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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

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Articles

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Prediction schizophrenia using random forest Zuherman Rustam; Glori Stephani Saragih
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14837

Schizophrenia is a mental illness with a very bad impact on sufferers, attacking the part of human brain that disables the ability to think clearly. In 2018, Rustam and Rampisela classified Schizophrenia by using Northwestern University Schizophrenia Data, based on 66 variables consisting of group, demographic, and questionnaires statistics, based on the scale for the assessment of negative symptoms (SANS), and scale for the assessment of positive symptoms (SAS), and then classifiers that used are SVM with Gaussian kernel and Twin SVM with linear and Gaussian kernel. Furthermore, this research is novel based on the use of random forest as a classifier, in order to predict Schizophrenia. The result obtained is reported in percentage of accuracy, both in training and testing of random forest, which was 100%. This classification, therefore, shows the best value in contrast with prior methods, even though only 40% of training data set was used. This is very important, especially in the cases of rare disease, including schizophrenia.

Pulmonary rontgen classification to detect pneumonia disease using convolutional neural networks Zuherman Rustam; Rivan Pratama Yuda; Hamimah Alatas; Chelvian Aroef
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14839

Every organism is known to have different structural and biological system, specifically in human immunity. If the immune system weakens, the body is susceptible to disease especially pneumonia disease. Pneumonia disease is caused by the bacterium Streptococcus pneumonia, and according to the World Health Organization (WHO), it is identified as the leading cause of death in children worldwide, which is about 16%, for those under the age of 5. Meanwhile, someone who is predicted to have pneumonia by a doctor is recommended for an X-ray. Convolutional neural networks (CNNs) is an accurate method to help the doctor's predicted correctly. CNNs is divided into two important parts, feature extraction layer (convolutional layer and pooling layer) and fully connected layer. CNNs method is commonly used for image data classification. Therefore, CNNs is suitable to classify pneumonia based on lung X-ray in order to obtain accurate prediction results. And then, the results can be seen based on the graph of the accuracy value and the loss value. When CNNs method applied on the dataset, an accuracy rate of 97% was obtained. Based on accuracy rate, it shows that CNNs can be applied to image data (especially lung X-ray) for classification of pneumonia disease.

Cervical cancer classification using convolutional neural network-support vector machine Jane Eva Aurelia; Zuherman Rustam; Ilsya Wirasati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.20406

Cervical cancer is the second most common cancer in women worldwide, and occurs when there are presences of abnormal cells in the cervix, which continue to grow uncontrollably. In the early stages, cervical cancer indications are not perceptible; however, it is easily detected with different forms of machine learning methods, such as the convolutional neural network (CNN). This is a popular method with a wide range of applications and known for its high accuracy value. Moreover, there is a support vector machine (SVM) with several kernel functions that is commonly used in the classification of diseases, and also known for its high accuracy value. Therefore, the combination of CNN–SVM with several linear kernels functions as classifier for the categorization of cervical cancer.

Comparing random forest and support vector machines for breast cancer classification Chelvian Aroef; Yuda Rivan; Zuherman Rustam
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14785

There are more than 100 types of cancer around the world with different symptoms and difficulty in predicting itsappearance in a person due to its random and sudden attack method. However, the appearance of cancer is generally marked by the growth of some abnormal cell. Someone might be diagnosed early and quickly treated, but the cancerous cell most times hides in the body of its victim and reappear, only to kill its sufferer. One of the most common cancers is breast cancer. According to Ministry of Health, in 2018, breast cancer attacked 42 out of every 100.000 people in Indonesia with approximately 17 deaths. In addition, the Ministry recorded a yearly increase in cancer patients. Therefore, there is adequate need to be able to determine those affected by this disease. This study applied the Boruta feature selection to determine the most important features in making a machine learning model. Furthermore, the Random Forest (RF) and Support Vector Machines (SVM) were the machine learning model used, with highest accuracies of 90% and 95% respectively. From the results obtained, the SVM is a better model than random forest in terms of accuracy.

