Double Exponential Smoothing Method Research Articles

2030 Palm Oil Plantation Carbon Footprint Estimation using O-LCA and Forecasting

Since palm oil products have a crucial role in society development, palm oil business in Indonesia has grown significantly over the past few decades. However, this industry is frequently linked to environmental issues, particularly its potential emission of greenhouse gases (GHGs). This study attempts to estimate the carbon footprint of a palm oil company’s operation for the year of 2030. In doing so, a novel methodology that consists of organisational lifecycle assessment (O-LCA), simple linear regression (SLR), and double exponential smoothing (DES) methods is proposed. O-LCA is used to identify the sources of emission and estimate the amount of the emission generated, while SLR and DES are used to forecast the sources. As the result, the carbon footprint in 2030 is estimated to be 62,758,433 kg Carbon Dioxide equivalent (CO2eq) where on average a ton of crude palm oil (CPO) produces 1.08 ton CO2eq. The study also discloses that the three largest emission sources are palm oil mill effluent (POME), fertilizer, and transportation. The forecasting methods used are quite accurate with mean absolute percentage error (MAPE) of less than 10%. The results of this study can shed a light to help Indonesia achieving its target to reduce GHG emission by 2030.

Perapan Metode Double Exponential Smoothing pada Prediksi Nilai Tukar Petani di Provinsi Sumatra Barat Tahun 2023

West Sumatra is one of the provinces in Indonesia where the agricultural sector is very important for the regional economy. To encourage the development of the agricultural sector, an appropriate strategy is needed for each location. One of the efforts to encourage the development of the agricultural sector is to pay attention to the availability of agricultural facilities and infrastructure, so that there are no obstacles for producers that increase production costs and harm farmers. The purpose of this study was to determine the model and prediction results of Farmer Exchange Rate in West Sumatra using the Double Exponential Smoothing Method from January 2023 to June 2023. The prediction equation for NTP in West Sumatra Province in 2023 obtained is 〖 F〗_(t+m)=110,5049+(m)0,1773with a value of α = 0.99, β = 0.0115 and MAPE value = 0.99%. The Exchange Rate in West Sumatra from January 2023 to June 2023 tends to increase. The increasing Farmer Exchange Rate in West Sumatra can have a positive impact on the agricultural sector and improve the economic welfare of farmers.

Supply Control Of Oil Filter Spareparts Using EOQ And Min-Max Methods Based On Sales Forecast (Case Study: PT Astra International Tbk Soekarno Hatta)

Planning and controlling product inventory is very important for a company to ensure demand is met and maximize profits from sales effectively and efficiently. In addition, inventory planning and control are carried out to prevent excess or shortage of inventory. This study aims to predict sales and calculate inventory control to overcome excess stock and increased inventory costs on oil filter spare parts. The forecasting method used is Holt's Double Exponential Smoothing method to determine the number of sales of the four types of oil filter spare parts in the future and overcome the problem of oil filter spare parts inventory using the EOQ and Min-Max methods. Based on the research results, the resulting forecasting is accurate and the EOQ method results in a lower total inventory cost than the Min-Max method.

Forescating the Amount of Corn Production in North Sumatra Based on 2017 – 2021 Data Using The Single and Double Exponential Smoothing Method (Case Study of Central Bureau of Statistics of North Sumatra )

North Sumatra Province has high potential in the management and marketing of corn (Zae Mays) crops. Cultivation of corn plants is a superior product where the largest income is obtained from the sale of commodities and their processing so that they can assist the government in improving the economy. This study aims to determine the yield of corn in North Sumatra Province in the coming year. Data collection for this study uses secondary data, namely primary data obtained from other parties which are generally made in the form of tables and diagrams. Data obtained from the Central Bureau of Statistics of North Sumatra for corn production in 2017 was 1,741,257 tons, corn production in 2018 was 1,710,784 tons, corn production in 2019 was 1,960,424 tons, corn production in 2020 was 1,965. 444 tons, corn production in 2021 is 1,724,398 tons. To find out the increase in corn production, the consideration and comparison of the forecasting methods needed to minimize forecast errors that aim to approach reality are the single and double exponential smoothing methods with one parameter from Brown.

