Android Based Expert System Application for Diagnose COVID-19 Disease: Cases Study of Banyumas Regency

Since being confirmed by WHO, the status of COVID-19 outbreak has become a global pandemic, the number of cases has been confirmed positive, cured, and even death worldwide. Artificial intelligence in the medical has given rise to expert systems that can replace the role of experts (doctors). Tools to detect someone affected by COVID-19 have not been widely applied in all regions. Banyumas Regency, Indonesia is included confirmed region of COVID-19 cases, and it’s difficult for someone to know the symptoms that are felt whether these symptoms include indications of someone ODP, PDP, positive, or negative COVID-19, and still at least a referral hospital handling COVID-19. Expert system with certainty factor can help someone make a self-diagnose whether including ODP, PDP, positive, or negative COVID-19. This expert system provides ODP diagnostic results with a confidence level of 99.96%, PDP 99.99790%, positive 99.9999997%, negative 99.760384%, and the application runs well on Android OS

Supplier Selection Very Small Aperture Terminal using AHP-TOPSIS Framework

There are several methods of decision making VSAT IT goods suppliers such as: Promethee, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytical Hierarchy Process (AHP). Decision-making in the selection of the best suppliers, we have the basis of assessment criteria, and we will also be faced with more than one alternative. If alternatives are only two, maybe still easy for us to choose, but if the alternative is a lot of choice, it is quite difficult for us to decide. Analytical Hierarchy Process (AHP) is a technique that was developed to help overcome this difficulty, because the Analytical Hierarchy Process (AHP) is a form of decision-making model with many criteria. One of the reliability of the Analytical Hierarchy Process (AHP) is able to perform simultaneous analysis and integrated between the parameters of qualitative or quantitative. In this study the authors use six criteria and alternatives 6, the results of these alternatives will be obtained perangkingan alternative used as a reference supplier selection VSAT IT goods company Total EP Indonesie

Applied Exponential Smoothing Holt-Winter Method for Predict Rainfall in Mataram City

Weather conditions in the city of Mataram tend to be erratic and difficult to predict, such as the condition of rainfall data in 2018 which changes over a certain period of time so that the weather is difficult to predict accurately. In this study, we propose the Exponential Smoothing Holt-Winter method to forecast rainfall in the city of Mataram, so that it can be a decision support for various interested sectors. This method has been tested using secondary data from the Mataram City Central Bureau of Statistics for the period January 2014 to 2018 and evaluated using Mean Absolute Deviation (MAD), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results of this study indicate that using the Exponential Smoothing Holt-Winter method yields better results, each of which is MAPE 142.3, MAD 95.6 and MSD value 24988.7 and the data smoothing value is obtained for the smallest combination value of α 0.2, β 0.1, and γ 0.1. It can be concluded that the proposed method can provide better information and can be used to predict rainfall in Mataram City for the next 12 periods.

Modeling Spatial Error Model (SEM) On Human Development Index (IPM) In Central Java 2018

The Human Development Index (HDI) is a human development index that is used to achieve the development outcomes of a region. HDI is formed by 3 basic dimensions, namely the health dimension as seen from the indicator of life expectancy at birth, the dimension of knowledge seen from a combination of indicators of average length of schooling and expectation of school years and dimensions of decent living standards as seen from the indicator of average per capita expenditure has been adjusted. The development of HDI in Central Java shows an increase every year. In 2018 the HDI figure for Central Java Province reached 71.12% and increased by 0.6% from the previous year. This is because the large HDI figures in an area are influenced by the large HDI numbers in adjacent areas. The location / area factor is thought to have a spatial dependence effect on the HDI figure. This problem can be overcome by using spatial regression by including the relationship between regions into the model. The spatial regression approach used in this study is the Spatial Error Model (SEM). The weighting matrix used in this study is Queen Contiguity (intersection between sides and corners). This study provides results that the variables that significantly influence HDI are poverty and school enrollment rates.

Modeling of Tuberculosis Case In Central Java 2018 With Three Knot Point

Tuberculosis is a contagious disease caused by infection with the bacteria Mycobacterium Tuberculosis or known as Acid-Resistant Bacteria (BTA). Central Java is one of the provinces that has a high number of tubuerculosis cases in Indonesia. In 2018, Central Java was in second place after West Java in the highest number of Tuberculosis cases in Indonesia with the number of Tuberculosis cases of all types of 67,941 cases. Many variables can affect the number of TB cases. Therefore, a study was conducted in the form of modeling to determine the variables that affect the number of tuberculosis cases in Central Java. Based on data obtained from the Central Java Provincial Health Office in 2018, it shows that the pattern between the number of tuberculosis cases and the variables that are thought to influence it is not linearly related, then a spline regression approach is carried out. The results of this study indicate that the best spline regression model is to use three point knots with significant variables, namely population density and malnutrition. The value of 𝑅2 obtained is 54.6%.

