartini, Z Rustam, J Pandelaki, M Prasetyo, and R E Yunus
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Cerebral Infarction Classification Using the K-Nearest Neighbor and Naive Bayes Classifier
S H Rukmawan, F R Aszhari, Z Rustam, and J Pandelaki
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Preprocessing Unbalanced Data using Support Vector Machine with Method K-Nearest Neighbors for Cerebral Infarction Classification
A G M Sari, A M Putri, Z Rustam, and J Pandelaki
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Classification of Cerebral Infarction Data Using K-Means and Kernel K-Means
A M Putri, A G M Sari, Z Rustam, and J Pandelaki
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Fuzzy C-Means Clustering with Minkowski and Euclidean Distance for Cerebral Infarction Classification
R Khairi, S G Fitri, Z Rustam, and J Pandelaki
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Schizophrenia Classification Using Fuzzy Kernel C-Means
S Hartini and Z Rustam
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Pancreatic Cancer Classification Using the Kernel-based Support Vector Machine (KSVM)
R Khairi, S G Fitri, and Z Rustam
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Classification of Infarction using Random Forest
S H Rukmawan, F R Aszhari, Z Rustam, and J Pandelaki
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Prediction Insolvency of Insurance Companies Using Random Forest
Z Rustam and G Saragih
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Comparison between Fisher's Ratio and Information Gain with SVM classifier for 3 levels of enthusiasm classification through face recognition
Z Rustam, Andrea Laksmirani Kristina, and Y Satria
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Naïve Bayes Classifier and Particle Swarm Optimization Feature Selection Method for Classifying Intrusion Detection System Dataset
A S Talita, O S Nataza, and Z Rustam
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Kernel Entropy Based Fuzzy C-Means (KEFCM) for Acute Sinusitis
Z Rustam, N Shandri, T Siswantining, and J Pandelaki
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Prostate Cancer Classification Using Random Forest and Support Vector Machines
Z Rustam and N Angie
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Kernel Based Comparison between Fuzzy C-Means and Support Vector Machine for Sinusitis Classification
R A Putri, Z Rustam, J Pandelaki, and N Salmi
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1752, Published: 15 February 2021 IOP Publishing



Multiclass classification of leukemia cancer data using Fuzzy Support Vector Machine (FSVM) with feature selection using Principal Component Analysis (PCA)
I R Fauzi, Z Rustam, and A Wibowo
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1725, Published: 12 January 2021 IOP Publishing



Classification of the likelihood of Indonesian Facebook users in spreading hoaxes using Support Vector Machine (SVM)
T V Rampisela, H T Andarlia, and Z Rustam
Journal of Physics: Conference Series, ISSN: 17426588, eISSN: 17426596, Volume: 1725, Published: 12 January 2021 IOP Publishing

RECENT SCHOLAR PUBLICATIONS

Classification of Brain Tumor Using K-Nearest Neighbor-Genetic Algorithm and Support Vector Machine-Genetic Algorithm Methods
VVP Wibowo, Z Rustam, J Pandelaki
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Logistic Regression and Logistic Regression-Genetic Algorithm for Classification of Liver Cancer Data
VVP Wibowo, Z Rustam, AR Laeli, AA Sa'id
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Fuzzy C-Means-Grey Wolf Optimization for Classification of Stroke
QS Setiawan, Z Rustam, AA Sa'id, F Maulidina, W Sadewo, F Novkaniza
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



An Analysis of Convolutional Neural Network—Random Forest for Liver Cancer CT Scan Images
JE Aurelia, Z Rustam, D Lestari
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Lung Cancer Classification using Support Vector Machine and Hybrid Particle Swarm Optimization-Genetic Algorithm
F Maulidina, Z Rustam, J Pandelaki
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Liver Cancer Classification Using Random Forest and Extreme Gradient Boosting (XGBoost) with Genetic Algorithm as Feature Selection
VS Desdhanty, Z Rustam
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Comparison of Naive Bayes and Support Vector Machine with Grey Wolf Optimization Feature Selection for Cervical Cancer Data Classification
QS Setiawan, Z Rustam, J Pandelaki
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Feature Selection using Particle Swarm Optimization and Random Forest for Hepatocellular Carcinoma (HCC) Classification
F Maulidina, Z Rustam, M Novita, QS Setiawan
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Tuberculosis Detection based on Chest X-Rays using Ensemble Method with CNN Feature Extraction
AR Laeli, Z Rustam, J Pandelaki
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Convolutional Neural Network–Support Vector Machines for Age-Related Macular Degeneration Classification Based on Fundus Images
AA Sa'id, Z Rustam, F Novkaniza, M Novita
2021 International Conference on Decision Aid Sciences and Application (DASA 2021



Twin support vector machine using kernel function for colorectal cancer detection
Z Rustam, F Zhafarina, JE Aurelia, Y Amalia
Bulletin of Electrical Engineering and Informatics 10 (6), 3121-3126 2021



A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X
J Aurelia, Z Rustam
Open Access Macedonian Journal of Medical Sciences 9 (B), 1283-1289 2021



A hybrid model based on convolutional neural networks and fuzzy kernel k-medoids for lung cancer detection
GS Saragih, Z Rustam, JE Aurelia
Indonesian Journal of Electrical Engineering and Computer Science 24 (1 2021



