Digital Image Segmentation Resulting from X-Rays of Covid Patients using K-Means and Extraction Features Method

The COVID-19 pandemic has significant impact on people's lives such as economic, social, psychological and health conditions. The health sector, which is spearheading the handling of the outbreak, has conducted a lot of research and trials related to COVID-19. Coughing is a common symptoms among humans affected by COVID-19 in earlier stage. The first step when a patient shows symptoms of COVID-19 was to conduct a chest x-ray imaging. The chest x-rayss can be used as a digital image dataset for analysing the spread of the virus that enters the lungs or respiratory tract. In this study, 864 x-rays were used as datasets. The images were still raw, taken directly from Covid-19 patients, so there were still a lot of noise. The process to remove unnecessary images would be carried out in the pre-processing stage. The images used as datasets were not mixed with the background which can reduce the value at the next stage. All datasets were made to have a uniform size and pixels to obtain a standard quality and size in order to support the next stage, namely segmentation. The segmentation stage of the x-ray datasets of Covid-19 patients was carried out using the k-means method and feature extraction. The Confusion Matrix method used as testing process. The accuracy value was 78.5%. The results of this testing process were 78.5% of precision value, 78% of recall and 79% for f-measure

Implementation Of Docker Container On Local Network By Applying Reverse Proxy

Virtualization is an implementation of a software. Virtualization technology has changed the direction of the computer industrial revolution by reducing capital costs and operating costs. The availability of a virtualization will also increase the availability of higher services and data protection mechanisms. Docker is configured to create multiple containers on a network, each container containing one image. The three containers will be created in one compose where each container is connected to each other for WordPress configuration and two composers will be created. Furthermore, from each compose a reverse proxy configuration is carried out which aims to set a different domain address. Lighten computer performance and can reduce the required storage so as to make hosting more effective and efficient. Containers also provide a security advantage over complete control over management running on separate, isolated containers.

Edmodo E-Learning Analysis using End-User Computing Satisfaction Method (Case Study: Senior High School 1 Sampang)

The development of information technology that continues to grow in all aspects of life which makes human work more efficient and effective. The current COVID-19 pandemic is one of the conditions in which people's activities are hindered and limited in their daily activities. The application of Information Technology is a solution that can be applied for the sustainability of community activities in the midst of this pandemic, one of which is the education sector. Edmodo's E-Learning system is one of the most widely used e-learning systems for the teaching and learning process. The application of e-learning certainly changes the way students learn from offline to online. EUCS (end-user computing satisfaction) is one of the methods used to analyze user satisfaction of an information system, where this method has 5 variables, namely content, accuracy, format, ease of use, and timeliness. The case study of Senior High School 1 Sampang will be the object of this research, where the EUCS method is used to measure the satisfaction of end users of the Edmodo E-Learning system. Data was collected by distributing questionnaires to active students at Senior High School 1 Sampang by obtaining a sample of 80 respondents. From the results of the research conducted, it was found that the EUCS method can be applied to the Edmodo E-Learning system at Senior High School 1 Sampang because the data obtained from the respondents was declared valid and reliable so that it can be relied upon in measuring end user satisfaction with the Edmodo E-learning system, while the factors - the factors of the EUCS method that affect the satisfaction of the end user of the Edmodo E-Learning system at Srnior High School 1 Sampang are the content and accuracy factors.

Battery Power Control and Monitoring System with Internet of Things Technology

Along with the development of battery/ACCU technology, the ACCU charging and loading system has also developed manually or automatically. In this paper, the results of research on the manufacture of tools that function to monitor and control the charging and loading of electrical power will be explained from batteries that are burdened with lights and DC motors through a control panel or android smart phone with IoT technology. This tool is designed to monitor 2 ACCUs, namely ACCU1 and ACCU2. ACCU1 is loaded with lamp and parallel with DC motor load while ACCU2 is loaded with DC motor and parallel with lamp load. If ACCU1 and ACCU2 are full, ACCU1 is loaded with only one lamp and ACCU2 is only loaded with DC motor. For example, ACCU1's electrical power is still within the limits that can be loaded with lights and DC motors while ACCU2 can't be loaded because electric power is not capable of being loaded with DC motors, the DC motor's load will be transferred to ACCU1. So that ACCU1 is loaded with DC lamps and motors and ACCU2 is charging the electricity, as well as for the opposite condition. The design of this tool uses the main component of the ESP32 microcontroller, where this component has been integrated with the wifi module and other supporting circuit modules, namely the current sensor circuit, voltage sensor and OLED display. The software design consists of a program code designer for hardware using C++ and an android smart phone application design using MIT App Inventor. The results of this study are in accordance with the design specifications, namely the device can be used to control charging and loading as well as monitor ACCU1 and ACCU2 electrical power on OLED screens and on Android smart phones screens

Traffic Sign Detection Using Region And Corner Feature Extraction Method

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.

