scholarly journals Implementasi Metode Weighted Moving Average Untuk Sistem Peramalan Penjualan Markas Coffee

2021 ◽  
Vol 6 (3) ◽  
pp. 154
Author(s):  
Muchamad Rizqi ◽  
Antonius Cahya ◽  
Nova El Maida
Keyword(s):  
Information System ◽  
Calculation Result ◽  
Moving Average ◽  
Percentage Error ◽  
Arabica Coffee ◽  
Previous Period ◽  
Robusta Coffee ◽  
Forecasting Method ◽  
The Mean ◽  
Weighted Moving Average

Headquarters Coffee is one of the businesses engaged in the culinary field of coffee drinks. The problem that occurs at the Coffee Headquarters is that business activities are still carried out manually. In addition, determining sales in the next period only refers to the sales data of the previous period, resulting in owners often experiencing shortages or excess stocks of coffee to be sold due to uncertain sales. Therefore we need a forecasting method (Forecasting) that is appropriate and can be applied to an Information System in the form of a Website. The purpose of making this forecasting information system is to assist companies in recording sales to make it more practical by applying the Weighted Moving Average (WMA) method. From the results of the calculation of the WMA method, the level of accuracy will then be calculated using the Mean Absolute Percentage Error (MAPE) method. The results of forecasting by applying the WMA method and MAPE calculations on weights 3, 4 and 5 show that the Robusta coffee on the Robusta menu which has the smallest MAPE is weight 3 with a calculation result of 19.2499 and the Robusta Milk menu which has the smallest MAPE is weight 4 with the calculation result is 15.21879166 and Excelsa coffee on the excelsa menu which has the smallest MAPE is weight 3 with a calculation result of 19.1538 and the Excelsa Susu menu which has the smallest MAPE is weight 5 with a calculation of 17.27650182 while for Arabica coffee on the Arabica menu which has the smallest MAPE is weight 4 with a calculation result of 18.1735 and the Arabica Susu menu which has the smallest MAPE is weight 5 with a calculation result of 16.24012072. Where the Mape value produced by each type of coffee is still below 20%, which means the forecasting results can be categorized as good.

AN EXPONENTIALLY WEIGHTED MOVING AVERAGE METHOD WITH DESIGNED INPUT DATA ASSIGNMENTS FOR FORECASTING LIME PRICES IN THAILAND

2017 ◽  
Vol 79 (6) ◽  
Author(s):  
Thitima Booranawong ◽  
Apidet Booranawong
Keyword(s):  
Input Data ◽  
Moving Average ◽  
Weighting Factor ◽  
Percentage Error ◽  
Exponentially Weighted Moving Average ◽  
Monthly Data ◽  
Forecasting Error ◽  
The Mean ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In this paper, the Exponentially Weighted Moving Average (EWMA) method with designed input data assignments (i.e. the proposed method) is presented to forecast lime prices in Thailand during January 2016 to December 2016. The lime prices from January 2011 to December 2015 as the input data are gathered from the website’s database of Simummuang market, which is one of the big markets in Thailand. The novelty of our paper is that although the performance of the EWMA method significantly decreases when applying to forecast data which show trend and seasonality behaviors and the EWMA method is used for short-range forecasting (i.e. usually one month into the future), the proposed method can properly handle such mentioned problems. For this purpose, to forecast lime prices, five different input data are intently defined before assigned to the EWMA method: a) the monthly data of the year 2015 (i.e. the recent year data), b) the average monthly data of the year 2011 to 2015, c) the median of the monthly data of the year 2011 to 2015, d) the monthly data of the year 2011 to 2015 after applying the linear weighting factor, where the higher weight value is applied to the recent data, and e) the average monthly data of the year 2011 to 2015 after applying the exponential weighting factor, where the higher weight is also applied to the recent data. These designed input data are used as agents of the raw data. Our study reveals that using the input data b) with the EWMA method to forecast lime prices during January 2016 to September 2016 gives the smallest forecasting error measured by the Mean Absolute Percentage Error (MAPE). Forecasted lime prices of October 2016 to December 2016 are also provided. Additionally, we demonstrate that the proposed method works well compared with the Double Exponentially Weighted Moving Average (DEWMA), the Multiplicative Holt-Winters (MHW), and the Additive Holt-Winters (AHW) methods, which are suitably used for forecasting data with the trend and the seasonality.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals Sales Analysis Using the Forecasting Method

bit-Tech ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 146-149
Author(s):  
Amesanggeng Pataropura ◽  
Riki Riki ◽  
Ariadi Saputra
Keyword(s):  
Current Flow ◽  
Current System ◽  
Moving Average ◽  
Scientific Work ◽  
Transaction Data ◽  
Forecasting Method ◽  
Effectiveness And Efficiency ◽  
To Come ◽  
New System ◽  
Weighted Moving Average

