Double Exponential Research Articles

Analytical formalism for calculations of parameters needed for quantitative analysis by X-ray photoelectron spectroscopy

Analytical formalism for calculations of parameters needed for quantitative analysis by X-ray photoelectron spectroscopy

Implementasi Metode Penghalusan Ekponensial Tunggal Dalam Prediksi Penjualan Buku

The prediction of the quantity of product sales in the future is intended to control the amount of existing product stock, so that product shortages or excess stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be sought on time and the cooperation of the store with the relationship is maintained well so that the store can avoid losing both sales and consumers. The purpose of this study is to compare the effectiveness of the use of the Single Exponential Smoothing method and methods Double Exponential Smoothing with a smoothing parameter value a = 0.5 for forecasting sales by comparing the error values in the two methods using the Mean Squared Error (MSE) method, the MSE results of the Single Exponential Smoothing method is 4967.75 while the MSE Double Exponential Smoothing is 5113.03. Thus, the Single Exponential Smoothing method is more accurate than Double Exponential Smoothing in calculating book sales forecasting because it has a low MSE value.

APLIKASI POINT OF SALE DAN PERAMALAN STOK BARANG MENGGUNAKAN METODE DOUBLE EXPONENTIAL SMOOTHING DI KURNIA STUDIO PAMEKASAN

In the current era of globalization, information technology is increasing very rapidly, especially in sales marketing. The computer as an intermediary created to facilitate human work. Kurnia studio which is engaged in the sale of goods and services requires a lot of information software that supports and provides satisfying services for consumers. Until now all the relaxation in the studio has not been computerized, starting from recording the order to storing other data related to sales to reporting, so that it is possible during the process of recording errors in the calculation, the accuracy of the data made and delays in searching the data required. Another problem faced by Kurnia Studio is that it is difficult to determine the stock of goods. Determination of the right stock of goods is needed to be able to divide the capital allocation so that services and sales can run smoothly. For that we need a point of sale application that is useful in finding data and making studio financial reports to be more accurate and faster. with this technology a forecasting system is also used using the double exponentiation smoothing method to analyze the stocking of goods for the following month. The use of this method is based on the stock of goods in the studio studio which tends to go up or down.Keywords : Point of Sale,, Forecasting,Metode Double exponential smoothing

Trend Analysis and Short-Term Forecasting of Goat and Sheep Populations and their Meat Production in Sri Lanka using Single and Double Exponential Smoothing Models

Trend Analysis and Short-Term Forecasting of Goat and Sheep Populations and their Meat Production in Sri Lanka using Single and Double Exponential Smoothing Models

Forecasting Natural Rubber Price in Malaysia by 2030

Natural rubber (NR) has recently become one of Malaysia's most important economic sectors. Despite, the price of Standard Malaysia Rubber 20 changes frequently. That is why it is important to develop a NR price forecasting model. Because there was a significant time lag between making output decisions and the actual output of the commodity in the market. The aim of this study is to determine the time series pattern for natural rubber price in Malaysia within 1995 until 2020 and to forecast the natural rubber price in Malaysia for 10 years ahead. The data used is from year 1995 until 2020 that were obtained from Malaysian Rubber Board (MRB). This study also used univariate forecasting like Naïve with Trend, Double Exponential Smoothing, Holt’s Winter and Autoregressive Integrated Moving Average (ARIMA). Then, the measurement error is used to determine the best method to forecast the future data. The measurement error that used in this study are Mean Absolute Error, Mean Squared Error, Root Mean Square Error, Mean Absolute Percentage Error and The Theil Inequality Coefficient. Result: The natural rubber price in Malaysia showed a trend pattern. Then, ARIMA is used to determine the forecast of natural rubber price for next 10 years since it has the lowest measurement error. Conclusion: There are volatility in the price of natural rubber in Malaysia over the next 10 years.

