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.

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Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v9i2.563 ◽

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

AbstractHotel 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%.Keywords: forecasting, linier regression, moving average, exponential smoothing.AbstrakHotel 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%.Kata kunci: peramalan, regeresi linier, moving average, exponential smoothing.

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Implementasi Metode Penghalusan Ekponensial Tunggal Dalam Prediksi Penjualan Buku

Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) ◽

10.30646/tikomsin.v9i2.539 ◽

2021 ◽

Vol 9 (2) ◽

pp. 1

Author(s):

Heri Setyawan ◽

Sri Hariyati Fitriasih ◽

Retno Tri Vulandari

Keyword(s):

Mean Squared Error ◽

Exponential Smoothing ◽

Smoothing Method ◽

Squared Error ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Product Sales ◽

Exponential Smoothing Method ◽

The Relationship ◽

Single Exponential

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.

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ANALISIS PERAMALAN PENJUALAN PRODUK KERIPIK PISANG KEMASAN BUNGKUS (Studi Kasus : Home Industry Arwana Food Tembilahan)

J TI UNDIP JURNAL TEKNIK INDUSTRI ◽

10.14710/jati.11.3.135-142 ◽

2017 ◽

Vol 11 (3) ◽

pp. 135 ◽

Cited By ~ 1

Author(s):

Siti Wardah ◽

Iskandar Iskandar

Keyword(s):

Data Processing ◽

Trend Analysis ◽

Standard Error ◽

Moving Average ◽

Exponential Smoothing ◽

A Value ◽

Forecasting Method ◽

Past Data ◽

Product Sales ◽

Exponential Smoothing Method

Peramalan adalah metode untuk memperkirakan suatu nilai dimasa depan dengan menggunakan data masa lalu. Penelitian ini dilakukan pada Home Industry Arwana Food. Pada penelitian ini, penulis membahas mengenai analisis peramalan penjualan produk kripik pisang untuk jenis kemasan bungkus. Peramalan yang dilakukan mengggunakan tiga metode yaitu metode Moving Average, metode Exponential Smoothing with Trend dan metode Trend Anayisis dengan membandingkan tingkat kesalahan (error) terkecil, maka metode peramalan yang terpilih yaitu metode Trend Analysis, dengan nilai MAD sebesar 161,3539, MSE sebesar 55744,16, dan standar error sebesar 242,947. Dari analisis pengolahan data yang telah dilakukan berdasarkan metode peramalan yang terpilih, peramalan penjualan terhadap produk kripik pisang jenis kemasan bungkus adalah sebanyak 1121,424 atau 1122 bungkus/bulan, artinya pihak Home Industry Arwana Food Tembilahan harus menyediakan produk kripik pisang kemasan bungkus adalah sebanyak 1122 bungkus untuk tiap bulannya. ABSTRACT Forecasting is a method to estimate a value of the future using past data. This research was conducted at the Home Industry Arowana Food. In this study, the authors discuss the analysis of product sales forecasting banana chips for this type of packaging wrap. Forecasting that do use traditional three methods are methods Moving Average, Exponential Smoothing method with Trend and Trend Anayisis method by comparing the level of errors (error) the smallest, then the selected forecasting method is the method of Trend Analysis, with a value of 161.3539 MAD, MSE of 55744 , 16, and the standard error of 242.947. From the analysis of data processing that has been carried out based on the method chosen forecasting, sales forecasting for products banana chips are as many types of packaging wrap 1121.424 or 1 122 packs / month, meaning the Home Industry Arowana Food Tembilahan must provide products banana chips wrapped packs is as much as 1122 wrap for each month.

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

Muthanna Journal for Agricultural Sciences ◽

10.52113/mjas04/8.2/12 ◽

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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Forecasting Shoe Sales Volume with Exponential Smoothing Method at PT Kujang Utama Antasena - Bogor

JUDICIOUS ◽

10.37010/jdc.v2i2.463 ◽

2021 ◽

Vol 2 (2) ◽

pp. 134-137

Author(s):

Siti Juriah

Keyword(s):

Moving Average ◽

Control Process ◽

Exponential Smoothing ◽

Smoothing Method ◽

Smoothing Factor ◽

Planning And Control ◽

Shoe Industry ◽

Forecasting Method ◽

Exponential Smoothing Method ◽

And Control

PT Kujang Utama Antasena is a shoe industry company specifically for security. The purpose of this study is to forecast or predict sales. This study uses a quantitative method with exponential smoothing, smoothing factor/constant (?) of 0.2. In production activities, forecasting is carried out to determine the amount of demand for a product and is the first step of the production planning and control process to reduce uncertainty so that an estimate that is close to the actual situation is obtained. The exponential smoothing method is a moving average forecasting method that gives exponential or graded weights to the latest data so that the latest data will get a greater weight. In other words, the newer or more current the data, the greater the weight.

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Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES

Indonesian Journal on Software Engineering (IJSE) ◽

10.31294/ijse.v4i1.6228 ◽

2019 ◽

Vol 4 (1) ◽

pp. 1-6

Author(s):

Ratih Yulia Hayuningtyas

Keyword(s):

Medical Equipment ◽

Exponential Smoothing ◽

Smoothing Method ◽

Equal Weight ◽

Mean Square ◽

Double Exponential ◽

Forecasting System ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Single Exponential

Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory. Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.

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

Jurnal Matematika MANTIK ◽

10.15642/mantik.2016.2.1.46-51 ◽

2016 ◽

Vol 2 (1) ◽

pp. 46 ◽

Cited By ~ 1

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.

