scholarly journals FORECASTING THE CONSUMER PRICE INDEX IN YOGYAKARTA BY USING THE DOUBLE EXPONENTIAL SMOOTHING METHOD

2021 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Syintya Febriyanti ◽  
Wahyu Aji Pradana ◽  
Juliana Saputra Muhammad ◽  
Edy Widodo
Keyword(s):  
Data Collection ◽  
Price Index ◽  
Consumer Price Index ◽  
Original Data ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Monthly Data ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

The Consumer Price Index (CPI) is an indicator that is often used to measure the inflation rate in an area, or can be interpreted as a comparison between the prices of a commodity package from a group of goods or services consumed by households over a certain period time. The spread of COVID-19 throughout the world affects the economy in Indonesia, especially Yogyakarta. Forecasting CPI data during the COVID-19 pandemic has the benefit of being an illustration of data collection in the CPI of D.I Yogyakarta Province in the predicted period. This is useful as a comparison with the original data at the time of data collection and publication, as well as a consideration in making policies and improving the economy. Researchers use the Double Exponential Smoothing (DES) method to predict the CPI of Yogyakarta D.I Province, which aims to determine the best forecasting model and forecasting results. This method is rarely used in research on CPI data forecasting in Yogyakarta. The data in this study are monthly data from March 2020 to August 2021. The highest CPI in Yogyakarta occurred in August 2021 at 107.21 or 107.2, while the lowest CPI in Yogyakarta occurred in April 2020 at 105.15 or 105.2. The average CPI in Yogyakarta per month is 106.1. The Mean Absolute Percentage Error (MAPE) value obtained from the DES method is 0.1308443%, so that the accuracy of the model is 99.869%. Forecasting with the DES method is quite well used in forecasting the CPI data of Yogyakarta in September 2020 - November 2021. The results of CPI forecasting in Yogyakarta using the DES method were 107.2602, 107.3104, and 107.3606 from September-November.

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

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.

scholarly journals Aplikasi Peramalan Jumlah Siswa Sekolah Dasar di Kabupaten Tanah Laut Menggunakan Metode Holt’s Double Exponential Smoothing

2018 ◽  
Vol 7 (2) ◽  
pp. 64-69
Author(s):  
Nora Apriliyani ◽  
Herfia Rhomadhona ◽  
Jaka Permadi
Keyword(s):  
Exponential Smoothing ◽  
Learning Processes ◽  
Flow Diagram ◽  
Percentage Error ◽  
School Students ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Exponential Smoothing Method

Learning processes in the elementary schools at Tanah Laut District is affected by the number of students. Through the number of students can be predicted how much the need of additional teachers, rooms, textbooks and learning medias that support learning processes in the schools. In other words, the infrastructure of the schools can be predicted by the number of students that is registered in Tanah Laut District. The research use Holt’s Double Exponential Smoothing method to predicting the number of the prospective students in Tanah Laut District. Mean Absolute Percentage Error (MAPE) technique is used to calculate the percentage of error from the forecasting’s result. The system is designed by Entity Relationship Diagram (ERD) and Data Flow Diagram (DFD). The forecasting that have been done said that the number of Tanah Laut’s elementary school students at 2018 is 35655 students, with the value of MAPE is about 0.77%, a = 0.77 and ß = 0.8.

scholarly journals Prediksi Penjualan Kertas Menggunakan Metode Double Exponential Smoothing

2021 ◽  
Vol 9 (1) ◽  
pp. 60
Author(s):  
Erinsyah Aditya Nugroho Putro ◽  
Elistya Rimawati ◽  
Retno Tri Vulandari
Keyword(s):  
Raw Materials ◽  
Exponential Smoothing ◽  
Economic Losses ◽  
Percentage Error ◽  
Business Owner ◽  
Know How ◽  
Goods And Services ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

One of the important thing in business is the inventory of goods and services. Business goal can be reached when business owner know how the number of their inventory. Printing business is using forecasting model in their purchasing raw materials to estimate and calculate their selling prediction. That model is used to minimize economic losses when the costumer canceled order because paper was ran out and to prevent paper damage does not occur date to storage that to long. Double Exponential Smoothing method is used in this research to predict the sales of Paper A and HVS A3+ paper and calculates the prediction error with MAPE (Mean Absolute Percentage Error). This study aims to make an accurate forecasting application. The prediction results from application are in the form of prediction calculations for sales in the following month which will be used to optimize the purchase of paper to be sold. In applying the research results of Paper A and HVS A3 +, the best alpha was obtained in the 12th period, namely 0.3 and 0.6 with a MAPE error of 12% and 18% and an accuracy rate of 88% and 82% where the alpha was used to predict period 13 and produces a forecast value of 446 for Paper A and 474 for HVS A3 +

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.

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 PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

2021 ◽  
Vol 10 (3) ◽  
pp. 325-336
Author(s):  
Anes Desduana Selasakmida ◽  
Tarno Tarno ◽  
Triastuti Wuryandari
Keyword(s):  
Time Series ◽  
Precious Metal ◽  
Exponential Smoothing ◽  
Fuzzy Time Series ◽  
Monthly Data ◽  
Toxic Gases ◽  
Double Exponential ◽  
Speed Up ◽  
Past Data ◽  
Double Exponential Smoothing

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data. 

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

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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