scholarly journals Analisis Peramalan (Forecasting) Penjualan Jasa Pada Warnet Bulian City di Muara Bulian

2019 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Fauziah Fauziah ◽  
Yulia Istia Ningsih ◽  
Eva Setiarini
Keyword(s):  
Error Rate ◽  
Average Method ◽  
Forecast Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Business World ◽  
Business Forecasting ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

In the business world Forecasting is one of the most important factors that must be applied in a business. Forecasting is a method for estimating a value in the future by using past data effectively and efficiently. This research was conducted at Warnet Bulian City In this study, the author discusses the analysis of forecasting (Forecasting) sales of services at the Bulian City internet cafe in Muara Bulian. Forecasting is done using three methods namely the MOVA (Moving Average) method, the WMA (Weight Moving Average) method and the Exponential Smoothing Method by comparing the smallest error rate Forecasting using the MA (Moving Average) method for 3 periods is predicted the level of profit to be gained by Bulian City Warnet in August amounted to. 11,117,833 with MAD 1,487,370. Forecasting using the Weigh Moving Averages (WMA) 3 method is forecasted at 12,287,300 with MAD Error 3,016,016 while the forecast using the double exponential smoothing method is 13,522,572 with MAD 5513617,364 then the forecasting method chosen is the Single Exponential Smoothing method with the Forecast value in August 2018 9,581.69 for the The Forecast Error is MAD of 1,378,375 which is the method with the smallest error rate.

scholarly journals PENERAPAN METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN JUMLAH KLAIM DI BPJS KESEHATAN PAMEKASAN

2016 ◽  
Vol 2 (1) ◽  
pp. 46 ◽  
Author(s):  
Faisol Faisol ◽  
Sitti Aisah
Keyword(s):  
Time Series ◽  
Average Method ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Smoothing Method ◽  
Repair Procedure ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method

Time series model is the model used to predict the future using past data, one example of a time series model is exponential smoothing. Exponential smoothing method is a repair procedure performed continuously at forecasting the most recent data. In this study the exponential smoothing method is applied to predict the number of claims in the health BPJS Pamekasan using data from the period January 2014 to December 2015, the measures used to obtain the output of this research there are four stages, namely 1) the identification of data, 2) Modeling, 3) forecasting, 4) Evaluation of forecasting results with RMSE and MAPE. Based on the research methodology, the result for the period 25 = 833.828, the 26 = 800.256, period 27 = 766.684, a period of 28 = 733.113, period 29 = 699.541, and the period of 30 = 655, 970. Value for RMSE = 98.865 and MAPE = 7.002, In this case the moving average method is also used to compare the results of forecasting with double exponential smoothing method. Forecasting results for the period 25 = 899.208, the 26 = 885, 792, 27 = 872.375 period, a period of 28 = 858.958, period 29 = 845.542, and the period of 30 = 832.125. Value for RMSE = 101.131 and MAPE = 7.756. Both methods together - both have very good performance because the value of MAPE is below 10%, but the method of exponential smoothing has a value of RMSE and MAPE are smaller than the moving average method.

scholarly journals Perbandingan Metode Holt Eksponential Smoothing dan Winter Eksponential Smoothing Untuk Peramalan Penjualan Souvenir

2017 ◽  
Vol 11 (2) ◽  
pp. 123
Author(s):  
Ruli Utami ◽  
Suryo Atmojo
Keyword(s):  
Error Rate ◽  
Forecast Error ◽  
Exponential Smoothing ◽  
Optimal Number ◽  
Smoothing Method ◽  
Forecasting Error ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Comparison Of Effectiveness ◽  
Exponential Smoothing Method

