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All Journal Indonesian Journal of Applied Statistics International Journal of Computing Science and Applied Mathematics-IJCSAM
Nabila Churin Aprilia
Universitas Sebelas Maret
Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Mathematics

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Analisis Cluster Intensitas Kebencanaan di Indonesia Menggunakan Metode K-Means Hafiz Yusuf Heraldi; Nabila Churin Aprilia; Hasih Pratiwi
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34911

Abstract

Indonesia is one of the most prone countries to natural disasters in the world because of the climate, soil, hydrology, geology, and geomorphology. There are many different natural disasters, but the three most common natural disasters in Indonesia are flood, landslide, and tornado. This research aimed to cluster the provinces in Indonesia based on the flood, landslide, and tornado’s intensity in 2018. The results of clustering by K-Means method in this research divided the provinces in Indonesia into four clusters. The second cluster contained West Java, Central Java, and Bali, the third cluster contained DKI Jakarta, the fourth cluster contained DI Yogyakarta, and the first cluster contained the other 29 provinces. The result of this research hopefully can help the government in order to make decision and improve the natural disaster management system, such as preparedness, disaster response, and disaster recovery based on the most common disaster in each province. Furthermore, the society is expected to be more aware on natural disaster management based on the most common natural disaster in province that they lived.Keywords : natural disaster, cluster, k-means
Combined Model of Markov Switching and Asymmetry of Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity for Early Detection of Financial Crisis in Hong Kong Sugiyanto Sugiyanto; Sri Subanti; Isnandar Slamet; Etik Zukhronah; Irwan Susanto; Winita Sulandari; Nabila Churin Aprilia
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 10 No. 2 (2024)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v10i2.4541

Abstract

The financial crisis in Hong Kong occurred in 1997 and 2008. To prevent a crisis or reduce the impact of a crisis, action is needed through early detection of the crisis using export indicator. The combination of Markov Switching and Asymmetric Generalized Seasonal Autoregressive Moving Average Conditional Heteroscedasticity (MS-AGSARMACH) models explains the crisis well. The results show that the MSAGSARMACH(2,1,1) model can explain past and future crises well.
Co-Authors Hafiz Yusuf Heraldi Irwan Susanto Isnandar Slamet Pratiwi, Hasih Sri Subanti Sugiyanto Sugiyanto Winita Sulandari Zukhronah, Etik