Comparison of Double Exponential Smoothing Method with Weighted Moving Average in Forecasting UD Sales. Setya Abadi D. M as Financial Literacy

Purpose: UD. Setya Abadi D. M is a business unit focusing on the food industry, such as making frozen food (Maryam, churros, tortillas). In carrying out its production activities, this company certainly needs a strategy to compete with other competitors, one of which is forecasting. In this research, the data used is past product sales data from July 2021 to June 2023. This research aims to forecast sales in the future period in order to meet customer needs.Methodology:This data processing uses the weighted moving average and double exponential smoothing methods, which are then tested with the Mean Absolute Percent Error value. Forecasting with the lowest MAPE value is an effective method. The α value used in the double exponential smoothing method from 0.1 to 0.9 is chosen to have the lowest MAPE level: α = 0.6 and α = 0.7. Meanwhile, the weighted moving average method uses three months and four months.Findings: In the MAPE calculation, the results obtained for double exponential smoothing α = 0.6 are 6.45%, and α = 0.7 are 7.14%, while the moving average n = 3 is 13.59%, and n = 4 is 13, 48 %.Implication:It can be concluded that the lowest MAPE value was produced using the DES (Double Exponential Smoothing) method with ???? = 0.6 with a MAPE value of 6.45%. This value is lower than DES ???? = 0.7, 3-month WMA, and 4-month WMA with weights of 0.4, 0.3, 0.2, 0.1.

Time series forecasting cancers cases in Algeria using double exponential smoothing method

Cancers are the second cause of death worldwide. Prevalence and incidence of cancers are getting increased by aging and population growth. This study aims to predict and model the evolution of breast, colorectal, lung, bladder and prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data are not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Analisis Peramalan Demand Produk RBL dengan Metode Double Exponensial Smoothing, Moving Avarage, dan Regresi Linear di PT Seiwa Indonesia

Based on historical data on the number of product requests and products produced in the January 2021-December 2022 period, there is a considerable discrepancy. Therefore to be able to predict demand in one or several subsequent periods based on past product demand data, the researcher conducts a forecasting analysis using the double exponential smoothing, moving average, and linear regression methods to find out the most accurate forecasting method to use. Based on the calculation results, it can be concluded that the most appropriate Forecasting method is the linear regression method because it has the lowest MSE value of 1,346,936,387. It is hoped that it will be able to assist PT Seiwa Indonesia in providing future stocks of RBL products in more accurate manner so as to reduce losses due to excessive production.

Prediksi Ekspor Migas Indonesia Dengan Double Exponential Smoothing

In Indonesia, there are several forms of exports, one of which is oil and gas exports. The Double Exponential Smoothing method is a forecasting method that can be used to forecast oil and gas export data. This method uses two smoothing parameters, namely the average smoothing parameter alpha (α), the trend smoothing parameter beta (β). In this study, the population used is data on oil and gas exports in Indonesia. And the sample used is Indonesia's oil and gas export data from January 2020 to October 2021, we get 22 data sourced from the Central Statistics Agency (BPS). The Double Exponential Smoothing method is used because the data to be processed has a trend, both an up and a down trend and gas exports in Indonesia have an up and down pattern. The Double Exponential Smoothing method can also predict the prediction of oil and gas exports in Indonesia in the next months, November 2021 to February 2022. To process the data, the researcher used the Holt Double Exponential Smoothing method .

Comparison of the Accuracy of Brown's and Holt's Double Exponential Smoothing in LQ45 Stock Price Forecasting

