This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.
Balikpapan City Manpower Office as 3 (three) fields, and one of them is the Field of Placement and Job Expansion. The Job Placement and Expansion Sector has annual profile data which is recorded at the Disnaker Balikpapan. The profile data used for this research is job seeker data, job vacancies, job placement (received) to the level of education. The method used for this research is Profile Analysis, which is a statistical method for analyzing data with more than two dependent variables together. This profile analysis aims to determine the characteristics of two independent populations. One of the characteristics of the population is to test the hypothesis, the hypothesis test used is the parallel test, coincident test, and similarity (level test). for each population. So that the Profile Analysis method is used to determine the characteristics of the population in the Job Placement and Expansion Sector at the Balikpapan City Manpower Office.
Rubber commodity is one of the essential agricultural sectors in the City of Balikpapan. Rubber plantation has become a leading product to boost the economic growth in Balikpapan from the agricultural sector. However, this commodity is vulnerable through the changes of rainfall index since less rainfall can decrease the amount of latex in a rubber tree. This unexpected event will make a financial loss to farmers because of delaying the cultivation time. Therefore, the crop insurance for rubber farmers should be discovered. This study applied the Burn Analysis method to determine some classes of rainfall index and calculating the crop insurance premiums based on Black Scholes methods. The lowest class of rainfall index is 1264.36 mm with the crop insurance premiums at this class is Rp. 521,482.73. Otherwise, the highest rainfall index is 1761,36 mm whereas the premium level is Rp.568,592.93.
Job seekers are a process that matches jobs to match. If all workers and all types of workers are the same, then all workers are suitable for all types of work, then job search will not be a problem. In this research, an analysis is needed to determine the job seeker who chooses according to the desired job group. By using the analysis of the Pricipal Component Analysis using SPSS, the output is the value of KMO and Barlett's Test, MSA, communality, Total Variance, Scree plot and Matrix Components. By using the Main Component Analysis methodology to reduce/reduce the factors that affect job seekers according to groups from 9 existing variables, the results of the reduction are obtained.
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