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

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Journal : telkomnika telecommunication computing electronics and control

 1 
The comparison study of kernel KC-means and support vector machines for classifying schizophrenia Sri Hartini; Zuherman Rustam
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.v18i3.14847

Abstract

Schizophrenia is one of mental disorder that affects the mind, feeling, and behavior. Its treatment is usually permanent and quite complicated; therefore, early detection is important. Kernel KC-means and support vector machines are the methods known as a good classifier. This research, therefore, aims to compare kernel KC-means and support vector machines, using data obtained from Northwestern University, which consists of 171 schizophrenia and 221 non-schizophrenia samples. The performance accuracy, F1-score, and running time were examined using the 10-fold cross-validation method. From the experiments, kernel KC-means with the sixth-order polynomial kernel gives 87.18 percent accuracy and 93.15 percent F1-score at the faster running time than support vector machines. However, with the same kernel, it was further deduced from the results that support vector machines provides better performance with an accuracy of 88.78 percent and F1-score of 94.05 percent.
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
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