Bengkulu Province, Indonesia, which lies in two active faults, Semangko fault and Mentawai fault, is an area that has high seismic activity. As earthquake-prone area, the characteristic of each earthquake in Bengkulu Province needs to be studied. This paper presents the earthquake epicenter clustering in Bengkulu Province. Tectonic earthquake data at Bengkulu Province and surrounding areas from January 1970 to December 2015 are used. The data is taken from single-station Agency Meteorology, Climatology and Geophysics (BMKG) Kepahiang Bengkulu. K-Means clustering using Euclidean distance method is used in this analysis. The variables are latitude, longitude and magnitude. The optimum number of cluster is determined using Krzanowski and Lai (KL) index which is 7. The analysis for each clustering experiment with variation number of cluster is presented.

Today Indonesia is experiencing health problems that are also being faced by all countries in the world, namely Covid-19. Jakarta, the capital of the state of Indonesia, is one of the provinces that has been the epicenter of the Covid-19 cases. Aim of study is to determine dependency between Covid-19 and maximum temperature in Jakarta, Indonesia. Data of Covid-19 cases used are daily cumulative cases, new cases, and deaths. The correlations used are Pearson, Spearman, and Kendall. The correlation coefficient only provides information on the measure of the two variable relationship and does not show the structure of dependency between these variables. One of the methods used to see the dependency structure between variables is copula. One of the copula that is widely used is the clayton copula because of its flexible characteristics. Meanwhile, to see the dependency structure between variables will be used the Copula method from Clayton Copula. The results show that maximum temperature is significantly associated with the Covid-19 pandemic. Based on clayton copula model, the small parameters indicate small dependencies between Covid-19 and maximum temperature.

This study provides an overview in combining spatial analysis and time series analysis to model the frequency of earthquake. The aim of this research is to apply the spatial statistical analysis and time series analysis in estimating semivariogram parameters for the next four steps. The data in this study is secondary data that has been validated based on sources that publish parameters of earthquake events. Looking at the characteristics of the earthquake frequency frequency data, there are spatial and time elements. The method used in this research is interpolation kriging and Autoregressive Moving Average (ARMA) model. The semivariogram models used in kriging interpolation are: Spherical, Exponential, Gaussian, and Linear. The parameters of the semivariogram model are modeled using ARMA time series analysis adjusted to the model diagnostic results. To measure of fit model is used Mean Square Error (MSE). The result of research is a suitable semivariogram model to be applied in the modeling of earthquake events is the Spherical model. While each parameter is estimated using ARMA model (2,2) with different coefficient estimation value.

Hujan adalahn unsur iklim yang sangat penting karena curah hujan berpengaruh terhadap perubahan iklim dan iklim berpengaruh terhadap banyak sektor seperti pertanian dan perikanan. Hal ini menjadikan permodelan curah hujan sangat penting untuk dikaji. Kota Bengkulu terdiri dari dua musim (Hujan dan Kemarau) dan juga memiliki cuaca yang sangat cepat berubah karena letak geografis Bengkulu yang berbatasan dengan Samudra Hindia yang berakibat jika terjadi tekanan rendah di Samudra Hindia maka Bengkulu akan mengalami hujan yang tinggi. Curah hujan yang terdiri dari dua musin dan terjadi secara berulang, sehingga curah hujan termasuk kedalam pola monsunal yang dicirikan oleh tipe curah hujan yang bersifat unimodial (satu puncak musim hujan). Jika menggunakan data curah hujan masa lalu maka metode yang tepat untuk memodelkan curah hujan adalah metode Seasonal Autoregressive Integrated Moving Average (SARIMA). Tujuan yang ingin dicapai dalam penelitian ini adalah mengetahui model SARIMA yang terbentuk pada data curah hujan mingguan Kota Bengkulu. Hasil yang diperoleh model SARIMA (0,1,1)(0,1,1)12 merupakan model curah hujan bulanan di Kota Bengkulu yang terbaik dengan AIC 207,40 dan SBC 215,06. Model ini selanjutnya dapat digunakan untuk peramalan.