Huda Prasetya, Nurul
Unknown Affiliation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

Classification of Pneumonia Risk Factor Characteristics in Toddlers Using Classification and Regression Trees (CART) Case Study Regional General Hospital dr. Tengku Mansur Tanjungbalai Mangunsong, Elvira Yolanda; Huda Prasetya, Nurul; Rakhmawati, Fibri
Quadratic: Journal of Innovation and Technology in Mathematics and Mathematics Education Vol. 1 No. 2 (2021): October 2021
Publisher : Pusat Studi Pengembangan Pembelajaran Matematika Sekolah UIN Sunan Kalijaga Yogyakarta Jl. Marsda Adisucipto, Yogyakarta 55281

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/quadratic.2021.012-02

Abstract

Pneumonia is one of the leading causes of death in children in the world. The World Health Organization (WHO) estimates that this disease causes 16% of deaths in children under 5 years of age worldwide. Pneumonia is included in the top 10 diseases that suffer the most every month at the RSUD dr.Tengku Mansyur Tanjungbalai, throughout 2019 there were 73 children under five years with pneumonia and 5 of them were declared dead without hospitalization. This study is useful to find out the results of the classification accuracy of the classification and regression trees (CART) for risk factors for pneumonia in children under five years. CART classification is done by dividing the total data of under-five patients with the ratio of 15% testing data and 85% learning data. The classification accuracy obtained for the prediction data was 50% with the percentage of sensitivity and specificity of 94,12% and 14,29% respectively.
FORECASTING THE NUMBER OF PASSENGERS FOR THE MEDAN-KUALANAMU TRAIN WITH TIME INVARIANT FUZZY TIME SERIES METHOD Nasution, Pauziah; Huda Prasetya, Nurul; Rakhmawati, Fibri
Journal of Mathematics and Scientific Computing With Applications Vol. 3 No. 2 (2022)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1239.711 KB) | DOI: 10.53806/jmscowa.v3i2.78

Abstract

For forecasting, a forecasting model is needed. Forecasting to find out the increase or even decrease for the departure of Medan - Kualanamu for the next few years. Then the value generated by fuzzy logic is not yes, which has a value of 1, and is not worth 0. Time series is data that is collected from time to time, to describe the development of an activity. From the case of forecasting the number of passengers of the Medan-Kualanamu Train, the author will use the Time Invariant Fuzzy Time Series method. Based on the research, it is obtained that the forecasting of the number of passengers of the Medan-Kualanamu / Kualanamu-Medan train in 2021 is 182,874.9; and 2022 is 266,527,510.6 with AFER is 2,5%.
FORECASTING THE NUMBER OF PASSENGERS FOR THE MEDAN-KUALANAMU TRAIN WITH TIME INVARIANT FUZZY TIME SERIES METHOD Nasution, Pauziah; Huda Prasetya, Nurul; Rakhmawati, Fibri
Journal of Mathematics and Scientific Computing With Applications Vol. 3 No. 2 (2022)
Publisher : Pena Cendekia Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53806/jmscowa.v3i2.78

Abstract

For forecasting, a forecasting model is needed. Forecasting to find out the increase or even decrease for the departure of Medan - Kualanamu for the next few years. Then the value generated by fuzzy logic is not yes, which has a value of 1, and is not worth 0. Time series is data that is collected from time to time, to describe the development of an activity. From the case of forecasting the number of passengers of the Medan-Kualanamu Train, the author will use the Time Invariant Fuzzy Time Series method. Based on the research, it is obtained that the forecasting of the number of passengers of the Medan-Kualanamu / Kualanamu-Medan train in 2021 is 182,874.9; and 2022 is 266,527,510.6 with AFER is 2,5%.