Sales Analysis Using Forecasting Method aims to improve effectiveness and efficiency that facilitates companies in business transaction processes, improve the delivery of information quickly, accurately, and transaction data well and minimize errors. The method used in the presentation of this scientific work is by using a forecasting method which helps determine the approximate stock of goods to come. With 3 forecasting modules are: Moving Average, Weighted Moving Average, Trend Projection is used to perform the forecasting process of the upcoming stock of goods. Can solve problems that exist in the current system so that it can help in improving its services by calculating the stock and helping by determining the average data that has been linked to the forecasting module whose results can be concluded through reports per period. It can be concluded that the results of implementing this new system can help companies in recording each transaction that occurs becomes more efficient and effective, so that it can overcome the problems that exist in the current system. With this we can predict the current flow of goods that have been calculated based on 3 (three) modules that have connections with the system

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

2018 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
M. Tirtana Siregar ◽  
S. Pandiangan ◽  
Dian Anwar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Mean Absolute Percentage Error ◽  
Programming Method ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

Prediksi Kedatangan Turis Menggunakan Algoritma Weighted Exponential Moving Average

2020 ◽  
Vol 12 (2) ◽  
pp. 129-132
Author(s):  
Sherly Florencia ◽  
Alethea Suryadibrata
Keyword(s):  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Error Measurement ◽  
Absolute Percentage Error ◽  
Natural Beauty ◽  
Expenditure Data ◽  
The Mean ◽  
Tourism Expenditure ◽  
Using Data

Tourism is an important factor for the development of a country. Tourism can be used as a promotion to introduce natural beauty and cultural uniqueness. Government needs to predict how many tourists will come every year to do a planning. Therefore, an application is needed to help to predict the arrival of tourists in each country. In this paper, we use Weighted Exponential Moving Average (WEMA) method to predict the arrival of tourist, tourism expenditure in the country, and departure using data from 2008 to 2018. Error measurement is calculated using the Mean Absolute Percentage Error (MAPE). The result shows that the lowest average MAPE on arrival data with span 2 is at 3.28. The lowest average MAPE on tourism expenditure data with span 2 is at 3.99%. The result shows that the lowest average MAPE on departure data with span 2 is at 3.63%.

A Directional Multivariate Control Chart for Monitoring Univariate Autocorrelated Processes

2012 ◽  
Vol 217-219 ◽  
pp. 2607-2613
Author(s):  
Wen Wan Yang ◽  
Xue Min Zi ◽  
Chang Liang Zou
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Sign Test ◽  
Directional Information ◽  
Autocorrelated Process ◽  
Autocorrelated Processes ◽  
Multivariate Control Chart ◽  
The Mean ◽  
Multivariate Control ◽  
Weighted Moving Average

A new nonparametric multivariate control chart, based on a spatial-sign test and integrating the directional information from processes with the exponentially weighted moving average (EWMA) scheme, is developed for monitoring the mean of a univariate autocorrelated process. Simulation studies show that it has robustness in in-control (IC) performance, and it is more sensitive to the small and moderate mean shifts for non-normality underlying process than other existing multivariate chart methods.

scholarly journals Penerapan Ukuran Ketepatan Nilai Ramalan Data Deret Waktu dalam Seleksi Model Peramalan Volume Penjualan PT Satriamandiri Citramulia

2011 ◽  
Vol 2 (2) ◽  
pp. 636 ◽  
Author(s):  
Iwa Sungkawa ◽  
Ries Tri Megasari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Data Series ◽  
Past Data ◽  
The Mean

Forecasting is performed due to the complexity and uncertainty faced by a decision maker. This article discusses the selection of an appropriate forecasting model with time series data available. An appropriate forecasting model is required to estimate systematically about what is most likely to occur in the future based on past data series, so that errors (the differences between what actually happens and the results of the estimation) can be minimized. A gauge is required to detect the required the value of forecast accuracy. In this paper ways of forecasting accuracy of detection are discussed using the mean square error (MSE) and the mean absolute percentage error (MAPE). The forecasting method uses Moving Average, Exponential Smoothing, and Winters method. With the three methods forecast value is determined and the smallest value of MSE and Mape is selected. The results of data analysis showed that the Exponential Smoothing is considered an appropriate method to forecast the sales volume of PT Satriamandiri Citramulia because it produces the smallest value of MSE and Mape. 

scholarly journals Predicting Movement of Stock of Apple Inc. using Sutte Indicator

2017 ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Moving Average ◽  
Investment Decision ◽  
Technical Analysis ◽  
Percentage Error ◽  
Decision Making Process ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Investment Decision Making ◽  
The Mean

The purpose of this study is to apply technical analysis e.g. Sutte Indicator in Stock Market that will assist in the investment decision-making process to buy or sell of stocks. This study took data from Apple Inc. which listed in the NasdaqGS in the period of 1 January 2008 to 26 September 2016. Performance of the Sutte Indicator can be seen with comparison with other technical analysis e.g. Simple Moving Average (SMA) and Moving Average Convergence/Divergence (MACD). Comparison of the reliability of prediction from Sutte Indicator, SMA, and MACD using the mean of square error (MSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE).

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