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All Journal Jurnal Varian
Bobby Poerwanto
Universitas Negeri Makassar, Indonesia
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels Bobby Poerwanto
Jurnal Varian Vol 6 No 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i2.2756

Abstract

In solving economic problems, the government has implemented several development policies. However, this policy is considered to be too centered on big cities. So, through this research it is hoped that it can provide an overview related to regional groups that fall into the poorer category so that the government can also provide accelerated development policies that are oriented towards improving the economy of residents in the area. This study aims to determine the results of classifying district/city poverty levels in Indonesia as a basis for classification for predictions and to classify district/city poverty levels based on influencing factors. The method used in this study is K-Means Clustering using the poverty depth index and poverty severity index variables, then proceed with using the Backpropagation Neural Network (BNN) algorithm using the GRDP, per capita expenditure, human development index, and mean years of schooling. The results obtained using the K-Means algorithm are that there are 42 districts/cities that belong to cluster 1 where this region has a poverty index depth and severity index value that is higher than the 472 districts/cities in cluster 2. Furthermore, the cluster results are used as response variables for classification with BNN. The accuracy of the model obtained is very high, which is equal to 98.06, so the model is very feasible to be used as a poverty rate prediction model based on the variables used.
Survival Analysis with Cox Proportional Hazard Model for Tuberculosis (TBC) Patients Zahratun Nisa; Bobby Poerwanto; Muhammad Fahmuddin Sudding
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2994

Abstract

Survival analysis is a method in statistics which aims to analyze the relationship between time from the beginning of observation until the occurrence of an event (response variable) with factors that have an influence on the event (predictor variables). To determine the relationship between the response variable and the predictor variable, where the response variable is the time until the event occurs, one method that can be used is the cox proportional hazard regression method. The data used in this research is data on hospitalizations of tuberculosis sufferers at Haji Makassar Hospital in 2022 because it has characteristics that are in accordance with the aim of survival analysis, namely to determine the relationship between the life span of TBC patients and the factors that influence TBC disease. The results of the analysis obtained factors that significantly influence the recovery rate of patients with TBC are shortness of breath and smoking habits. The shortness of breath variable has an influence on the recovery rate of TBC patients, namely 0.3506, which means that TBC patients who do not experiencing shortness of breath has a recovery rate of 0.3506 times the likelihood of recovery compared to patients who experience shortness of breath. Variable smoking habit was 0.7367, which means that patients with TBC did not smoking habit has a recovery rate of 0.7367 times recovered compared to patients who had a smoking habit.
Co-Authors Muhammad Fahmuddin Sudding Zahratun Nisa