Now-a-days, coronary heart disease is one of the deadliest diseases in the world. An unfavorable lifestyle, lack of physical activity, and consuming tobacco are the causes of coronary heart disease aside from genetic inheritance. Sometimes the patient does not know whether he has abnormalities in heart function or not. Therefore, this study proposes a system that can detect heart abnormalities through the iris, known as the Iridology method. The system is designed automatically in the iris detection to the classification results. Feature extraction using five characteristics is applied to the Gray Level Cooccurrence Matrix (GLCM) method. The classification process uses the Support Vector Machine (SVM) with linear kernel variation, Polynomial, and Gaussian to obtain the best accuracy in the system. From the system simulation results, the use of the Gaussian kernel can be relied on in the classification of iris conditions with an accuracy rate of 91%, then the Polynomial kernel accuracy reaches 89%, and the linear kernel accuracy reaches 87%. This study has succeeded in detecting heart conditions through the iris by dividing the iris into normal iris and abnormal iris.
Cedarwood is one of the most sought-after materials since it can be used to create a wide variety of household appliances. Other than its unique aroma, the product's quality is the most important selling attribute. Fiber patterns allow for a qualitative categorization of this wood. Traditionally, workers in the wood-processing business have relied solely on their eyesight to sort materials into several categories. As a result, there will be discrepancies in precision and efficiency, which will hurt the reputation of the regional wood sector. The answer to this issue is machine learning. In this study, we compare the performance of two different cedarwood quality classification systems where both systems use different machine learning methods namely Support Vector Machine (SVM) and Convolutional Neural Network (CNN). Each system will be sent images captured with a Logitech Brio 4K equipped with a joystick and ultrasonic sensors, labeled as belonging to one of five cedar classes (A, B, C, D, or E). In the initial method to learn the wood's pattern and texture, the Histogram of Oriented Gradient (HOG) will be used to identify the material. Meanwhile, the classification method uses a Support Vector Machine (SVM) which will be compared to find the best accuracy and time computation. The first system's experiment achieves 90 percent accuracy with a computation time of 1.40 seconds. For the second, we use a Convolutional Neural Network, a deep learning technique, to classify cedarwood (CNN). Extraction of features occurs in the convolution, activation, and pooling layers. Experimental results demonstrated a considerable enhancement, with an accuracy of 97% and a prediction speed of 0.56 seconds.
The development of telecommunications in Indonesia until now has experienced a very significant increase and has become a significant need in communication. Many people use communication tools daily, causing many providers to set up Base Transceiver Stations (BTS) to reach their users to remote areas. BTS has a transmit power that can reach the destination area, but most people still do not know the level of radiation emitted and the health effects on the body. Therefore the International Commission for Non-Ionizing Radiation Protection (ICNIRP) has set a threshold level of safe radiation for the human body. Sambas is one of the cities in West Kalimantan which is the target for the development of BTS establishments by operators. This makes the surrounding community feel afraid of the health caused by radiation from the BTS. So it is necessary to do some research, socialize, measuring, and evaluate the level of radiation emitted from BTS, especially in residential areas. The research was conducted through several stages, including; data collection, data collection methods on variations in distance from BTS, results of radiation level measurements, and comparisons to the safe threshold value for radiation intensity that has been set by ICNIRP. The measurement results from 20 BTS in Sambas show that the radiation level from the BTS measured is still far from the safe radiation threshold that has been set by ICNIRP.
The need for a communication system with a higher data rate and mobility grows along with information and communication technology development. Combining MC-CDMA with the MIMO system and supporting the system with a good transmit diversity technique is a promising idea to provide the needed communication system, especially in high mobility conditions. MC-CDMA can support ubiquitous communications without affecting the achievable BER and is more capable of high-speed mobility. It integrates the benefit of both OFDM and CDMA. On the other hand, QO-STBC increases the bit rate without using additional bandwidth to transmit diversity in the MIMO system. So, this study proposed a system combining the MIMO MC-CDMA system with QO-STBC. The proposed system is investigated under high mobility conditions to see the system's performance. The simulation results show that our system performs better than the MC-CDMA STBC system and the QOSTBC system but not better than the MC-CDMA multilevel coding scheme. To reach the value of BER 10−3, MC-CDMA multilevel Coding requires less power, around 5 dB, than the proposed system.
Heart disease is a disease with the second-highest mortality rate in the world. This happens because of an unhealthy human lifestyle. This unhealthy lifestyle affects the performance of the body's organs in carrying out their functions. Stroke can be prevented by exercising regularly, eating nutritious foods, not consuming alcohol, and not consuming tobacco. One way to find out if someone is free from stroke or not can be done by medical check-ups. However, this method is quite expensive. Given these problems, this study aims to design an early identification system for detecting early-stage stroke. The system is designed by utilizing the condition and history of the subject for identification. This study uses a back propagation neural network for the classification process. Variations in the use of hidden layers in each experiment were used to obtain the highest accuracy in the training process. From the results of the study, it was found that the system designed can detect early stroke with an accuracy rate of 97.8%.
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