Demand Forecasting of The Automobile Sales Using Least Square, Single Exponential Smoothing and Double Exponential Smoothing

Sales strategies require the right managerial in marketing products with the development of technology and communication, the decision making in product sales supported by complete data and can be analyzed into intelligence business solutions. The research discussed and provided solutions about how to forecast future demand targets from a set of data history by making a predictive model of product demand in the real case. The research study was obtained from automobile sales, which the company probably set the strategy from the forecast result of automobile sales by the system in the future. The research used forecasting methods such as Least Square, Single Exponential Smoothing, and Double Exponential Smoothing to achieve a small percentage of prediction error. The dataset was collected from Mitsubishi Motors Corporation which obtained 60 samples of popular product types such as Pajero, FE and L300 from 2014 to 2018 over a period of months. The experimental results reported that Double Exponential Smoothing has given a better performance than other methods. The forecasting result of Pajero reached the MAPE of 3.26%, FE reached the MAPE of 3.24%, and L300 reached the MAPE of 3.37%. This study indicates that the selection of the forecasting method depends on the actual data pattern and the adjustment of the parameters in predicting future points.

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Perbandingan Metode Single Exponential Smoothing dan Metode Holt untuk Prediksi Kasus COVID-19 di Indonesia

Jurnal ULTIMATICS ◽

10.31937/ti.v12i2.1689 ◽

2020 ◽

Vol 12 (2) ◽

pp. 89-94

Author(s):

Nur Hijrah As Salam Al Ihsan ◽

Hanifah Hanun Dzakiyah ◽

Febri Liantoni

Keyword(s):

Disaster Management ◽

Exponential Smoothing ◽

Smoothing Method ◽

Management Agency ◽

Double Exponential ◽

Double Exponential Smoothing ◽

The Right ◽

National Disaster ◽

Exponential Smoothing Method ◽

Single Exponential

Coronavirus disease (COVID-19) was first discovered in December 2019 in Wuhan, China, and spread so quickly into a pandemic. This outbreak has spread to 24 other countries, including Indonesia. Its spread is very fast, so a co-19 prediction study is needed to be able to make the right policy. To be able to predict the number of COVID-19 cases can be done with the Forecasting Technique. The purpose of this study is to forecast and compare Single Exponential Smoothing and Double Exponential Smoothing ¬ against the number of COVID-19 cases in Indonesia. The results of this study can be used as consideration for policymaking in dealing with the spread of COVID-19. Distribution predictions are based on data released by the Indonesian National Disaster Management Agency (BNPB) in the first 100 days of COVID-19 deployment. The results of this study are the Double Exponential Smoothing method is more accurate than the Single Exponential Smoothing method because the forecasting results show an increase from the previous data. And the percentage of errors (MAPE) obtained is significantly smaller.

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Perbandingan Metode Double Exponential Smoothing dan Least Square untuk Sistem Prediksi Hasil Produksi Teh

BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer ◽

10.37148/bios.v1i2.12 ◽

2021 ◽

Vol 1 (2) ◽

pp. 48-53

Author(s):

Muhammad Bagus Nurkahfi ◽

Victor Wahanggara ◽

Bakhtiyar Hadi Prakoso

Keyword(s):

Production Capacity ◽

Least Square Method ◽

Exponential Smoothing ◽

Least Square ◽

Market Demand ◽

Double Exponential ◽

Tea Production ◽

Double Exponential Smoothing ◽

The Right ◽

Exponential Smoothing Method

Tea is one of the mainstay commodities of Indonesian plantation. In order to meet market demand, it is necessary to plan the right production needs, so that the amount of production capacity and market demand is balanced. To meet the needs of the right production requires good planning. The way that can be done is by making predictions. In this study, the prediction of tea production was carried out using the Double Exponential Smothing and Least Square methods. From the test results, it was found that the MAPE value of the Double Exponential Smoothing method, the most optimal α value is α 0.1 with a MAPE value of 18.084% and for the Least Square method the MAPE value is 17.008%.