Naïve Bayes Algorithm for Classification of Student Major’s Specialization

Majors are important in determining student specialization. If there is an error in the direction of the student, it will certainly affect the education of subsequent students. In SMA Negeri 1 Kampar Timur, there are two majors, namely Natural Sciences and Social Sciences. To determine these majors, it is necessary to reference the average value of student grades from semester 3 to semester 5 which includes the average value of Islamic religious education, Indonesian, Citizenship Education, English, Natural Sciences, Social Sciences, and Mathematics. Naive Beyes algorithm is an algorithm that can be used in classifying majors found in SMA Negeri 1 Kampar Timur. To determine the classification of majors in SMA Negeri 1 Kampar Timur, training data and test data are used, respectively at 70% and 30%. This data will be tested for accuracy using a confusion matrix and produces a fairly high accuracy of 96.19%. With this high accuracy, the Naive Bayes algorithm is very suitable to be used in determining the direction of students in SMA Negeri 1 Kampar Timur.

Integration of GSTAR-X and Uniform location weights methods for forecasting Inflation Survey of Living Costs in Central Java

Inflation is a tendency to increase prices of goods and services that take place continuously. Inflation is a monthly time series data that is thought to be influenced by location elements. Modeling for inflation forecasting that involves time and location (spatio temporal) can use the Generalized Space Time Autoregressive (GSTAR) method. To increase accuracy in modeling and forecasting, the GSTAR model was developed into the GSTARX model by involving exogenous variables. Exogenous Variavel used in GSTARX modeling for forecasting Inflation is a variation of the Eid calendar. This GSTARX modeling is applied for inflation forecasting in six cities Cost of Living Survey (SBH) in Central Java, namely Cilacap, Purwokerto, Semarang, Kudus, Magelang and Surakarta. The purpose of this study is to get the best GSTARX model for inflation forecasting for six SBH cities in Central Java. The selection of the best model from the GSTARX method is seen with the smallest RMSE value of each model. Obtained that the GSTARX model with uniform weights is the best model because it has a smaller RMSE compared to the GSTARX model with inverse distance weights, the RMSE values are 0.6122 and 0.6137, respectively. It can be concluded that the GSTARX method with Uniform weighting can provide better performance and can be used to predict the inflation of the six SBH cities in Central Java in the next 12 periods.

Poverty Mapping in Central Java Province Using K-Means Algorithm

Prosperity has a relative, dynamic, and quantitative meaning. Until now, the formula is not finished because it will continue to grow along with the times. Public welfare is a condition where all citizens are always in a condition that is completely adequate in all their needs. Poverty in Central Java Province is still above national poverty. Poverty grouping is one way to focus on the people's budget in each region so that they can take development policies and strategies that are right on target and effective. In this study, the proposed K-means algorithm for classifying poverty in Central Java is based on poverty indicators. The results of the first cluster study consisted of 22 districts / cities with the category of not poor, the second cluster consisted of 13 districts / cities that were categorized as poor.

Evaluation of Optima Regional Health Information System with HOT-Fit on Technology Aspects Approach in Johar Baru Health Center Jakarta

The Information technology development has affected various sectors, including health services. The several technologies have been used to improve health facilities performance. At Johar Baru Health center, central Jakarta, SIKDA (Sisitem Informasi Kesehatan Daerah) Optima application has been applied. Meanwhile, the implementation of SIKDA Optima is not as good as expected. There still many disruptions during the use of this application such a delay service and delivery of report was not in a real time, therefore an evaluation is needed. The purpose of this study was to determine the quality of system, information, and service which is affecting the satisfaction of SIKDA Optima users at Johar Baru Health Center, Central Jakarta. This study used a quantitative approach with observational survey and cross-sectional design. The population in this study was 98 persons and the sample were 79 users of SIKDA Optima, consist of 19 doctors, 22 nurses, 17 midwives, 9 pharmacies, 2 medical recorder and 10 administration staffs. Data analysis was performed using multiple linear regression. The results of multiple linear regression test showed that the user satisfaction of SIKDA Optima = -3.832 + 0.549 (KS) + 0.757 (KI) + 0.359 (KL) with a p-value of KS 0.001<0.05), p-value KI 0,000 <0,05), and the p-value of KL is 0.009 <0.05. The conclusion of this study is the quality of system, information, and services that is used at Johar Baru Health Center have a significant influence on the satisfaction of SIKDA Optima users.

Binary Logistic Regression Analysis of Variables That Influence Poverty in Central Java

The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.