PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDAR UDARA INTERNASIONAL JUANDA MENGGUNAKAN METODE EXPONENTIAL SMOOTHING EVENT-BASED

The increase in the number of airplane passengers occurs at certain times, such as Eid al-Adha, Eid al-Fitr, and Christmas holidays. Of course, an excessive rise in the number of passengers can cause extreme flight traffic density so that which can cause flight delays, decreased airport service level performance, and other impacts. This study predicts the number of aircraft passengers at Juanda International Airport using the Exponential Smoothing Event-Based method. The Exponential Smoothing Event-Based method is a forecasting method that considers special events using the Exponential Smoothing method as the initial calculation. This study uses data on the number of passengers from January 2014 to December 2020. From the forecasting model, MAPE is 11.8905%, and MSE is 4202958561.0706, so that the resulting forecast can be categorized as good.

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PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDARA INTERNASIONAL AHMAD YANI DENGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN METODE EXPONENTIAL EMOOTHING EVENT BASED

Jurnal Gaussian ◽

10.14710/j.gauss.v9i4.29448 ◽

2020 ◽

Vol 9 (4) ◽

pp. 535-545

Author(s):

Sofiana Sofiana ◽

Suparti Suparti ◽

Arief Rachman Hakim ◽

Iut Triutami

Keyword(s):

Historical Data ◽

Seasonal Pattern ◽

Exponential Smoothing ◽

Smoothing Method ◽

International Airport ◽

Special Event ◽

Special Events ◽

Forecasting Method ◽

Event Based ◽

Exponential Smoothing Method

Forecasting the number of airplane passengers can be a consideration for the airline at Ahmad Yani International Airport related with addition of extra flight. The number of airplane passengers can be influenced by certain seasonal or special events. The seasonal influences can be known through historical data patterns and if there is a seasonal pattern, the Holt Winter’s Exponential Smoothing method can be used. Exponential Smoothing Event Based (ESEB) forecasting method can be use to see the special events that effect the number of airplane passengers at Ahmad Yani International Airport. After compared, the Holt Winter’s Exponential Smoothing method is a better method of forecasting the number of airplane passengers at Ahmad Yani International Airport because it has a smaller error value, namely the MSE value and the MAPE value than the Exponential Smoothing Event Based (ESEB)method. The MAPE and MSE values be produced from the best method each of 5,644139% and 619,998,718 .Keywords : Airplane Passengers, Seasonal Pattern, Special Event, Exponential Smoothing Event Based , Holt Winter’s Exponential Smoothing.

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METODE EXPONENTIAL SMOOTHING EVENT BASED (ESEB) DAN METODE WINTER’S EXPONENTIAL SMOOTHING (WES) UNTUK PERAMALAN JUMLAH PENUMPANG TIBA DI PELABUHAN PENYEBERANGAN GORONTALO

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN ◽

10.30598/barekengvol13iss3pp197-202ar935 ◽

2019 ◽

Vol 13 (3) ◽

pp. 197-202

Author(s):

Muhammad Rezky Friesta Payu ◽

Nurwan Nurwan

Keyword(s):

Historical Data ◽

Exponential Smoothing ◽

Special Events ◽

Forecasting Method ◽

Seasonal Influences ◽

Event Based

Forecasting the number of passengers can be a consideration for managers of Gorontalo Crossing Port regarding the provision of tickets. The number of passengers can be influenced by certain seasonal or special events. To see the special events that affect the number of passengers arriving at Gorontalo Crossing Port, the forecasting method used is the Exponential Smoothing Event Based (ESEB) method. The seasonal influences can be known through historical data patterns and using the Winter’s Exponential Smoothing (WES) method. After compared, the ESEB method is a better method of forecasting the number of arriving passengers at Gorontalo Crossing Port because it has a smaller MAPE value than the WES method.

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Requirement Forecasting of Equipment’s Maintenance Spares-Parts Based on Exponential Smoothing Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.605-607.9 ◽

2012 ◽

Vol 605-607 ◽

pp. 9-13

Author(s):

Xiao Chun Liu ◽

Yong Liang Zhang ◽

Ai Jun Huang ◽

Li Ya Xu

Keyword(s):

Exponential Smoothing ◽

Smoothing Method ◽

Forecasting Model ◽

Microsoft Excel ◽

Alternative Approach ◽

Forecasting Method ◽

Exponential Smoothing Method

Management of equipment’s maintenance spares-parts is one of important contents of equipment support. In allusion to the problem of requirement forecasting of maintenance equipment spares-parts, and based on exponential smoothing method, a requirement forecasting model of equipment’s maintenance spares-parts was built. Microsoft EXCEL was employed to forecast the equipment’s maintenance spares-parts requirements. The forecasting method provides a scientific and alternative approach to forecast requirement of equipment’s maintenance equipment.

