scholarly journals Prediction of passenger train using fuzzy time series and percentage change methods

2021 ◽  
Vol 10 (6) ◽  
pp. 3007-3018
Author(s):  
Solikhin Solikhin ◽  
Septia Lutfi ◽  
Purnomo Purnomo ◽  
Hardiwinoto Hardiwinoto
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Rate Of Change ◽  
Smoothing Method ◽  
Fuzzy Time Series ◽  
Double Exponential ◽  
Passenger Volume ◽  
Double Exponential Smoothing ◽  
Railway Passenger ◽  
Exponential Smoothing Method

In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

PERBANDINGAN METODE FUZZY TIME SERIES HSU DAN DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR AMERIKA

2016 ◽  
Vol 1 (2) ◽  
pp. 153-162 ◽  
Author(s):  
Wulan Anggraeni
Keyword(s):  
Time Series ◽  
Error Rate ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Fuzzy Time Series ◽  
Series Method ◽  
Double Exponential ◽  
Time Series Method ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

The purpose of this study is to determine which accuracy is better between fuzzy time series method and Holt double exponential smoothing method. The data used is daily published rupiah exchange rate of Bank Indonesia in the period of 1 April 2016 until 17 june 2016. After being calculated, the error rate fuzzy time series Hsu method is at 0,6 %, while the error rate holt double exponential smoothing method is at 2,25%. Based on the calculation, it can be concluded that the error rate forecasting rupiah exchange rate using fuzzy time series method is lower than the holt double exponential smoothing It means that the fuzzy time series hsu method has better accuarcy than Holt double exponential smoothing method. The result of forecasting in 21, 22, 23, 24, and 25 respectively are Rp. 13355, Rp. 13375, Rp. 13395, Rp. 13465, Rp 13.475. Keywords: fuzzy time series, holt double exponential, forecasting

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 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 TOPSIS dan Double Exponential Smoothing untuk Perangkingan dan Peramalan Penjualan Laptop

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suto Sugiraharjo ◽  
Rina Candra Noor Santi
Keyword(s):  
Data Mining ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Topsis Method ◽  
Data Mining Techniques ◽  
A Value ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method

Problems that occur CV. Mustika Rajawali, which deals with laptop sales ranking and forecasting, is how to predict future laptop sales based on previous sales data. Forecasting is very influential in determining the sales target that must be achieved by CV. Mustika Rajawali. The method has not been used in predicting laptop sales at CV. Mustika Rajawali so that consumers' needs can be seen, whether it has met the sales target or not. The products to be developed in this study are laptop sales ranking and forecasting using the TOPSIS method and double exponential smoothing. To calculate the potential sales as accurately as possible, it can be done using data mining techniques using double exponential smoothing, while the TOPSIS method is used for ranking. Ranking of laptop sales using the TOPSIS method obtained the sales order of Asus A490JA laptops, Asus A409JP, Asus A409MA, Asus E402YA, Asus TP203NAH. Prediction of laptop sales at CV. Mustika Rajawali with a value of α = 0.1 to α = 0.9 obtained the smallest MAE value using α = 0.9, which is 178,237,067 so that the prediction of CV sales. Mustika Rajawali with the exponential smoothing method using a value of α = 0.9.

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.

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