Cervical cancer classification using convolutional neural network-support vector machine
JE Aurelia, Z Rustam, I Wirasati
Telkomnika 19 (5), 1605-1611 2021



Twin Support Vector Machines for Thalassemia Classification
AA Sa'Id, Z Rustam, F Novkaniza, QS Setiawan, F Maulidina, ...
2021 International Conference on Innovation and Intelligence for Informatics 2021



Pancreatic cancer classification using logistic regression and random forest
Z Rustam, F Zhafarina, GS Saragih, S Hartini
IAES International Journal of Artificial Intelligence 10 (2), 476 2021



Hepatitis classification using support vector machines and random forest
JE Aurelia, Z Rustam, I Wirasati, S Hartini, GS Saragih
IAES International Journal of Artificial Intelligence 10 (2), 446 2021



Acute sinusitis data classification using grey wolf optimization-based support vector machine
AM Putri, Z Rustam, J Pandelaki, I Wirasati, S Hartini
IAES International Journal of Artificial Intelligence 10 (2), 438 2021



Estimating probability of banking crises using random forest
S Hartini, Z Rustam, GS Saragih, MJS Vargas
IAES International Journal of Artificial Intelligence 10 (2), 407 2021



Comparison some of kernel functions with support vector machines classifier for thalassemia dataset
I Wirasati, Z Rustam, JE Aurelia, S Hartini, GS Saragih
IAES International Journal of Artificial Intelligence 10 (2), 430 2021

MOST CITED SCHOLAR PUBLICATIONS

Nave Bayes classifier models for predicting the colon cancer
N Salmi, Z Rustam
IOP Conference Series: Materials Science and Engineering 546 (5), 052068 2019
Citations: 40



Insolvency prediction in insurance companies using support vector machines and fuzzy kernel c-means
Z Rustam, F Yaurita
Journal of Physics: Conference Series 1028 (1), 012118 2018
Citations: 37



Classification of schizophrenia data using support vector machine (SVM)
TV Rampisela, Z Rustam
Journal of Physics: Conference Series 1108 (1), 012044 2018
Citations: 36



Comparison between support vector machine and fuzzy c-means as classifier for intrusion detection system
Z Rustam, D Zahras
Journal of Physics: Conference Series 1028 (1), 012227 2018
Citations: 36



Feature selection using random forest classifier for predicting prostate cancer
M Huljanah, Z Rustam, S Utama, T Siswantining
IOP Conference Series: Materials Science and Engineering 546 (5), 052031 2019
Citations: 34



Fuzzy kernel c-means algorithm for intrusion detection systems
Z Rustam, AS Talita
Journal of Theoretical and Applied Information Technology 81 (1), 161-165 2015
Citations: 33



Classification of cancer data using support vector machines with features selection method based on global artificial bee colony
T Nadira, Z Rustam
AIP Conference Proceedings 2023 (1), 020205 2018
Citations: 29



Application of machine learning on brain cancer multiclass classification
V Panca, Z Rustam
AIP Conference Proceedings 1862 (1), 030133 2017
Citations: 29



Cancer classification using Fuzzy C-Means with feature selection
AA Rachman, Z Rustam
2016 12th International Conference on Mathematics, Statistics, and Their 2016
Citations: 28



Kernel spherical k-means and support vector machine for acute sinusitis classification
Z Rustam, J Pandelaki, A Siahaan
IOP Conference Series: Materials Science and Engineering 546 (5), 052011 2019
Citations: 27



Support Vector Machines for classifying policyholders satisfactorily in automobile insurance
Z Rustam, NPAA Ariantari
Journal of Physics: Conference Series 1028 (1), 012005 2018
Citations: 25



Fuzzy kernel k-medoids algorithm for multiclass multidimensional data classification
Z Rustam, AS Talita
Journal of Theoretical and Applied Information Technology 80 (1), 147-151 2015
Citations: 23



Comparing random forest and support vector machines for breast cancer classification
C Aroef, Y Rivan, Z Rustam
Telkomnika 18 (2), 815-821 2020
Citations: 21



Classification of breast cancer using fast fuzzy clustering based on kernel
Z Rustam, S Hartini
IOP Conference Series: Materials Science and Engineering 546 (5), 052067 2019
Citations: 21



Hybrid preprocessing method for support vector machine for classification of imbalanced cerebral infarction datasets
Z Rustam, DA Utami, R Hidayat, J Pandelaki, WA Nugroho
International Journal on Advanced Science Engineering Information Technology 2019
Citations: 20



Osteoarthritis disease prediction based on random forest
U Aprilliani, Z Rustam
2018 International Conference on Advanced Computer Science and Information 2018
Citations: 19



Application of support vector regression in indonesian stock price prediction with feature selection using particle swarm optimisation
Z Rustam, P Kintandani
Modelling and Simulation in Engineering 2019 2019
Citations: 18



Cervical cancer risk classification based on deep convolutional neural network
D Zahras, Z Rustam
2018 International Conference on Applied Information Technology and 2018
Citations: 18



Fuzzy Kernel k-Medoids algorithm for anomaly detection problems
Z Rustam, AS Talita
AIP conference proceedings 1862 (1), 030154 2017
Citations: 17



Ovarian cancer data classification using bagging and random forest
A Arfiani, Z Rustam
AIP Conference Proceedings 2168 (1), 020046 2019
Citations: 16