Forecasting Malaysian Ringgit Using Exponential Smoothing Techniques

Forecasting the foreign currency exchange is a challenging task since it is influenced by political, economic and psychological factors. This paper focuses on the forecasting Malaysian Ringgit (MYR) exchange rate against the United States Dollar (USD) using Exponential Smoothing Techniques which are Single Exponential Smoothing, Double Exponential Smoothing, and Holt’s method. The objectives of this paper are to identify the best Exponential Smoothing Technique that describes MYR for 5 years period and to forecast MYR 12 months ahead by using the best Exponential Smoothing Technique. The comparison between these techniques is also made and the best one will be selected to forecast the MYR exchange rate against USD. The result showed that Holt’s method has the smallest value of error measure which depending on the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) for the evaluation part. The MSE is 1.43915x10-14 and MAPE is 2.5413 x 10-6. Meanwhile, the forecast value of MYR in August 2019 is RM 4.30226.

Applicability of Smoothing Techniques in Generation of Phenological Metrics of Tectona grandis L. Using NDVI Time Series Data

Information on phenological metrics of individual plant species is meager. Phenological metrics generation for a specific plant species can prove beneficial if the species is ecologically or economically important. Teak, a dominating tree in most regions of the world has been focused on in the present study due to its multiple benefits. Forecasts on such species can attain a substantial improvement in their productivity. MODIS NDVI time series when subjected to statistical smoothing techniques exhibited good output with Tukey’s smoothing (TS) with a low RMSE of 0.042 compared to single exponential (SE) and double exponential (DE). Phenological metrics, namely, the start of the season (SOS), end of the season (EOS), maximum of the season (MAX), and length of the season (LOS) were generated using Tukey-smoothed MODIS NDVI data for the years 2003–2004 and 2013–2014. Post shifts in SOS and EOS by 14 and 37 days respectively with a preshift of 28 days in MAX were observed in the year 2013–2014. Preshift in MAX was accompanied by an increase in greenness exhibiting increased NDVI value.LOS increased by 24 days in the year 2013–2014, showing an increase in the duration of the season of teak. Dates of these satellite-retrieved phenological occurrences were validated with ground phenological data calculated using crown cover assessment. The present study demonstrated the potential of a spatial approach in the generation of phenometrics for an individual plant species, which is significant in determining productivity or a crucial trophic link for a given region.

Forecasting Of Criminality Problems Using Double Exponential Smoothing Method

Crime is a form of social action that violates legal norms relating to acts of seizing property rights of others, disturbing public order and peace, and killing one or a group of people. This has always been a concern for residents in various places in the Ngoro sub-district, therefore this information system was created to help police officers to find out where crimes have occurred. This information sfystem was created to predict the area in Ngoro sub-district using the Double Exponential Smoothing method. So that this system can predict which areas in the next month there will be no crime, and can assist the public in reporting the occurrence of criminal acts without having to go to the police station first. The Double Exponential Smoothing method was chosen by the author because this method can be used. The data used is data on theft of crime from 2017 – 2019. The results of forecasting in one village in Ngoro sub-district such as Manduro are 0.07426431198 if rounded up to 0.1 which is categorized as low crime and has a MAPE value of 7.94%. Based on the MAPE value of the forecasting results, it can be concluded that a good constant is between 0.1 – 0.3.

Comparison of double exponential smoothing and triple exponential smoothing methods in predicting income of local water company

Local Water Company is a government-owned business entity that has a business scope in the management of drinking water and water facilities to improve the welfare of the community, which includes social, health, and public service aspects and has a very important role for the community in terms of supporting the smooth development of the region so that the company’s success must always be sought. This effort certainly needs to be supported also in terms of setting revenue targets at the company. Determination of revenue targets in local water companies is currently using manual calculation methods so that the accuracy and effectiveness of setting revenue targets is less accurate. Forecasting models with mathematical methods are needed to predict future revenue targets so that monitoring of the success of regional development and consideration in decision making can be monitored. Forecasting this revenue target is based on actual data within the previous 5 (five) years, namely from January 2014 until December 2018, by comparing the two forecasting methods, namely the Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES) methods. The forecasting accuracy method is used the Mean Absolute Percentage Error (MAPE) method to measure the accuracy of the forecasting results from the two forecasting methods used. Forecasting test results are performed using alpha constant values of 0.1, 0.3, 0.5, 0.7, and 0.9 as trial data. Based on the trials conducted, the forecasting results presented show that forecasting results with the Double Exponential Smoothing method provide a more optimal forecasting result at alpha 0.7 with a MAPE value of 9.54%, so that the use of the Double Exponential Smoothing method is recommended in forecasting revenue targets in the Local Water Company because it has an error value under 10%.

