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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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IMPLEMENTASI ALGORITMA ENKRIPSI CITRA DIGITAL MENGGUNAKAN SKEMA TRANPOSISI BERBASIS FUNGSI CHAOS MT, Suryadi; Rustam, Zuherman; Widhianto, Wiwit
Prosiding KOMMIT 2014
Publisher : Prosiding KOMMIT

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Algoritma enkripsi citra digital yang dikembangkan dalam paper ini ditujukansebagai alternatif dalam mengamankan informasi citra tersebut. Lenaha yangdilakukan adalah dengan menggunakan skema transposisi yang berbasis fungsichaos, yaitu fungsi Arnold’s cat map. Fungsi tersebut berfungsi sebagai bentuktransposisi atau pertukaran posisi dari informasi data aslinya. Akan ditetapkanskema transposisi tertentu untuk mengacak informasi asli sehingga sulit untukdibaca kembali oleh pihak ketiga. Selanjutnya dilakukan pengujian secarapraktis. Pengujian dilakukan untuk beragam data berupa citra digital denganberbagai ukuran. Hasil analisis pengujian secara praktis menunjukkan bahwaruang kunci yang dihasilkan sangat jauh lebih besar dan tingkat sensitivitasnyasangat jauh lebih kecil.
Pancreatic cancer classification using logistic regression and random forest Zuherman Rustam; Fildzah Zhafarina; Glori Stephani Saragih; Sri Hartini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp476-481

Abstract

In the medical field, technology machinery is needed to solve several classification problems. Therefore, this research is useful to solve the problem of the medical field by using machine learning. This study discusses the classification of pancreatic cancer by using regression logistics and random forest. By comparing the accuracy, precision, recall (sensitivity), and F1-score of both methods, then we will know which method is better in classifying the pancreatic cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that random forest has better accuracy than logistic regressions. It can be seen with maximum accuracy of logistic regressions 96.48 with 30% data training and random forest 99.38% with 20% of data training.
Estimating probability of banking crises using random forest Sri Hartini; Zuherman Rustam; Glori Stephani Saragih; María Jesús Segovia Vargas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp407-413

Abstract

Banks have a crucial role in the financial system. When many banks suffer from the crisis, it can lead to financial instability. According to the impact of the crises, the banking crisis can be divided into two categories, namely systemic and non-systemic crisis. When systemic crises happen, it may cause even stable banks bankrupt. Hence, this paper proposed a random forest for estimating the probability of banking crises as prevention action. Random forest is well-known as a robust technique both in classification and regression, which is far from the intervention of outliers and overfitting. The experiments were then constructed using the financial crisis database, containing a sample of 79 countries in the period 1981-1999 (annual data). This dataset has 521 samples consisting of 164 crisis samples and 357 non-crisis cases. From the experiments, it was concluded that utilizing 90 percent of training data would deliver 0.98 accuracy, 0.92 sensitivity, 1.00 precision, and 0.96 F1-Score as the highest score than other percentages of training data. These results are also better than state-of-the-art methods used in the same dataset. Therefore, the proposed method is shown promising results to predict the probability of banking crises.
Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification Glori Stephani Saragih; Sri Hartini; Zuherman Rustam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp60-65

Abstract

This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.
Comparison some of kernel functions with support vector machines classifier for thalassemia dataset Ilsya Wirasati; Zuherman Rustam; Jane Eva Aurelia; Sri Hartini; Glori Stephani Saragih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp430-437

Abstract

In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%.
Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image Zuherman Rustam; Aldi Purwanto; Sri Hartini; Glori Stephani Saragih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp291-297

Abstract

Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy.
Acute sinusitis data classification using grey wolf optimization-based support vector machine Ajeng Maharani Putri; Zuherman Rustam; Jacub Pandelaki; Ilsya Wirasati; Sri Hartini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp438-445

Abstract

Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming language with different percentages of training data, which started from 10% to 90%. The GWO-SVM method proposed provides better accuracy than using SVM without GWO.
Hepatitis classification using support vector machines and random forest Jane Eva Aurelia; Zuherman Rustam; Ilsya Wirasati; Sri Hartini; Glori Stephani Saragih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp446-451

Abstract

Hepatitis is a medical condition defined by inflammation of the liver. It can be caused by infection of the liver by hepatitis viruses or is of unknown aetiology. There are 5 main hepatitis viruses, such as virus types A, B, C, D and E. The infection may occur with limited or no symptoms, but also may include some symptoms like abdominal pain, dark urine, extreme fatigue, jaundice, nausea or vomiting. Because Indonesia is a large archipelago, the prevalence of viral infections varies greatly by region of acute hepatitis patients. This research uses data of hepatitis examination result with amount of 113 data and 5 features. The method that used is support vector machines (SVM) and random forest method. SVM is the classification method that uses discriminant hyper-plane, dividing to classes. meanwhile, random forest is a tree-based ensemble depending on a collection of random variables. SVM and random forest (RF) are applied to predict hepatitis data, and then the results will be compared.
Linear discriminant analysis and support vector machines for classifying breast cancer Zuherman Rustam; Yasirly Amalia; Sri Hartini; Glori Stephani Saragih
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp253-256

Abstract

Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.
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.
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