There are more than 100 types of cancer around the world with different symptoms and difficulty in predicting its appearance 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.
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.
Everyone joints go through a cycle of damage and repair during their lifetime, but sometimes the body’s process to repair our joints can cause changes in their shape or structure. When these changes happen, it’s known as osteoarthritis. Osteoarthritis is the most common form of arthritis, affecting millions of people worldwide. Osteoarthritis causes pain, swelling, stiffness in the areas, and decreased the ability to move for the sufferers. Therefore it requires accurate method of classification. Many methods have been used to classify osteoarthritis, but this study will apply Multinomial Logistic Regression and Super Vector Machine (SVM) as the machine learning methods. We used CT scan result data from RSUPN dr. Cipto Mangunkusumo, Central Jakarta. The results show the SVM provides better results than Multinomial Logistic Regression in terms of classification accuracy. The highest accuracy of SVM reaches around 85%, while Multinomial Logistic Regression only 71%.
Biometrics are physical or behavior characteristics of human that can be used to identify someone. One form of physical characteristics possessed by humans is fingerprints, retinal scanning, face and hand geometry, while one form of behavioral characteristics possessed by humans is handwriting, signatures, mouse usage analysis, walking patterns, etc. Basically, physical characteristics are more easily observed than behavioral characteristics. Therefore, physical characteristics are more often used in many aspects of security. One of the most common physical characteristics is face. By seeing the face, we can find out or predict how old they are, their gender and even their expression. However, there are still many mistakes in predicting a gender through person’s face. In fact, there are still many crimes in falsifying self-identity (such as gender). So, we need a method that is able to classify identity (gender) based on a person’s face appropriately. One method that can be used is Convolutional Neural Networks (CNNs). Later, CNNs will classify a person’s gender (male / female) based on a person’s face image data. And based on.
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