As of May 2022, 787 stocks are listed on the Indonesia Stock Exchange (IDX), and the number of stock indices in Indonesia to date is 38. One interesting and important stock index is the LQ45 index. Because this index is a very important reference index for investors, this research data focuses on stocks in the LQ45 index. There are two essential things in the forecasting process: the data and the right forecasting method. Two forecasting methods that can be used are Brown and Holt's Double Exponential Smoothing (DES). This study examines two methods with the lowest accuracy error in forecasting the LQ45 stock price data. Mean Absolute Percentage Error (MAPE) is used to measure the accuracy of the error. The analysis methods used to compare the MAPE of the two methods are the F test for variance similarity, Boxplot, t-test to test paired means with different cases of variance, and Wilcoxon signed rank test to test paired means nonparametric statistics. The result is that the MAPE average with Holt's DES method is smaller than the average MAPE with Brown's DES method. This is supported by the t-test for paired means with different cases of variance and also supported by the Wilcoxon signed exact rank test. Meanwhile, the MAPE standard deviation with Holt's DES method is smaller than the MAPE standard deviation with Brown's DES method. This is supported by the F test to test the variance similarity and is visually supported by a Boxplot diagram. From this study, LQ45 stocks with the smallest MAPE value accuracy are ICBP stocks. In general, based on the MAPE value, Holt's DES method is better than Brown's DES method in predicting the prices of stocks in the LQ45 index.

A Penerapan Metode Double Moving Average dan Double Exponential Smoothing pada Peramalan Nilai Impor Barang Konsumsi Tahun 2017-2022

The smoothing method is classified into two, namely the average smoothing method and the exponential smoothing method. This study examines the application of the double moving average (DMA) and double exponential smoothing (DES) methods in forecasting a data. This study uses 72 data, namely consumer goods import value data for the period January 2017 to December 2022. The method with the lowest MSE and MAPE values is used to predict the import value of consumer goods. The results obtained show that the brown double exponential smoothing method with parameter α, which is 0.1, is the best method for predicting the import value of consumer goods in 2017-2022 with an MSE value of 60374.46 and a MAPE value of 13.66%.

Proyeksi Produksi dan Keunggulan Komparatif Perdagangan Ubi Kayu Indonesia

Indonesia is one of the largest cassava importers among the ten cassava-producing countries. While there is a production surplus, Indonesia's cassava imports tend to increase yearly to meet industrial needs. This research has four objectives. First, to analyze (change) the production and trade of the world's largest cassava-producing countries in the future and the implications for Indonesia. Second, to evaluate the enabling environment for cassava production and marketing. Third, to identify the comparative advantages of Indonesia's cassava trade compared to other producing countries. Fourth, evaluate the potential for partnerships to increase Indonesian cassava production and marketing sustainability. This study used FAOSTAT data (1961-2020) projected to 15 years (2021-2035) with a double exponential smoothing method. Comparative advantages are evaluated with the Relative Export Advantage, Relative Import Advantage, Relative Trade Advantage, Net Export, and Grubel-Lloyd indexes. This study found that Indonesia's cassava harvested area will likely decrease while cassava imports will likely increase. Indonesia has a comparative advantage in exporting cassava. However, it is under-optimized due to inefficient cassava trades at the regional level and less successful cassava partnerships (between farmers and industry). This study recommends improving cassava production and trade conditions through improving enabling environments and collaborations between actors in the cassava chain.

Implementasi Metode Double Exponential Smoothing Brown Untuk Meramalkan Jumlah Penduduk Miskin

The poor population is a group of people who have limited economic resources sufficient to meet their basic needs. Based on the Badan Pusat Statistik Kabupaten Blitar, the Blitar Regency has seen an increase in poor people over the past three years. This is proven by the poverty presentation from 2019 to 2021, which has increased to 8.94, 9.33, and 9.65. This research predicts the number of poor people in Blitar Regency using Brown’s Double Exponential Smoothing method. The results of calculating the best error values obtained from this research, MAD is 4.95, MSE is 49.47, and MAPE is 3.79. The error value calculation is obtained when the alpha error parameter = 0.7. The results of forecasting with Brown’s Double Exponential Smoothing method on the number of poor people in Blitar Regency for the period 2023 to 2027 is as follows: Year 2023 amounting to 100.07259, in 2024 amounting to 96.52018, in 2025 amounting to 92.96777, in 2026 amounting89.42536, and in 2027 amounting 85.86295. Based on the results obtained, it is hoped that this forecasting can help the government determine appropriate policies to improve the welfare of the people of Blitar Regency.

