This study uses intraday electricity load demand data from Kuaro Main Gate data in East Kalimantan as the basis of an empirical comparison of Double Seasonal ARIMA models for prediction up to a day ahead. For the purpose of this study, a one-year hourly Kuaro Main Gate data load demand from 1 January 2018 to December 2018 measured in Megawatt (MW) is used. In multiple times of load demand data, in addition to intraday and intra week cycles, and intra year seasonal cycle is also apparent. We extend the Double Seasonal ARIMA methods in order to accommodate the Intra year seasonal cycle. The mean absolute percentage error (MAPE) is used as the measure of forecasting accuracy. A notable feature of the time series is the presence of both an intraweek and an intraday seasonal cycle. We also propose that a Double Seasonal ARIMA model with the one-step-ahead forecast as the most appropriate model for forecasting the two-seasonal cycles Kuaro Main Gate data load demand time series. We use the Statistical Analysis System package to analyze the data. Using the least-squares method to estimate the coefficients in a Double Seasonal ARIMA model, followed by model validation and model selection criteria, we propose the ARIMA (1,1,1)(0,1,1)24(0,1,1)168 within-sample MAPE of 0.000992 as the best model for this study. Comparing the forecasting performances by using k-step ahead forecasts and one-step-ahead forecasts, we found that the MAPE for the one-step ahead out-sample forecasts from any horizon ranging from one week lead time to one month one week lead time are all less than 5%. Therefore we propose that a double seasonal ARIMA model with a one-step-ahead forecast must be considered in forecasting time series data with two seasonal cycles.
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.
Forest fires are adverse events both economically and mentally. Triggers of fires are complex variables so that the spread of fire to date has not been predicted. Modeling is one solution to study the process of spreading fire. The Cellular automota was used to simulate the spread of fire in forest fires. Simulations carried out with simulations carried out by varying the value of burnability. burnProbability is a value that shows the probability of a tree burning. The value of burnability used is 10%, 20%, 30%, 40%, 50%, 60%, 70% and 80%. From each burnProbability value, the percentage of forest that is burned is calculated. The simulation was carried out for 17 iterations for each burnProbability value. The results of the average is the greater the value of burnProbability, the greater the percentage of burned forest. When the burnProbability value of 80% of the burned forest is almost entirely, it is 97.23183%.
Polusi udara yang keluar dan menyebar dari cerobong asap industri dapat menimbulkan berbagai macam penyakit bagi masyarakat. Jangkauan penyebaran polusi udara yang bervariasi dan bergantung pada berbagai faktor seperti kecepatan dan arah angin, ketinggian cerobong, dan konsentrasi polutan perlu mendapat perhatian agar tidak menimbulkan dampak negatif pada masyarakat di suatu pemukiman. Oleh karena itu, dalam penelitian ini model Gaussian plume digunakan untuk menentukan pola penyebaran polusi udara dan konsentrasinya. Polutan yang diamati adalah SO2, NO2, dan CO2 dengan konsentrasi yang berbeda dan memperhatikan dua nilai kecepatan angin serta intensitas cahaya matahari. Dengan mengikuti solusi model Gaussian Plume yang diperoleh dengan transformasi Laplace dapat ditentukan jarak aman pemukiman dari cerobong asap industri. Lebih lanjut, kondisi stabilitas atmosfer juga mempengaruhi besarnya nilai konsentrasi maksimum. Semakin stabil kondisi atmosfer mengakibatkan konsentrasi maksimal pada masing-masing polutan menurun dan semakin jauh polutan mencapai titik maksimum dari cerobong asap.
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