DETERMINATION OF THE BEST FORECASTING METHOD FROM MOVING AVERAGE, EXPONENTIAL SMOOTHING, LINEAR REGRESSION, CYCLIC, QUADRATIC, DECOMPOSITION AND ARTIFICIAL NEURAL NETWORK AT PACKAGING COMPANY

PT. Peace Industrial Packaging is a company that produces Styrofoam and plastic bottles that are used as containers or places or commonly referred to as packaging used by other companies to place their finished products. After getting data on the number of requests and production results obtained every month then the data will be processed using several forecasting methods such as Single Moving Average; Double Moving Average; Weight Moving Average; Single Exponential Smoothing; Double Exponential Smoothing; Linear Regression; Quadratic Method, Method Cyclic; Decomposition Method; and Artificial Neural Network (ANN) Method. After conducting the research calculation, the following conclusions can be drawn. The right forecasting method used for the HBL 100 ML product is the ANN (Artificial Neural Network) method because it has the smallest error value, namely the MAD error method of 760.583554, the MSE error method of 863,032.834043, the SDE error method of 970.304264, the MAPE error method is 0.112530, and the MPE error method is 0.112530

Implementasi IoT Untuk Early Warning System(EWS) Pada Tambak Udang Vaname

Vaname shrimp is a type of aquaculture that has a high economic value and has relatively fast growth. Good water quality is key in the cultivation of vaname shrimp on ponds. However, monitoring of pond water conditions is still done manually and not done intensively. Internet of Things (IoT) and Early Warning System (EWS) as the latest technology can be a solution in periodic pond monitoring activities and give early warning to farm owners. Double Exponential Smoothing Holt is one method for forecasting in time series. In this study, the system predicts the potential for endangering the pond conditions, when there are indications of danger, it will inform the user via Telegram chat. The accuracy test value of the forecasting method used for water temperature is the MSE value of 0.01954 and MAPE of 9.0859%. Whereas for the water pH, the MSE value is 0.000155 and MAPE of 0.828%.

Analisis Peramalan Peluang Bisnis Tanah Menggunakan Metode Double Exponential Smoothing

PT. JNF is a company engaged in Engineering, Construction, and Project Management, located in South Jakarta. This company wants to develop its business by building housing in the city of Malang, to be precise in the Kedungkandang and Lowokwaru districts. The company needs some data and information about population density and land prices in the area to help the process of their project work. Providing information on data and forecasting results about population density and land prices in the area will help companies facilitate business development. Therefore, companies need an information system that can predict population density data and land prices in Kedungkandang sub-district and Lowokwaru sub-district, which functions to help companies see land business opportunities in Malang for housing development. The method used to make this system is the Double Exponential Smoothing method, because this method has a trend pattern that matches the data pattern of the object to be predicted. The data used are data from 2005 - 2018. The result of population density forecasting for Lowokwaru sub-district is 8713.98 and has a MAPE value of 1.39%, for Kedungkandang sub-district is 4949.07 and has a MAPE value of 2.55%, for the land price of Lowokwaru sub-district is 2777725.18 and has a MAPE value of 3.45 % and Kedungkandang sub-district is 1766560.27 and has a MAPE value of 8.36%. Based on the calculation of the MAPE value the results of the forecast above, it can be concluded that if the greater the constant value, the lower the MAPE value, the best constant value is between 0.6 - 0.9. The land business opportunity in Lowokwaru and Kedungkandang sub-districts is high based on the forecasting results of population density and land prices which tend to increase every year and the land business opportunity value based on ROI (Return of Investment) for the company is 0.38% of the initial capital.
 Keywords: information systems, forecasting, land business opportunities, land prices, population density, double exponential smoothing.

