Geometric Brownian Motion is a mathematical model that can be used in stock price forecasting. This research aimed to predict the stock prices during the outbreak of coronavirus in Indonesia. There are four important steps of this research, such as calculating the return of the stocks, analyzing the normality test of stock price return, forecasting the stock price by using Geometric Brownian Motion, and calculating the errors of the forecasting. Based on the research, the MAPE for three forecasted stock prices is mostly around 10%.
<p class="JRPMAbstractBodyEnglish">This research aims to resolve the heteroscedasticity problem in time series data by modeling and analyzing volatility the gold return using GARCH models. Heteroscedasticity means not the constant variance of residuals. The sample data is a return data from January 1, 2014 to September 23, 2016. The data analysis technique used is a stationary test, model identification, model estimation, diagnostic check, heteroscedasticity test, GARCH model estimation, and evaluation. The results showed that ARIMA (3,0,3)-GARCH (1.1) is the best model.</p>
Global warming is caused by various factors, one of them is the emission of CO2. Time series data of CO2 emission will be analyzed using moving average and exponential smoothing to forecast the CO2 emission of the period ahead. Both models provide estimates of forecasting based on the average value of the previous data and can be used for forecasting time series data containing trend component. The best models are selected based on the smallest error value based on the criteria of MAPE, MSD, and MAD AbstrakPemanasan global disebabkan oleh berbagai faktor, salah satunya adalah emisi gas CO2.. Data runtun waktu emisi CO2 akan dianalisis menggunakan metode runtun waktu moving average dan exponential smoothing untuk memprediksi emisi CO2 periode ke depan. Kedua model ini memberikan nilai estimasi peramalan berdasarkan rataan dari data runtun waktu sebelumnya serta dapat digunakan untuk peramalan data runtun waktu yang memuat komponen trend. Model terbaik dipilih berdasarkan nilai error terkecil berdasarkan kriteria MAPE, MSD, dan MAD.Kata Kunci: data runtun waktu, exponential smoothing, moving average
Biomass energy sources have several advantages, such as being used as a renewable energy source so that the energy source from biomass can provide a sustainable energy source. One of the first steps to determine the potential of energy resources that can be developed into renewable energy sources is by collecting data. The data collection carried out in this study focuses more on the biomass found in Balikpapan. The biomass potential in Balikpapan needs to be known by collecting and classifying the biomass data based on products from agriculture and plantations. The data that has been collected from secondary data and from surveys are then mapped to see the greatest biomass potential found in Balikpapan. The largest percentage of crop yields per year is found in North Balikpapan Subdistrict, which is 31% compared to five other sub-districts. The potential of biomass from Balikpapan City's natural resources, which the greatest amount of harvest, is the cassava food plant in North Balikpapan sub-district of 7,259 tons / year. In the type of fruit, snakefruit (salak) has the highest number of yields per year, which is about32,945 tons / year. The potential for waste from food plants, cassava waste originating from tree trunks, is 5,807.2 tons / year, and cassava skin is 1,088.8 tons / year
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.
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.
Peningkatan suhu udara akibat perubahan iklim dan pemanasan global telah menjadi perhatian utama bagi pembuat kebijakan, salah satunya adalah pemerintah Kalimantan Timur. Konsumsi energi listrik memiliki hubungan erat dengan perkembangan ekonomi di Kalimantan Timur. Sehingga diperlukan peramalan terhadap suhu udara guna memprediksi konsumsi energi listrik di masa mendatang. Tujuan penelitian ini adalah untuk mengetahui peramalan suhu udara di Kalimantan Timur, yaitu kota Balikpapan, Samarinda dan Berau. Serta mengetahui hubungan antara suhu udara dan konsumsi energi listrik di Kalimantan Timur. Dalam penelitian ini, metode yang digunakan ialah metode ARIMA (Autoregressive Integrated Moving Average) dan Regresi Linear. Hasil analisis diperoleh model terbaik untuk kota Balikpapan, Samarinda dan Berau secara berturut-turut, yaitu ARIMA(1,0,1), ARIMA(1,0,0) dan ARIMA(2,0,0). Sedangkan untuk Regresi Linear diperoleh nilai R-Square sebesar 39%. Dari hasil uji t dan uji F, diketahui bahwa suhu udara berpengaruh signifikan terhadap kenaikan konsumsi energi listrik di Kalimantan Timur. Kata Kunci: ARIMA, Listrik, Pemanasan Global, Peramalan, Suhu Udara
One of the climate’s elements that has an influence on daily activities is the wind speed. Wind is a movement of air that flows from high pressure to low pressure region. In the shipping and aviation, wind speed is a very important thing to predict. This is due to the wind speed is very influential on the process of the transportation activities. A strong wind can disturb the fluency of transportation. Therefore, information regarding the wind speed prediction is very important to know. In this paper, Kalman Filter algorithm is applied in the wind speed prediction by taking the case in Balikpapan. In this case, the Kalman Filter algorithm is applied to improve the result of ARIMA prediction based on error correction, so we get the prediction result, called ARIMA-Kalman Filter. Based on the simulation result in this study, it can be shown that the prediction result of ARIMA-Kalman Filter is better than ARIMA’s. This is known from the level of accuracy from ARIMA-Kalman Filter, which increased about 65% from ARIMA result.
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