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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Prosiding Seminar Nasional Official Statistics
Akhmad Fatikhurrizqi
Politeknik Statistika STIS
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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COMPARISON OF REGIONAL CLUSTER ANALYSIS ACCORDING TO INCLUSIVE DEVELOPMENT INDICATORS IN JAVA ISLAND 2018 BETWEEN HIERARCHICAL AND PARTITIONING CLUSTERING STRATEGIES Akhmad Fatikhurrizqi; Arie Wahyu Wijayanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 2 (2021): JITK Issue February 2021
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1401.374 KB) | DOI: 10.33480/jitk.v6i2.1939

Abstract

Gross Domestic Product (GDP) is one of the most common indicators to reflect a nation’s development. Indonesia's GDP has an average growth rate of 5 percent over the 2015-2019 period with the highest growth rate occurred in 2018. Furthermore, the provinces in Java Island contributed the most out of any province to Indonesia’s GDP in that year. However, the development in Java Island still has several issues, such as high poverty, unequal income distribution, and high unemployment. This problem indicates that the economic growth in Java Island has not been inclusive concerning development. This study aims to group regencies/municipalities in Java Island based on indicators of inclusive growth. These indicators refer to McKinley (2010) in a journal published by the Asian Development Bank (ADB). The cluster methods used to represent each hierarchical and partitioning are the Agglomerative Nesting (AGNES) and K-Means methods. The results of this study show that there are 3 clusters based on the AGNES method and 4 clusters based on the K-Means method. Clusters with good inclusive growth characteristics are dominated by municipality areas based on the K-Means method. Meanwhile, the clusters with low inclusive growth characteristics are dominated by regencies/municipalities on Madura Island based on the K-Means and AGNES methods. The comparison of the appropriate methods in this study based on the silhouette value is the AGNES method.
PEMODELAN JUMLAH KASUS COVID-19 DI INDONESIA DENGAN PENDEKATAN REGRESI POISSON DAN REGRESI BINOMIAL NEGATIF Nadhifan Humam Fitrial; Akhmad Fatikhurrizqi
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (207.408 KB) | DOI: 10.34123/semnasoffstat.v2020i1.465

Abstract

The COVID-19 pandemic has spread throughout the world, including Indonesia. It is necessary to know the various factors that affect the spread of COVID-19 cases so that both the government and the community can make prevention and response efforts so that this pandemic does not spread further. This study aims to model the number of COVID-19 cases in Indonesia and then determine the variables that have a significant effect on them. The model used is Poisson regression and Negative Binomial regression. The two models were chosen because they are a model that is often used for count data such as the number of COVID-19 cases. Then from the two models, the best model will be selected along with the variables that have a significant effect on the number of COVID-19 cases in Indonesia. The unit of analysis in this research is all provinces in Indonesia which consists of 34 provinces. The response variable in this study is the cumulative number of COVID-19 cases in Indonesia on April 9, 2020 which were compiled from Gugus Tugas Percepatan Penanganan COVID-19. This date was chosen because it was the date before the implementation of the PSBB policy for the first time in Indonesia and this study did not take government intervention variables in modeling the cumulative number of COVID-19. The predictor variables in this study were population density, the percentage of the elderly population, the percentage of households with access to improper sanitation, and the percentage of illiteracy rates for the population aged 15 years and over. Based on the AIC value, the Negative Binomial regression is better used to model the number of COVID-19 cases in Indonesia than the Poisson regression. In the Negative Binomial regression model, the population density variable and the percentage of the elderly population have a positive and significant effect on the number of COVID-19 cases in Indonesia.
Co-Authors Arie Wahyu Wijayanto Nadhifan Humam Fitrial