Penerapan Metode Double Exponential Smoothing dan Moving Average pada Peramalan Permintaan Produk Gasket Cap di PT. Nesinak Industries

PT. Nesinak Industries is a company which focuses on the manufacturing process of an electronic component as well as automotive components (vehicle). In business activities, such as production, a strategy is required to survive in competition. Planning and forecasting are a strategy that can be implemented to accomplish these goals. In this study, the data used are previous sealing application data from January 2019 to March 2021. The objective of this study is to forecast product demand over the next period in order to be able to respond to customer demand. Data processing in this study utilize the Brown exponential double smoothing method and the moving average is then determined with the lowest MAPE (Mean Absolute Percentage Error) value to be used for the company’s product demand prediction calculations. The value of taken from Brown's exponential dual smoothing method is the value of with the two lowest error values from 0.1 to 0.9, whose value with the least error value is = 0.8 and = 0.9. In terms of the moving average method, the researchers tested a period of three months and a period of four months. In the MAPE calculation, the results of exponential double smoothing = 0.8 of 26.92 %, exponential double smoothing = 0.9 of 26.22 %, moving average n = 3 of 32.46%, and moving average n = 4 of 34.77%.

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Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

Jurnal ULTIMATICS ◽

10.31937/ti.v12i2.1688 ◽

2020 ◽

Vol 12 (2) ◽

pp. 95-103

Author(s):

Andini Diyah Pramesti ◽

Mohamad Jajuli ◽

Betha Nurina Sari

Keyword(s):

Population Distribution ◽

Exponential Smoothing ◽

Smoothing Method ◽

Percentage Error ◽

Population Census ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Better Than ◽

Population Numbers

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

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

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.4.2.335 ◽

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.

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The Prediction Of Export Product Prices With Holt’s Double Exponential Smoothing Method

2020 3rd International Conference on Computer and Informatics Engineering (IC2IE) ◽

10.1109/ic2ie50715.2020.9274679 ◽

2020 ◽

Author(s):

Hata Maulana ◽

Ulfa Mulyantika

Keyword(s):

Exponential Smoothing ◽

Smoothing Method ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Exponential Smoothing Method ◽

Export Product

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USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika ◽

10.37373/tekno.v8i2.112 ◽

2021 ◽

Vol 8 (2) ◽

pp. 117-122

Author(s):

Sambas Sundana ◽

Destri Zahra Al Gufronny

Keyword(s):

Time Series ◽

Moving Average ◽

Exponential Smoothing ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Single Exponential ◽

Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

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PREDIKSI KREDIT DAN INDEKS MUSIMAN (SEASONAL INDEX) PADA BANK PERKREDITAN RAKYAT DI KABUPATEN JEMBER

BISMA ◽

10.19184/bisma.v14i3.15982 ◽

2020 ◽

Vol 14 (3) ◽

pp. 210

Author(s):

Hari Sukarno ◽

Ratna Pratiwi Nugroho ◽

Susanti Prasetiyaningtiyas

Keyword(s):

Moving Average ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Development Pattern ◽

Research Approach ◽

Financial Reports ◽

Double Exponential ◽

Study Results ◽

Double Exponential Smoothing ◽

Seasonal Index

This research aims to analyze the credit's predictive value, the development pattern of credit distribution, and the credit fluctuations of 13 Rural Banks in Jember, influenced by seasonal index variables, credit interest, NPL, LDR, ROA, CAR, and operational efficiency ratio. This study used an explanatory research approach. The sample consisted of all Rural Banks' quarterly financial reports in 2014-2019 taken by a purposive sampling method. Data were analyzed using three methods, i.e., double exponential smoothing, moving average ratio, and multiple linear regression analysis methods. Results showed that, according to each data analysis method, ten Rural Banks experienced increased credit distribution. However, the other three Rural Banks experienced a decrease in credit distribution. The study results also indicated an increasing trend in the development pattern of credit distribution. Meanwhile, the NPL and LDR variables partially influenced credit fluctuations. Keywords: credit prediction, rural bank, seasonal index

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The Comparison of Double Moving Average and Double Exponential Smoothing Methods in Forecasting the Number of Foreign Tourists Coming to North Sumatera

Journal of Physics Conference Series ◽

10.1088/1742-6596/1462/1/012046 ◽

2020 ◽

Vol 1462 ◽

pp. 012046

Author(s):

Didi Febrian ◽

Said Iskandar Al Idrus ◽

Debora Agnes Jessica Nainggolan

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Smoothing Methods ◽

Double Exponential ◽

Double Exponential Smoothing

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Forecasting Of Criminality Problems Using Double Exponential Smoothing Method

Academia Open ◽

10.21070/acopen.4.2021.2003 ◽

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.

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ANALISIS PERAMALAN DAN PERMINTAAN KREDIT SEKTOR PERTANIAN PADA PT.BRI (PERSERO) UNIT KEPAHIANG I

Jurnal AGRISEP Kajian Masalah Sosial Ekonomi Pertanian dan Agribisnis ◽

10.31186/jagrisep.3.2.82-91 ◽

2004 ◽

Vol 3 (2) ◽

pp. 82-91

Author(s):

Redy Badrudin ◽

Bambang Sumantri ◽

Meiliza Cecilia

Keyword(s):

Agricultural Sector ◽

Moving Average ◽

Secondary Data ◽

Regression Function ◽

Agricultural Credit ◽

Factors Influencing ◽

Double Exponential ◽

Interest Level ◽

Double Exponential Smoothing ◽

Credit Data

The study was aimed to Know growth and forecast the amount of demand for agricultural sector credit at PT. BRI (Persero) Unit Kepahiang I, and (2) examine factors influencing for agricultural sector credit. Data was secondary data from 1993 up to 2003. Ratio to Moving Average Method, Double Exponential Smoothing with moved period 3 monthly and regression function of Non Doubled Linear were used. Results of research indicates that growth of demand for agricultural credit at PT. SRI (Persero) Unit Kepahiang I tend to fluctuate. Forecasting of demand for agricultural sector creditb to period quarterly at 2003 till 2004, tend to have experience decreasing significantly compare to previous period. Overall of factors influencing demand of agricultural sector is rate of interest level (X1), price level (X2), and agriculture product exchange rate (X3), while earning level (X4) does not have an effect on demand Key Words: BRI, Forecasting, demand for credits,agricultura/s , sectors

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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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