UD. Fajar Jaya is a trading business unit engaged in the supply of souvenirs. But in the management of the business there are some problems of which are UD. Fajar Jaya can not predict how the optimal number of souvenirs that must be provided to customers on every item souvenirs are sold. This causes the service to consumers less than the maximum, especially at certain moments sales of souvenirs (example: glass souvenirs) jumped dramatically from the number of average sales. To overcome the above, the authors propose to forecast the level of sales of souvenirs using Holt and Winter methods that exist in the development of Exponential Smoothing (ES) method. From the application of the two methods, then will make comparison of effectiveness of method which measured through actual data accuracy and forecasting result by knowing forecast error level. From the research results obtained forecasting results for Holt Double Exponential Smoothing method in July of 2017 is amounted to 599 items that may be sold with MAD forecasting error rate of 10.54 and MAPE of 3.70%. As for forecasting using Winter Exponential Smoothing method in July of 2017 is 549.6 items that may be sold with MAD 0.02 and MAPE error rate of 2.55%. The conclusion that can be drawn from the research that has been done on sales data souvenirs on UD. Fajar Jaya is that the Winter Exponential Smoothing method is more suitable to be applied in case study of souvenir sales in UD. Fajar Jaya is more than Holt Double Exponential Smoothing method.

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

2021 ◽  
Vol 6 (2) ◽  
pp. 101
Author(s):  
Niken Chaerunnisa ◽  
Ade Momon
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Oil Products ◽  
Cooking Oil ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Product Sales ◽  
Exponential Smoothing Method ◽  
Single Exponential

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 Bitcoin using Double Exponential Smoothing Method Based on Mean Absolute Percentage Error

2020 ◽  
Vol 4 (2) ◽  
pp. 91
Author(s):  
Febri Liantoni ◽  
Arif Agusti
Keyword(s):  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Business World ◽  
Absolute Percentage Error ◽  
A Value ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.

PERBANDINGAN METODE FUZZY TIME SERIES HSU DAN DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA

2016 ◽  
Vol 1 (2) ◽  
pp. 153-162 ◽  
Author(s):  
Wulan Anggraeni
Keyword(s):  
Time Series ◽  
Error Rate ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Double Exponential ◽  
Time Series Method ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

The purpose of this study is to determine which accuracy is better between fuzzy time series method and Holt double exponential smoothing method. The data used is daily published rupiah exchange rate of Bank Indonesia in the period of 1 April 2016 until 17 june 2016. After being calculated, the error rate fuzzy time series Hsu method is at 0,6 %, while the error rate holt double exponential smoothing method is at 2,25%. Based on the calculation, it can be concluded that the error rate forecasting rupiah exchange rate using fuzzy time series method is lower than the holt double exponential smoothing It means that the fuzzy time series hsu method has better accuarcy than Holt double exponential smoothing method. The result of forecasting in 21, 22, 23, 24, and 25 respectively are Rp. 13355, Rp. 13375, Rp. 13395, Rp. 13465, Rp 13.475. Keywords: fuzzy time series, holt double exponential, forecasting

scholarly journals Forecasting Of Criminality Problems Using Double Exponential Smoothing Method

Academia Open ◽  
2021 ◽  
Vol 4 ◽  
Author(s):  
Fatikhul Ikhsan ◽  
Sumarno
Keyword(s):  
Social Action ◽  
Police Officers ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Legal Norms ◽  
The Public ◽  
Police Station ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
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.

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

scholarly journals Forecasting production,consumption and the food gap of rice crop in Iraq using Exponential Smoothing method

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Iman K, Mustafa ◽  
Osamah.K. Jbara
Keyword(s):  
Exponential Smoothing ◽  
Smoothing Method ◽  
The Self ◽  
Rice Crop ◽  
Factors Affecting ◽  
Production Ratio ◽  
Self Sufficiency ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

"The aim of this research is to predict the production, consumption and food gap of the rice crop in Iraq, as well as the economic factors that affect the self-sufficiency ratio and the quantity of imports with the time series (2015-1980). Based on the statistical program (Minitab & SPSS) Is the Exponential Smoothing method for Forecasting the production, consumption, and nutritional gap of the rice crop. Two types of single and double (2016-2025) was the single Exponential Smoothing method for having the lowest MSE value of (11450.4) . As for the consumption of the rice yield for the period (2025-2016), the double Exponential Smoothing method was the most accurate (MSE), which is 87100.7. As for the food gap, the single Exponential Smoothing is the best predictor for the same period in terms of the lowest value (MSE) 84100.1. The self-sufficiency ratio was affected by five factors (cultivated area, Imports, available for consumption, import / production ratio, the dummy variable representing years of blockade), and Factors affecting the quantity of imports (rice production,available for consumption, border prices, the number of the population Al- Muthanna University All rights reserved"

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