PERBANDINGAN METODE SINGLE EXPONENTIAL SMOOTHING DAN MOVING AVERAGE PADA PERAMALAN PENJUALAN PRODUK MINYAK GORENG DI PT TUNAS BARU LAMPUNG

PT Tunas Baru Lampung is a company that produces palm cooking oil products under the Rose Brand brand. In product sales, companies sometimes experience ups and downs. Based on the sales data from Rose Brand Cooking Oil, the size of 1 L has fluctuated or in each period it changes and is not always boarding. Even though product sales are one of the important things to be evaluated from time to time on an ongoing basis. To predict future sales, forecasting is done. The forecasting method used is Double Exponential Smoothing and Moving Average. The method of accuracy will be compared using MSE, MAD, and MAPE. The results showed a comparison of the accuracy and the smallest error value in each method. By using the weight values ​​0.1, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 on the Single Exponential Smoothing method the weight value is 0.8 or α = 0.8, namely MSE of 250,570,764.80, MAD of 12,922.32 and MAPE of 33.55 Then, using the movement value n = 3 in the Moving Average method has an accuracy of 438,980,942.75 MSE, 18,142.14 MAD, and 41.37 MAPE. After comparing the accuracy of the two methods, the Single Exponential Smoothing method is the best method to predict sales of Rose Brand 1 L Cooking Oil products.

Forecasting Stock Market Price of Gold, Silver, Crude Oil and Platinum by Using Double Exponential Smoothing, Holt’s Linear Trend and Random Walk

Forecasting is an approach that has been used widely for ages. However, forecasting stock market price is a challenging task to be completed as the data fluctuates rapidly along the time and can be influenced by a variety of factors. Hence, this study aims to forecast the stock market price of gold, silver, crude oil and platinum by using Double Exponential Smoothing, Holt’s Linear Trend and Random Walk. Then, measures the forecast model obtained by using Sum Square Error, Mean Square Error and Root Mean Square Error which later can be used in determining the best forecasting method. Based on the analysis done, the result shows that Holt’s Linear Trend is the better forecasting method compared to Double Exponential Smoothing and Random Walk.

Growth Model and Forecasting Prices of Some Agricultural Products in Bangladesh

The aim of this paper was to explore the appropriate deterministic time series model using the latest selection criteria considering the price pattern of onion, garlic and potato products in Bangladesh (January 2000 to December 2016). It appeared from our analysis that the time series data for the prices of potato was first order homogenous stationary but onion and garlic were found to be the second order stationary. Four different forecasting models namely, linear trend model, quadratic trend model, exponential growth model, and S-curve trend model were used to find the best fitted model for the prices of above mentioned products in the Bangladesh. Three accuracy measures such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used for the selection of the best fitted model based on lowest value of forecasting error. Lowest values of these errors indicated a best fitted model. After choosing the best growth model by the latest model selection criteria, prices of selected agricultural commodities were forecasted using the following time-series analysis methods: Simple Exponential Method, Double Exponential Method using the time period from January 2017 to December 2021. The findings of this study would be useful for policy makers, researchers, businessmen as well as producers in order to forecast future prices of these commodities.

Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.

