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.
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.
Crop insurance provides a solution to economic losses experienced by farmers due to the risk of crop failure. The sample of crop survival data from Sukaratu Village in Cianjur-West Java had been analyzed. This research was analyzed the probability of failure based on the empirical study of the survival model. As a result, the probability of a crop failure studied in Sukaratu Village was calculated at 3.27%. Then, the exponential regression model was used to get the waiting time between crop failure events. The crop insurance product was simulated under the rate of premiums and benefits of insurance in several cases.
Designing the best digital product is vital for the competitiveness of any organization. Thus, this paper aims to determine the critical success design factors and to create guidelines for start-up founders, product managers, designers and entrepreneurs on how to design a successful digital product. To this end, six key design factors and 24 respective sub-factors were identified based on literature and expert opinions. Further, 21 experts were surveyed regarding their priorities on these factors, using the analytic hierarchy process (AHP). The results suggest that high-level planning design is the most important success factor, while having clear product vision, discovery, strategy and goals, building a great user experience, and creating an aesthetic user interface are the top three priority sub-factors for successful digital products.
The problem proposed in this research is about the amount rainy day per a month at Balikpapan city and discretetime markov chain. The purpose is finding the probability of rainy day with the frequency rate of rainy at the next month if given the frequency rate of rainy at the prior month. The applied method in this research is classifying the amount of rainy day be three frequency levels, those are, high, medium, and low. If a month, the amount of rainy day is less than 11 then the frequency rate for the month is classified low, if a month, the amount of rainy day between 10 and 20, then it is classified medium and if it is more than 20, then it is classified high. The result is discrete-time markov chain represented with the transition probability matrix, and the transition diagram.
Vegetables are one of the horticultural commodities that have high economic value and opportunity in the market. However, vegetables supply chain in Indonesia is often identified with the traditional supply chain of long marketing chains, difficult market access for farmers, low competitiveness, farmers’ low income, and injustice to supply chain actors, especially between farmers and wholesalers. In creating a structured supply chain, value co-creation intervention applied to synergize farmers and wholesalers in Ciwidey Sub-district to understand each other’s problems then seek solutions to solve them. This research has been conducted in Ciwidey Sub-district, West Java. This research aims to measure the competitiveness and farmers income of each wholesaler’s group before and after value co-creation intervention. The result shows that variables competitiveness and income of Sari Hejo Farmer Group and Hidayah Alam Farmer Group have significant positive changed, while one of three indicator variable of competitiveness has not significantly changed.
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