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Erdien, Fareka
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Clustering Municipality of Dengue Hemorrhagic Fever Typologies in Central Java Erdien, Fareka; Sumargo, Bagus; Nazhiifah, Nisriina; Kirana, Siti Julpia; Siregar, Dania; Mulyono
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08108

Abstract

Cases of dengue hemorrhagic fever (DHF) generally occur in areas with high temperatures. Central Java Province, Indonesia is one of the regions that has high temperatures, making it vulnerable to dengue cases. The study aimed at grouping DHF endemic areas in Central Java needs to be done to assist the government in determining policies to control or prevent DHF. The cluster analysis method used in this study is Average Linkage. The results showed that there were 3 clusters formed. Cluster A is a cluster with the characteristics of having the highest average percentage of households that have access to safe drinking water. Cluster B is the cluster with the highest average number of protected springs. While cluster C is dominant in 4 factors with the highest average, namely the percentage of households that behave in a clean and healthy life, the percentage of healthy homes, the number of Polindes (Village Maternity Hut), and the percentage of households that have access to proper sanitation. Clusters A and B are clean water type and Cluster C is a sanitation type, where clean water and sanitation are both indicators of environmental health. Therefore, environmental health is closely related to the presence of dengue fever in a community environment. The determination of three clusters was based on the chosen method and criteria. Other methods or criteria might suggest a different optimal number of clusters. The findings are specific to Central Java Province and may not be generalizable to other regions with different environmental and social contexts. In this case, it is necessary to pay attention to the community for environmental health in order to overcome or prevent the occurrence of DHF. where clean water and sanitation are both indicators of environmental health. Therefore, environmental health is closely related to the presence of dengue fever in a community environment.
Factor Analysis of Dengue Hemorrhagic Fever Nazhiifah, Nisriina; Sumargo, Bagus; Erdien, Fareka; Kirana, Siti Julpia; Rahayu, Widyanti; Mulyono
Jurnal Statistika dan Aplikasinya Vol. 8 No. 1 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08107

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus. DHF cases have always been a serious problem every year in Central Java. This study aimed to determine the factors that cause Dengue Hemorrhagic Fever (DHF) in the province of Central Java because cases of DHF in the region become a serious problem every year. The method used for this research is Principal Component Analysis and Factor Analysis using secondary data from the Central Java Provincial Health Office in 2018. The results of the analysis show that 3 factors are causing DHF, namely the population participation factor in health, sanitation factors, and clean drinking water factors. This shows the importance of environmental education to increase population awareness in terms of healthy living and local government intervention is needed in environmental health projects, namely sanitation and clean drinking water This research only uses seven variables that are considered relevant, other variables that may also have an influence are not included in this analysis. This research focuses on a particular year that shows a decreasing trend in cases. This approach offers a fresh and distinct perspective on understanding the dynamics of dengue fever and the factors that contribute to its reduction.
FORECASTING THE PRICE OF CURLY RED CHILI PEPPERS IN EAST JAVA PROVINCE USING ARIMA MODEL WITH ITERATIVE OUTLIER DETECTION PROCEDURE Erdien, Fareka; Rahayu, Widyanti; Sumargo, Bagus; Wulansari, Ika Yuni; Ali, Didiq Rosadi
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09208

Abstract

Curly red chili is one of the vegetables with high economic value because it plays a role in supporting the food industry and meeting domestic needs. Fluctuations in the price of curly red chili peppers can change at any time, requiring forecasting to prevent losses for economic actors. This research aims to get the best model for forecasting and determine the accuracy of forecasting the price of curly red chili. The Autoregressive Integrated Moving Average (ARIMA) model is one method that can be used for forecasting with limitations requiring data that must be stationary. Outliers in the ARIMA model affect the autocorrelation structure of a time series so that the estimated values of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) become biased so that forecasting with the ARIMA model is less accurate and requires handling outliers in the form of outlier detection, one of which is an iterative procedure. From this study, it was found that the ARIMA(0,2,3) model with outlier detection was the best model for forecasting. Forecasting tends to show a downward trend with an accuracy level of MAPE value of 4.612, which means that the model is very good for forecasting.
Co-Authors Ali, Didiq Rosadi Bagus Sumargo, Bagus Kirana, Siti Julpia Mulyono Nazhiifah, Nisriina Siregar, Dania Widyanti Rahayu Wulansari, Ika Yuni