Inventory Management of Broilers in PD Mugi Jaya Using Economic Order Quantity (EOQ) and Silver Meal Method

PD Mugi Jaya is one of the business units in the field of chicken procurement in Bandung. Products produced in the form of chicken sent to various restaurants in Bandung. PD Mugi Jaya can distribute 5,000 chickens to restaurants that become customers and sell the type of chicken in retail in Bandung. The need to evaluate the inventory procurement strategy in 2021 in order to obtain a minimum total cost. The evaluation begins by determining the data patterns formed from the request data from September 2019 to December 2020. The pattern formed is a random pattern, which is then done forecasting demand 2021 with the method of forecasting that has the smallest error value. The preferred forecasting method is the Double Exponential Smoothing method with a MAPE value of 43.6%. The forecasting results are processed by the method of procurement of lot sizing (existing), Economic Order Quantity (EOQ), and Silver Meal supplies. The method that managed to obtain the minimum total cost is the Silver Meal method with a total cost of Rp 23,725,500.

Perancangan Distribusi Produk Tepung Bumbu PT.SI Dengan Metode Distribution Requirement Planning

<div class="WordSection1"><p><strong>PT. SI is an FMCG (Fast Moving Consumer Goods) of seasoning and foods corporate in Indonesia. PT. SI’s products such as MNG (Mono Natrium Glutamate), Seasoning Flour (SF), a range of condiments, coconut milk, and instant seasoning. PT SI has SF product’s selling data from 2017 until 2020. However, that data yet to be used optimally. That data could be processed further for the organization’s advantage. For example, to knowledge the trend that’s happening, and, for predicting sales value with doing some forecasting and designing DRP. Based on the calculation of the forecasting, we’re obtained the best results using Double Exponential Smoothing by Brown, whereas Mean Absolute Percentage Error’s value is the smallest between the other methods. DRP’s methods make scheduling for the demand of products for determining times and the amounts that needed, and determining plans for arriving products to anticipates sales. With the scheduling, therefore, the distributions can go smoothly and meet the needs of every distribution center. For acknowledging the amounts of trucks that we use, the sums of PORelease is divided with the trucks’ capacities which are 18 tonnages for one delivery, therefore, the delivery costs that we got are multiplied by the transport’s cost for every destination. For distribution costs, company costs are as much as Rp. 20,831,275,897 whereas with DRP method acquired the value for Rp. 17,611,094,522 margins 15,45% from company cost.</strong></p><p><strong><em>Keywords – </em></strong><em>Distribution Requirement Planning, Forecasting, Distribution cost.</em></p></div>

MENGGABUNGKAN METODE RBF, ARIMA DAN DOUBLE EXPONENTIAL SMOOTHING UNTUK PERAMALAN KURS RUPIAH

Forecasting is an integral part of management decision-making activities. Forecasts are made generally based on the data of the past that were analyzed by using certain methods. Therefore in this research will predict time series data using Radial Basis Fuction, ARIMA and Double Exponential Smoothing using Matlab version 8.1. The data used is the daily selling rate of Rupiah against the UERO that started from June 2020 to January 2021. Of the three results forecast, will be used prediction results are combined by hybrid methods. From the results of RBF forecasting, ARIMA, Double Exponential Smoothing obtained MAPE respectively 0.43%, 1.49% and 0.46% and after combined with methods hybridization obtained MAPE of 0.39%. It can be seen that for the best MAPE obtained by hybrid methods. Hopefully this research can help in analyzing the fluctuation of the value of a particular currency movements at the time of transaction - purchase of foreign currency

Prediction Of Drug Sales Using Methods Forecasting Double Exponential Smoothing (Case Study : Hospital Pharmacy of Condong Catur)

Purpose: Knowing the best alpha value from the data for each type of drug with various alpha parameters in the Double Exponential Smoothing Method and knowing the prediction results on each type of drug data at the Condong Catur Hospital pharmacy.Design/methodology/approach: Applying the Double Exponential Smoothing method with alpha parameters 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9Findings/result: The test results on a system built using test data show that the double exponential smoothing method provides accuracy below 20% by producing a different Alpha (α) for each type of drug because the trend patterns in each drug sale are different at the Pharmacy at the Condong Catur Hospital. .Originality/value/state of the art: Based on previous research, this study has similar characteristics such as themes, parameters and methods used. Previous researchers used smoothing methods such as Double Exponential Smoothing in predicting stock / sales of goods