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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Mathematics and Scientific Computing With Applications
Siahaan, Maharani Putri Adam
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Robust Geographically Weighted Regression Modeling In Cases Of Stunting Toddlers In North Sumatera Utara Siahaan, Maharani Putri Adam; Husein, Ismail
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20934

Abstract

Stunting is a problem related to chronic malnutrition that can be caused by inadequate nutritional intake. Stunting is usually found in toddlers aged 12-36 months which is often not realized because usually the difference between normal children and stunted children is not specifically visible. Therefore, researchers want to know the problem of how to model cases of stunted toddlers using Robust Geographically Weighted Regression in North Sumatra. With quantitative research methods, the results obtained were 33 models of the number of cases of stunted toddlers in North Sumatra which were formed using the Robust Geographically Weighted Regression (RGWR) method with a fixed Gaussian kernel weighting function and gave different results for each Regency /City in North Sumatra. Among them is the RGWR mode in Central Tapanuli = 193.2119 + 1.306099 ???? 1 + 0.013863 ???? 2 + 0.000913 ???? 3 – 0.00469 ???? 4 – 0.051564 ???? 5 + ???? and it is obtained that the level of accuracy of the RGWR model is able to provide better estimation results. This is supported by the MAPE value of the RGWR model of 15%, which is in the range of 10% -20%. So the model used is appropriate and effective in estimating the number of cases of stunting toddlers in North Sumatra.        
APPLICATION OF THE MONTE CARLO METHOD IN PREDICTING THE NUMBER OF BUDGET PROPOSALS ACCEPTED IN NORTH SUMATRA PROVINCIAL HEALTH OFFICE Harahap, Riska; Siahaan, Maharani Putri Adam; Widyasari, Rina
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 1 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v4i1.873

Abstract

A budget is a planning tool regarding future expenditure and revenues, generally prepared for one year. The prediction simulation for approved budget proposals is an estimate of the calculation of the approval rate for approved proposals in the following year. This research uses the Monte Carlo method in solving problems. This method can be used in problems with nonlinear boundary conditions, namely prediction limits.the author uses a quantitative descriptive method, which is a form of research that focuses on the facts and characteristics of the research object by combining related variables. This research uses the Monte Carlo method uses random numbers and probability statistics to solve problems.The data used to predict the approved proposal budget is the budget proposal data that is approved each year. The following is one of the approved proposal data, namely the approved budget proposal data from 2021, 2022 and 2023 budget proposals received using the Monte Carlo Method which has been implemented at the North Sumatra Provincial Health Service with the simulation namely with an average percentage in 2022 of 84% and in 2023 by 76%. So with the successful application of the Monte Carlo Method to predict the number of budget proposals received at the North Sumatra Provincial Health Service for 2024 it will provide convenience for the North Sumatra Provincial Health Service to find out what the predicted number of budget .
APPLICATION OF THE MONTE CARLO METHOD IN PREDICTING THE NUMBER OF BUDGET PROPOSALS ACCEPTED IN NORTH SUMATRA PROVINCIAL HEALTH OFFICE Harahap, Riska; Siahaan, Maharani Putri Adam; Widyasari, Rina
Journal of Mathematics and Scientific Computing With Applications Vol. 5 No. 1 (2024)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v5i1.873

Abstract

A budget is a planning tool regarding future expenditure and revenues, generally prepared for one year. The prediction simulation for approved budget proposals is an estimate of the calculation of the approval rate for approved proposals in the following year. This research uses the Monte Carlo method in solving problems. This method can be used in problems with nonlinear boundary conditions, namely prediction limits.the author uses a quantitative descriptive method, which is a form of research that focuses on the facts and characteristics of the research object by combining related variables. This research uses the Monte Carlo method uses random numbers and probability statistics to solve problems.The data used to predict the approved proposal budget is the budget proposal data that is approved each year. The following is one of the approved proposal data, namely the approved budget proposal data from 2021, 2022 and 2023 budget proposals received using the Monte Carlo Method which has been implemented at the North Sumatra Provincial Health Service with the simulation namely with an average percentage in 2022 of 84% and in 2023 by 76%. So with the successful application of the Monte Carlo Method to predict the number of budget proposals received at the North Sumatra Provincial Health Service for 2024 it will provide convenience for the North Sumatra Provincial Health Service to find out what the predicted number of budget .
Co-Authors Harahap, Riska Ismail Husein, Ismail Widyasari, Rina