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FORECASTING USES DOUBLE EXPONENTIAL SMOOTHING METHOD AND FORECASTING VERIFICATION USES TRACKING SIGNAL CONTROL CHART (CASE STUDY: IHK DATA OF EAST KALIMANTAN PROVINCE)

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol14iss1pp013-022 ◽

2020 ◽

Vol 14 (1) ◽

pp. 013-022

Author(s):

Humairo Dyah Puji Habsari ◽

Ika Purnamasari ◽

Desi Yuniarti

Keyword(s):

Control Chart ◽

Exponential Smoothing ◽

Smoothing Method ◽

Signal Control ◽

East Kalimantan ◽

Forecasting Method ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Tracking Signal

Abstrak Peramalan merupakan suatu teknik untuk memperkirakan suatu nilai pada masa yang akan datang dengan memperhatikan data masa lalu maupun data saat ini. Data yang menunjukan suatu trend, cocok dengan metode peramalan double exponential smoothing dari Brown atau metode double exponential smoothing dari Holt. Peramalan metode double exponential smoothing pada penelitian ini diaplikasikan pada data IHK Provinsi Kalimantan Timur periode Bulan Januari Tahun 2016 hingga Bulan Februari Tahun 2019 yang berpola trend. Tujuan dari penelitian ini adalah memperoleh hasil perbandingan akurasi metode peramalan double exponential smoothing berdasarkan nilai MAPE terkecil, memperoleh hasil verifikasi metode peramalan double exponential smoothing terbaik berdasarkan grafik pengendali tracking signal, dan memperoleh hasil peramalan menggunakan metode double exponential smoothing terbaik. Hasil penelitian menunjukkan metode peramalan terbaik adalah metode double exponential smoothing dari Holt dengan parameter dan berdasarkan nilai MAPE terkecil sebesar 0,361% dan nilai tracking signal yang keseluruhan terkendali pada grafik pengendali tracking signal. Kata kunci: Double Exponential Smoothing, IHK, MAPE, Tracking signal. Abstract Forecasting is a technique for estimating a value in the future by looking at past and current data. Data that shows a trend, matches the Brown’s exponential smoothing forecasting method or Holt's double exponential smoothing method. Forecasting of double exponential smoothing method in this study was applied to the IHK data of East Kalimantan Province for the period of January 2016 to February of 2019 which has a trend pattern. The purpose of this study was to obtain the results of the accuracy comparison of the double exponential smoothing forecasting method based on the smallest MAPE value, obtain the best verification results of the double exponential smoothing forecasting method based on tracking signal control charts, and obtain the best forecasting results using the double exponential smoothing method. The results showed that the best forecasting method was Holt's double exponential smoothing method with parameters and based on the smallest MAPE value of 0.361% and the overall tracking signal value was controlled on the tracking signal control chart. Keywords: Double Exponential Smoothing , IHK, MAPE, Tracking signal.

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GOOD SALES FORECASTING INFORMATION SYSTEM USING SINGLE EXPONENTIAL SMOOTHING METHOD

Jurnal Teknologi Informasi dan Pendidikan ◽

10.24036/tip.v14i1.453 ◽

2021 ◽

Vol 14 (1) ◽

pp. 77-82

Author(s):

Rahmadini Darwas ◽

Rahimullaily Rahimullaily ◽

Naufal Abdi

Keyword(s):

Information System ◽

Exponential Smoothing ◽

Mean Value ◽

Smoothing Method ◽

Sales Data ◽

Cooking Oil ◽

The Mean ◽

Exponential Smoothing Method ◽

Single Exponential

This study aims to determine the estimated number of items sold at one of the mini market, namely the Tita shop, especially Sari Murni cooking oil, 2 liter packs for the next one month based on sales data for January 2016 to December 2017. The problems that occur at Tita`s shop are is difficult to estimate the amount of stock of goods and calculate the estimated cost required for sales in the next month period, so it is necessary to build a forecasting information system using the single exponential smoothing method which assumes that the data fluctuates around the mean value without any trend or seasonal elements. This study resulted in the amount of 2 liter packaged sari murni cooking oil in Januari 2018, which was 42 pcs. Meanwhile, the estimated cost required to buy 2 liter packaged cooking oil stock in that period is Rp. 609.000,00 with a capital price unit of goods Rp. 14.500,00.

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ANALISIS PERAMALAN PENJUALAN AIR MINUM DALAM KEMASAN 240ml PADA PT TRIJAYA TIRTA DARMA (GREAT) DENGAN METODE SINGLE MOVING AVARAGE DAN EXPONENTIAL SMOOTHING

Industrika: Jurnal Ilmiah Teknik Industri ◽

10.37090/indstrk.v4i2.235 ◽

2020 ◽

Vol 4 (2) ◽

Author(s):

Dita Meliana ◽

◽

Suharto Suharto ◽

Putri Endah Suwarni ◽

◽

...

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Bottled Water ◽

A Value ◽

Forecasting Method ◽

The Future ◽

Future Events ◽

Past Data ◽

Product Sales ◽

Exponential Smoothing Method

Abstract Companies engaged in the field of product sales or distribution services, definitely want success in their activities in the future. This shows that every company always strives to continue to be able to develop in its business field in the future. Forecasting is a method for estimating a value in the future by using past data. Forecasting can also be interpreted as an art and science to predict future events. Researchers conducted a study by taking a location in PT Trijaya Tirta Dharma bottled water company. The author discusses forecasting sales of 240ml bottled water using two methods, namely Single Moving Average and Exponential Smoothing. The author compares the smallest error rate, the forecasting method chosen is Exponential Smoothing. For the Exponential Smoothing method, the results obtained using alpha 0.2 are for February of 195,767, March 197905, April 199,527, May 213,661, June 227,287, July 238,295, August 246,234, September 263,600, October 256,923, November 266,857, December 238,762. Then the forecast is MFE 22466,593, MAD 22466.593, MSE 55522225874, MAPE 2%.

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