STANDS: Journal of Statistics and Data Science

PANEL DATA REGRESSION ANALYSIS ON FACTORS INFLUENCING DIARRHEA CASES IN KENDARI CITY

Elvianis — 2023-07-30

Diarrhea is a disease of the digestive system which is characterized by defecation more than three times a day. Diarrhea in Kendari City is quite high, in 2017 it reached 3,328 cases, in 2020 it reached 6,582 cases and remains problematic to public health. This paper aims to obtain a panel data regression model to determine the factors that influence diarrhea cases in Kendari based on 2017-2020 dataset. In the panel data regression model, random effect model is introduced when conducting the generalized least square estimate. The results of the study show that the percentage of the population with access to proper sanitation facilitiesand the percentageof sub-districts with carrying out community-based total sanitation are to have significant effectson diarrhea casesin Kendari.

NEGATIVE BINOMIAL REGRESSION MODELING ON INCIDENT NUMBER OF POOR NUTRITIOUS UNDER-FIVE CHILDREN IN SOUTHEAST SULAWESI

Wa Ode Ferliana — 2023-07-30

The objective of this study is to determine the model using negative binomial regression on incident number of malnutrition under-five children in Southeast Sulawesi. In 2021 in Southeast Sulawesi there were 471 cases of malnutrition. To overcome the problem, a proper modeling is needed to find out the factors influencing the occurrence of malnutrition cases for under five children in Southeast Sulawesi. Since the data of malnutrition cases is discrete and to have overdispersion, we apply the use of negative binomial regression. The results of the analysis in this study show that percentage of coverage of vitamin A has a negative and significant effect on the number of malnutrition cases of under five children in Southeast Sulawesi.

CLUSTER ANALYSIS ON SENIOR HIGH SCHOOLS OF KENDARI USING SIMILARITY WEIGHT AND FILTER METHODS

Felia Baharu — 2023-07-30

In this paper, we aim to apply clustering method on senior high schools of Kendari using the
Similarity Weight and Filter Method (SWFM). This clustering method allows us to cluster
objects based on mixed variables, namely numerical and categorical variables. Before
implementing SWFM, we applied Wardhierarchical method on numerical variables and kmodes
on categorical variables. The results show that the schools can be grouped in 5 clusters.
Among these clusters, Cluster 1 is contained with state-owned and accredited highest rank
schools, while others are mostly private and accredited low rank schools.

MODELING THE NUMBER OF DIARRHEA INCIDENTS IN KENDARI USING GENERALIZED POISSON REGRESSION

Angga Saputra — 2023-07-30

The number of diarrhea incidents in Southeast Sulawesi Province, Indonesia, remains high, especially in the capital city, Kendari.As we can see in 2019 and 2020, there were 5.559 and 6.582 cases of diarrhea, respectively. For this reason, this article aims to address the problem by proposing the application of GeneralizedPoisson Regression (GPR) model. The adoption of GPR model is due to the existing of overdispersion on the dataset. The results of this research show that the population density variable is significant in the data analysis. Which means that the main factor results in high number of diarrhea incidents in Southeast Sulawesi Province is the population density

GENERALIZED POISSON REGRESSION MODEL ON INFANT MORTALITY RATE IN SOUTHEAST SULAWESI

Nanang Setia — 2023-07-30

The infant mortality rateis an indikator of a country’s health status and reflects on life
expectancy, welfare and quality of life of a community. Infant mortality rateofSoutheast
Sulawesi in 2020 experienced a drastic increase to reach extremly high, namely 503 cases. To
address this problem, we propose Generalized Poisson Regression since the dataset is discrete
and to have overdispersion. The results of the analysis in this study indicate that the variables
that influence Infant Mortality in Southeast Sulawesi are the number of low birth weight and the
number of toddlers with asphyxia.

LOW BIRTH WEIGHT MODELING WITH LOGISTIC REGRESSION IN CENTRAL BUTON

Amaludin Salam — 2023-07-30

Low Birth Weight (LBW) is a baby's weight at birth less than 2,500 grams. The birth of LBW babies is a major contributor to both short and long term neonatal morbidity and death. The number of LBW in Central Buton Regency reported in 2021 is getting worse than in 2020. Therefore, Logistic Regression Analysis in needed to identify and model the factors that influence LBW cases in Central Buton district. The data used in this study is secondary data on the population of birth weight in infants during the period January – June 2022. Data was obtained from the Medical Record of Puskesmas in Central Buton district, Mawasangka district area and Gu district area. The number of samples in the study was 327 infants. The model produced in this study is ĝ(x) = -37535 + 2,3562X1 + 1,2097X2 + 1,4590X4. From this model, it is known that the factors that significantly affect LBW in Buton Tengah Regency are gestational age (X1), Hb levels (X2) and pregnancy complications (X4) with an accuracy value of 90,51%.
Keywords: Low Birth Weight, Logistic Regression Analysis