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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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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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Advising an Appropriate Forecasting Method for a Snacks Item (Biscuit) Manufacture Company in Bangladesh

Dhaka University Journal of Science ◽

10.3329/dujs.v66i1.54545 ◽

2018 ◽

Vol 66 (1) ◽

pp. 55-58

Author(s):

Nandita Barman ◽

M Babul Hasan ◽

Md Nayan Dhali

Keyword(s):

Time Series ◽

Inventory Management ◽

Linear Trend ◽

Exponential Smoothing ◽

Demand And Supply ◽

Forecasting Methods ◽

Forecasting Method ◽

Short Term Forecasting ◽

Exponential Smoothing Method ◽

Manufacture Company

In this paper, we study the most appropriate short-term forecasting methods for the newly launched biscuit factory produces different types of biscuits. One of them is nut-orange twisted biscuits. As it is a newly launched biscuit factory, it does not use any scientific method to find future demand of their products to produce for the purpose of sales. Having an error free production as well as a good inventory management we try to find an appropriate forecasting method for the sets of data we analyzed for that specific production. Several forecasting methods of time series forecasting such as the Moving Averages, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. can be applied to estimate the demand and supply for these companies. This paper focuses on selecting an appropriate forecasting technique for the newly launched biscuit company. For this, we analyze Exponential Smoothing method as used to time series. We observe from the empirical results of the analysis that if the data has no trend as well as seasonality, Exponential Smoothing Forecasting Method processes as the most appropriate forecasting method for the factory. If the data experiences linear trend in it then Holt’s Forecasting Method processes as the most appropriate forecasting method for the sets of data we analyzed. Dhaka Univ. J. Sci. 66(1): 55-58, 2018 (January)

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Perancangan Aplikasi Peramalan untuk Metode Exponential Smoothing Menggunakan Aplikasi Lazarus (Studi Kasus: Data Konsumsi Listrik Kota Samarinda)

ESTIMASI: Journal of Statistics and Its Application ◽

10.20956/ejsa.v2i2.13364 ◽

2021 ◽

pp. 57-70

Author(s):

Hairi Septiyanor ◽

Syaripuddin Syaripuddin ◽

Rito Goejantoro

Keyword(s):

Golden Section ◽

Electricity Consumption ◽

Exponential Smoothing ◽

Consumption Data ◽

Forecasting Method ◽

Double Exponential ◽

Application Procedure ◽

Golden Section Method ◽

Smoothing Parameters ◽

Exponential Smoothing Method

Exponential smoothing is forecasting method used to predict the future. Lazarus is an open source software based on free pascal compiler. at this research, program Lazarus be design used exponential smoothing method to predict electricity consumption data in Samarinda City from September to November 2018. Purposed of this researched is to determine the procedure of building an exponential smoothing forecasting application and obtained forecasting result using the built application. Procedure of built the application are designed interface, designed properties and filled coding. The optimum smoothing parameters were obtained used the golden section method. Based on the analysis, electricity consumption data in Samarinda City shows a trend pattern, then the forecasting was used double exponential smoohting (DES) method are DES Brown and DES Holt. The best forecasting method for at this researched is DES Holt, because DES Holt method produced MAPE 0,0659% less than DES Brown method produced MAPE 0,0843%.

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Peramalan Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)

Journal of Mathematics, Computations, and Statistics ◽

10.35580/jmathcos.v1i1.9168 ◽

2019 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Hisyam Ihsan ◽

Rahmat Syam ◽

Fahrul Ahmad

Keyword(s):

Mean Squared Error ◽

Mean Absolute Error ◽

Absolute Error ◽

Exponential Smoothing ◽

Trial And Error ◽

Squared Error ◽

Forecasting Method ◽

Operational Activities ◽

Exponential Smoothing Method ◽

Single Exponential

Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.

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

JURNAL REKAYASA SISTEM INDUSTRI ◽

10.33884/jrsi.v6i2.3694 ◽

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.

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