Cacao (Theobroma cacao) Production Forecasting in Lampung Province with the Double Exponential Smoothing Method

Cacao (Theobroma cacao) is one of the Indonesian plantation commodities which can be processed into cacao and chocolate products which contain natural antioxidants. In Lampung, the cacao commodity is very superior because every year it has significantly increased and cocoa production is one of the plantation commodities that has contributed to the export development of Lampung Province. In this case, forecasting can be used to estimate that cacao production will always experience an increase or decrease. Another function is to provide information for farmers and the government. Forecasting uses the Double Exponential Smoothing method because the technique is suitable for forecasting with trend patterns and for the long term. The results and discussion of this research is that cacao production has increased in forecasting for 10 years with good MAPE criteria, namely 10.81484. From the discussion above, forecasting cocoa production using the Exponential Smoothing method, namely the Double Exponential Smoothing technique assisted by R software, is suitable for use because it produces good forecasts and a low forecasting accuracy value.

ANALYSIS OF RICE INVENTORY CONTROL AT PT. DAYA TANI SEMBADA NGAWI DISTRICT

The demand for rice fluctuates significantly along with the increase in population every year, making PT. Daya Tani Sembada, as a premium rice-producing company, must do planning to deal with changes in demand that will occur. This research aims to analyze rice demand forecasting and the optimal rice inventory. The method of determining the sample is purposive sampling, where the Director and Administrative Staff are vital informants. Demand forecasting was analyzed using the Double Exponential Smoothing (Holts) method and inventory control using Economic Order Quantity. The results showed that: 1) forecasting calculations show that the demand for rice in the next period is 33,367.38 tons; 2) inventory control calculations show that the company can prepare premium rice inventory of 29.89 tons, safety stock of 336.03 tons, ROP when rice inventory is at 779.47 tons, and TIC that must prepare amounting to IDR. 5,357,082,020, -, overall, demand for rice has increased by 8,662.58 tons from the previous period so that the company can increase production for the next period by the draft inventory control calculation

Penerapan Metode Double Exponential Smoothing dan Regresi Linier pada Peramalan Persediaan Packaging di PT. XYZ

PT. XYZ is one of the company that produces packaging materials, one of which is sacks. This study aims to determine forecasting on sack raw material packaging using the Double Exponential Smoothing method and Linear Regression in these calculations using manual calculation methods using Microsoft excel. The two methods are then identified as having the smallest error value. The data used in this study uses secondary data in the form of sales reports of raw material packaging in the past. Based on the forecasting results obtained using the Double Exponential Smoothing and Linear Regression methods, the smallest error value was obtained in the linear regression method with an error value of 275,711. The forecasting results in the next period were 16,713 by manual calculation. Thus, among the predicted results of the two methods, the linear regression method is the most optical. Keywords: Double Exponential Smoothing; Forecasting; Regresi Linier

COMPARISON OF FORECASTING RICE PRODUCTION IN MAGELANG CITY USING DOUBLE EXPONENTIAL SMOOTHING AND AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

Magelang City has experienced a significant decline in the rice production sector, triggering the need for forecasting research as the next crucial step. This research aims to forecast rice production in Magelang city. By applying Double Exponential Smoothing and ARIMA methods, the most suitable forecasting model is identified. Data on rice production was obtained from the Badan Pusat Statistik (BPS) of Magelang city. The results revealed that the ARIMA (0,1,1) model with MSE of 479,259 was the best choice. This model is expressed as . Using this model, rice production was forecast from July to December 2023, the forecasting results showed that rice paddy production is expected to fluctuate in the coming months. For July 2023, production is projected to be around 65,1762 units, followed by 51,4779 units in August, 58,2432 units in September, and so on.