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.

Aplikasi Metode Fuzzy Kernel K-Medoids untuk Klasifikasi Kanker berdasarkan Konsentrasi Logam di dalam Darah ZUHERMAN RUSTAM; ZUHELMI AZIZ
JURNAL ILMU KEFARMASIAN INDONESIA Vol 9 No 2 (2011): JIFI
Publisher : Fakultas Farmasi Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.307 KB)

Classification technique has already been applied widely in the medical data. One of its applications is for classification of cancer. The accuracy of this technique highly depends on the type of data to be processed (whether the data are separable or non-separable) and the dissimilarity function used. To surmount those hindrances and to improve the accuracy of classification therefore a method named Fuzzy Kernel K-Medoids (FKKM). The method can be used for separable or non separable of data. Based on the research on the concentration data of Zn, Ba, Mg, Ca, Cu, and Se in blood in order to diagnose cancer, FKKM gives better result than the Support Vector Machines Method. This paper will discuss an application of the FKKM method on the concentration data of Zn, Ba, Mg, Ca, Cu, and Se in blood samples and compared with the Support Vector Machines Method for the diagnosis of cancer. Results showed that the FKKM method produced a better result than the Support Vector Machines Method.

A hybrid model based on convolutional neural networks and fuzzy kernel K-medoids for lung cancer detection Glori Stephani Saragih; Zuherman Rustam; Jane Eva Aurelia
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp126-133

Lung cancer is the deadliest cancer worldwide. Correct diagnosis of lung cancer is one of the main tasks that is challenging tasks, so the patient can be treated as soon as possible. In this research, we proposed a hybrid model based on convolutional neural networks (CNN) and fuzzy kernel k-medoids (FKKM) for lung cancer detection, where the magnetic resonance imaging (MRI) images are transmitted to CNN, and then the output is used as new input for FKKM. The dataset used in this research consist of MRI images taken from someone who had lung cancer with the treatment of anti programmed cell death-1 (anti-PD1) immunotherapy in 2016 that obtained from the cancer imaging archive. The proposed method obtained accuracy, sensitivity, precision, specificity, and F1-score 100% by using radial basis function (RBF) kernel with sigma of {10-8, 10-4, 10-3, 5x10-2, 10-1, 1, 104} in 20-fold cross-validation. The computational time is only taking less than 10 seconds to forward dataset to CNN and 3.85 ± 0.6 seconds in FKKM model. So, the proposed method is more efficient in time and has a high performance for detecting lung cancer from MRI images.

Lung cancer classification based on support vector machine-recursive feature elimination and artificial bee colony Alhadi Bustamam; Zuherman Rustam; Selly A. A. K; Nyoman A. Wibawa; Devvi Sarwinda; Nadya Asanul Husna
Annals of Mathematical Modeling Vol. 3 No. 1 (2023)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/amm.v3i1.26

Early detection of cancerous cells can increase survival rates for patients by more than 97%. Microarray data, used for cancer classification, are comp osed of many thousands of features and from tens to hundreds of instances. Handling these huge datasets is the most imp ortant challenge in data classification. Feature selection or reduction is therefore an essential task in data classification. We prop ose a cancer diagnostic to ol using a supp ort vector machine for classifier and feature selection. First, we use supp ort vector machine-recursive feature elimination to prefilter the genes. This was enhanced with the artificial b ee colony algorithm. We ran four simulations using Ontario and Michigan lung cancer datasets. This approach provides higher classification accuracy than those without feature selection, supp ort vector machine-recursive feature elimination, or the artificial b ee colony algorithm. The accuracy of a supp ort vector machine using a feature selection-based recursive feature elimination metho d combined with the artificial b ee colony algorithm reached 98% with 100 b est features for the Michigan lung cancer dataset and 97% with 70 b est features for the Ontario lung cancer dataset. SVM with RFE-ABC as the feature selection metho d gives us an accurate result to diagnose Lung cancer using microarray data.

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

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