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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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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 Sistem Informasi Prediksi Hasil Penjualan Perangkat Komputer Menggunakan Metode Double Exponential Smoothing

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v4i3.2253 ◽

2020 ◽

Vol 4 (3) ◽

pp. 806

Author(s):

Nurul Adha Oktarini Saputri ◽

Nurul Huda

Keyword(s):

Exponential Smoothing ◽

Sales Forecast ◽

Double Exponential ◽

Sales Prediction ◽

The Future ◽

Time Series Method ◽

Past Data ◽

Double Exponential Smoothing ◽

The Right ◽

Exponential Smoothing Method

Prediction is an activity to predict a situation that will occur in the future by passing tests in the past. One way to get sales information in the future is to make sales forecasting. This sales forecast uses the Double Exponential Smoothing method because this method predicts by smoothing or smoothing past data by taking an average of several years to estimate the value of the coming year and this method uses the time series method. The results of this study are the right sales prediction information system, in order to determine the existing inventory of goods in accordance with the demand (demand) so that there is no overstock or lack of inventory in the future

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PREDIKSI INFLASI DI INDONESIA MENGGUNAKAN METODE MOVING AVERAGE, SINGLE EXPONENTIAL SMOOTHING DAN DOUBLE EXPONENTIAL SMOOTHING

Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika ◽

10.46306/lb.v1i2.25 ◽

2020 ◽

Vol 1 (2) ◽

pp. 123-129

Author(s):

Nugroho Arif Sudibyo ◽

Ardymulya Iswardani ◽

Arif Wicaksono Septyanto ◽

Tyan Ganang Wicaksono

Keyword(s):

Moving Average ◽

Exponential Smoothing ◽

Double Exponential ◽

Double Exponential Smoothing ◽

Single Exponential

Tujuan dari penelitian ini adalah untuk mengetahui model peramalan yang paling baik digunakan untuk meramalkan inflasi di Indonesia dengan data inflasi Januari 2015 sampai dengan Mei 2020. Penelitian ini menggunakan beberapa metode peramalan. Berdasarkan metode peramalan yang dilakukan didapatkan hasil peramalan yang paling baik dilihat dari MAPE, MAD dan MSD adalah single exponential smoothing. Selanjutnya, hasil peramalan menunjukkan bahwa tingkat inflasi di Indonesia pada Agustus 2020 sebesar 1,41746%.

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Forecasting the Number of Coronavirus (COVID-19) Cases in Ethiopia Using Exponential Smoothing Times Series Model

10.1101/2020.06.29.20142489 ◽

2020 ◽

Author(s):

Teshome Hailemeskel Abebe

Keyword(s):

Time Series Data ◽

Exponential Smoothing ◽

Series Data ◽

Smoothing Methods ◽

Times Series ◽

Double Exponential ◽

Forecasting Performance ◽

Double Exponential Smoothing ◽

The Right ◽

Exponential Smoothing Method

AbstractThe main objective of this study is to forecast COVID-19 case in Ethiopiausing the best-fitted model. The time series data of COVID-19 case in Ethiopia from March 14, 2020 to June 05, 2020 were used.To this end, exponential growth, single exponential smoothing method, and doubleexponential smoothing methodwere used. To evaluate the forecasting performance of the model, root mean sum of square error was used. The study showed that double exponential smoothing methods was appropriate in forecasting the future number ofCOVID-19 cases in Ethiopia as dictated by lowest value of root mean sum of square error. The forecasting model shows that the number of coronavirus cases in Ethiopia grows exponentially. The finding of the results would help the concerned stakeholders to make the right decisions based on the information given on forecasts.

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PERAMALAN PENERIMAAN BEA CUKAI INDONESIA

EKONOMIS Journal of Economics and Business ◽

10.33087/ekonomis.v2i1.38 ◽

2018 ◽

Vol 2 (1) ◽

pp. 137

Author(s):

Yolanda Sari ◽

Nurlia Fusfita

Keyword(s):

Regression Model ◽

Moving Average ◽

Exponential Smoothing ◽

Least Square ◽

Coefficient Of Determination ◽

Rational Approach ◽

Ordinary Least Square ◽

Export Duty ◽

Double Exponential ◽

Double Exponential Smoothing

The revenue of customs and excise is very important in APBN. By making accurate estimation, target of revenue can be better determined. In addition, the revenue of customs and excise is also influenced by many external factors that are difficult to predict therefore a rational approach is needed to estimate revenue. This research uses Double Exponential Smoothing, Ordinary Least Square (OLS) model and Moving Average in predicting customs and excise revenue. Data used in this research is secondary data in time coherent pattern. The data includes import duty, export duty and excise obtained from the Directorate General of Customs and excise (DJBC) in the form of annual and quarterly data. This data starts from 2002 to 2016 with out of sample from 2017 to 2019. Some of these models are compared to each other to obtain the best model, and from the best model is also obtained estimating results in 3 years ahead. This study shows that the Double Exponential Smoothing model is better for predicting import duties compared to OLS and Moving Average models, which are models that have the smallest Sum Square Error (SSE) value. While the export and excise duty is best estimated by using OLS model which is shown with coefficient of determination value (R2) regression model of export duty is 0.8, while the excise regression model has coefficient of determination of 0.9.Keywords: Customs Estimation, Double Exponential Smoothing, Ordinary Least Square, Moving Average

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