Evaluation of the Hospital Information System (HIS ) using EUCS and PIECES Methods on the Medical Record Section of RSUD dr. Haryoto Lumajang

The use of HIS itself in Indonesia is 82.21% of hospitals have adopted HIS and only 15.79% have not adopted HIS. However, the use of SIMRS still uses paperless medical records because there are still several aspects of SIMRS that need to be developed according to user needs. The purpose of this study was to evaluate HIS using the PIECES and EUSC methods. The PIECES method is used to evaluate based on system performance consisting of performance, information, economy, control, efficiency and service variables. The EUSC method is used to evaluate user satisfaction using content, format, acurancy, timeliness and easy of use variables. This type of research is quantitative descriptive and analytic with a sample of 45 people. Descriptive data processing using scoring and analytical using chi square test. Descriptive data processing using scoring and analytical using chi square test. The results of descriptive research using the PIECES method: the performance variable is 69.0%, the information variable is 70.18%, the economy variable is 67.3%, the control variable is 71.4%, the efficiency variable is 72.3%, the service variable is 73. ,2% and all variables included in the good category. The results of analytical research using the End User Computing Satisfaction method found that there are 4 variables that affect user satisfaction, namely content, format, timeliness and easy of use, while the acurancy variable does not affect HIS user satisfaction.

Brain Tumor Classification with Hybrid Algorithm Convolutional Neural Network-Extreme Learning Machine

Brain tumor is a disease that attacks the brains of living things in which brain cells grow abnormally in the area around the brain. Various ways have been done to detect this disease, one of which is through the anatomical approach to medical images. In this study, the authors propose a Convolutional Neural Network (CNN)-Extreme Learning Machine (ELM) hybrid algorithm through Magnetic Resonance Imaging (MRI). ELM was chosen because of its superiority in the training process, which is faster than iterative machine learning algorithms, while CNN was chosen to replace the traditional feature extraction process. The result is CNN-ELM, which has 8 filters in the convolution layer and 6000 nodes in the hidden layer, has the best performance compared to CNN-ELM another model which has different number of filters and number of nodes in the hidden layer. This is evidenced by the average value of precision, recall, and F1-score which is 0.915 while the accuracy of the test is 91.4%.

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method

In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used four different machines learning algorithms such as KNN (Neighbor K-Nearest), Naïve Bayes, Decision Tree, Random Forest plus two ensemble technique namely stacking and bagging. Results are expressed in terms of classic metrics and algorithms that produce the best result for the available data sets is the Random Forest classifier. It reveals the best withdrawals (0,88) as good as the stacking and bagging method with the same value

Imam Safii Heart Disease Classification using Gain Ratio Feature Selection with Hidden Layer Modification in Extreme Learning Machine

Heart disease is a non-communicable disease that causes a high mortality rate and is still a problem both in developed and developing countries. This disease often occurs because of the narrowing of blood vessels which causes the functioning of the heart is disturbed. The number of cases of heart disease in Indonesia is still quite high, making medical staff require a fairly in diagnosing the patient's conditional. The research proposed to implement Gain Ratio in selecting the most important feature that influences heart disease and building the classification models based on the modification of hidden layer weight on Extreme Learning Machine. The research collected the heart disease dataset which was obtained from Kaggle UCI Machine Learning consist of 1.025 samples, 14 attributes, and 2 labels. The data preprocessing include using data cleaning and normalization to find out dirty data or missing values. The experiment reported that Gain Ratio succeeds to generate the attribute ranking of heart disease dataset, then Gain Ratio score was added to the weighting of the hidden layer input on learning methods. The research used various validation sampling using the splitting test between training data and testing such as 70:30, 80:20, 90:10%, and set up 1500 hidden layers. The accuracy average performance of Extreme Learning Machine with modification using Gain Ratio reached 100% for the training phase and 97.67% for the testing phase. Keyword: Heart Disease, Gain Ratio, Modification, Classification, Extreme Learning Machine

Maturity Level for IT Implementation Measurement Focus on Business Goal 14 and 15 at Diskominfo Province

How Information Technology and Information System (IT/IS) has been implemented at an organization or institution needs to measure to evaluate IT/IS implementation. Nowadays, IT is compulsory in business processes. It helps the employee to handle some of their duty. But it needs to measure to monitor how IT/IS aligns with the business. So, that management could decide whether IT/IS is maintained or need to be upgraded or else. There are many ways to measure IT/IS implementation; this article will discuss the maturity level based on Cobit 4.1. It will measure per level from level 0 to level 5. The scope of this research is at business goal 14 and business goal 15 and a case study of this research in Diskominfo at one of the provinces in Indonesia. The result of this research is the maturity level of Business Goal 14, and Business Goal 15 is in Level 3, it means that Define Process.