Comparison of Double Exponential Smoothing Method with Weighted Moving Average in Forecasting UD Sales. Setya Abadi D. M as Financial Literacy

Purpose: UD. Setya Abadi D. M is a business unit focusing on the food industry, such as making frozen food (Maryam, churros, tortillas). In carrying out its production activities, this company certainly needs a strategy to compete with other competitors, one of which is forecasting. In this research, the data used is past product sales data from July 2021 to June 2023. This research aims to forecast sales in the future period in order to meet customer needs.Methodology:This data processing uses the weighted moving average and double exponential smoothing methods, which are then tested with the Mean Absolute Percent Error value. Forecasting with the lowest MAPE value is an effective method. The α value used in the double exponential smoothing method from 0.1 to 0.9 is chosen to have the lowest MAPE level: α = 0.6 and α = 0.7. Meanwhile, the weighted moving average method uses three months and four months.Findings: In the MAPE calculation, the results obtained for double exponential smoothing α = 0.6 are 6.45%, and α = 0.7 are 7.14%, while the moving average n = 3 is 13.59%, and n = 4 is 13, 48 %.Implication:It can be concluded that the lowest MAPE value was produced using the DES (Double Exponential Smoothing) method with ???? = 0.6 with a MAPE value of 6.45%. This value is lower than DES ???? = 0.7, 3-month WMA, and 4-month WMA with weights of 0.4, 0.3, 0.2, 0.1.

Predictive Hybrid Redundancy for Aircraft Wheel Braking System - A Design Study

Aircraft brake system plays an important role in safe take-off and landing phases of flight. In order to meet safety requirements, regulatory guidelines like ARP 4761 suggest duo duplex architecture for the aircraft Wheel Braking System (WBS). Safe and efficient braking systems for ground-based vehicles in the name of Brake By Wire (BBW) are also becoming very common resulting in significant research activity in the recent past. In order to reduce number of hardware components and to improve reliability further of BBW systems, Kim and team of Korean university came up with introduction of analytical redundancy for measurement of wheel angular velocity sensors. The scheme involves Predictive Hybrid Redundancy (PHR) for fault detection using a double exponential smoothing method. Since, such systems are not readily suitable for aircraft WBS, the present study is an attempt to extend this approach to aircraft WBS with appropriate modifications to integrate with normal mode, standby mode and emergency modes of aircraft WBS. Simulation studies for synthetic sensor signals show possibility of continued operation, with one sensor being operational, in the presence of transient, intermittent and hard over faults.

Peramalan Produksi Kelapa Sawit dan Karet di Provinsi Kalimantan Selatan

In Indonesia, the plantation sub-sector has an important role in increasing state revenue through the exports of its products, besides the mining and gas sector. The most widely produced plantation crops in Indonesia are oil palm and rubber and South Kalimantan is one of the top 10 provinces in Indonesia with oil palm plantations. This study aims to detect the correct forecasting model for data on oil palm crops and rubber production in South Kalimantan Province and to analyse the forecasting results for oil palm crops and rubber in South Kalimantan Province using the double exponential smoothing method. This research was conducted for 8 months (March 2022 to December 2022), using observational data from 2001 to 2021. Double Exponential Smoothing Holt was used in this study by looking at the error value obtained with the smallest Mean Absolute Percentage Error (MAPE). For palm oil production, the parameters α=0.8 and β=0.6 were the best parameters with a MAPE value of 8.05% and resulted in the forecasting of oil palm crops production in 2022 not increasing, in 2023 and 2024 experiencing an increase of 1%. As for forecasting rubber production, the parameters α=0.9 and β=0.9 are the best parameters with a MAPE value of 5.45% and forecasting rubber production in 2022 will increase by 1%, in 2023 and 2024 by 2%.

Forecasting Non-Oil and Gas Exports in Indonesia Using Double and Triple Exponential Smoothing Methods

Non-oil and gas exports could be forecasted using exponential smoothing for future periods. This study examines non-oil and gas export data in Indonesia from January 2015 to May 2021, indicating trends and seasonality. Based on the data characteristics, the obtained data were analyzed using Holt's double exponential smoothing method and triple exponential smoothing with multiplicative and additives Holt-Winters. The MAPE for all three models is less than 10%, indicating that the method is very good and could be used to forecast the next period. Using MAPE as a comparison, the best model for non-oil and gas exports is the additive Holt-Winters method triple exponential smoothing, which has the lowest MAPE of any model. The best method was employed to forecast data, making it possible for us to anticipate the pattern of non-oil and gas exports. This forecast data could be used as the basis for policymakers' decision-making. The forecast results using this method indicate that the value of non-oil exports will increase for the next period.