Forecasting Shoe Sales Volume with Exponential Smoothing Method at PT Kujang Utama Antasena - Bogor

PT Kujang Utama Antasena is a shoe industry company specifically for security. The purpose of this study is to forecast or predict sales. This study uses a quantitative method with exponential smoothing, smoothing factor/constant (?) of 0.2. In production activities, forecasting is carried out to determine the amount of demand for a product and is the first step of the production planning and control process to reduce uncertainty so that an estimate that is close to the actual situation is obtained. The exponential smoothing method is a moving average forecasting method that gives exponential or graded weights to the latest data so that the latest data will get a greater weight. In other words, the newer or more current the data, the greater the weight.

FORECASTING THE CONSUMER PRICE INDEX IN YOGYAKARTA BY USING THE DOUBLE 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.

ANALYSIS OF THE STRUCTURE AND PREDICTION OF THE MORBIDITY OF CHILDREN IN THE KAMENSKY DISTRICT

В статье приведены анализ и прогнозирование основных статистических показателей, характеризующих распространенность различных нозологических форм заболеваний среди детского населения Каменского района. Для определения качества медицинской помощи, предоставляемой в детской поликлинике, среди жителей района был проведен опрос, в ходе которого была выявлена частота посещения данного амбулаторно-поликлинического учреждения по поводу заболевания и с целью профилактики, оценен уровень оказываемой помощи по различным критериям, определены как положительные, так и отрицательные аспекты деятельности, а также предложены методы повышения эффективности работы поликлиники. С целью предвидения основных показателей заболеваемости был построен прогноз. В качестве данных для прогнозирования были использованы показатели заболеваемости детского населения прошлых лет. Прогнозирование осуществляется с помощью метода экспоненциального сглаживания с использованием линейного тренда и выбором оптимальных параметров сглаживания. Экспоненциальное сглаживание является интуитивным методом, который взвешивает наблюдаемые временные ряды неравномерно. Последние наблюдения взвешиваются более интенсивно, чем отдаленные наблюдения. Основной целью анализа и прогнозирования является выявление основных тенденций в изменении структуры заболеваемости, а также определение влияния качества и доступности оказываемых медицинских услуг в поликлинике на здоровье детского населения Каменского района The article presents the analysis and prediction of the main statistical indicators characterizing the prevalence of various nosological forms of diseases among the children of the Kamensky district. To determine the quality of medical care provided in the children's polyclinic, a survey was conducted among the residents of the district, during which the frequency of visits to this outpatient clinic for the disease and for the purpose of prevention was revealed, the level of care provided was assessed according to various criteria, both positive and negative aspects of activity were identified, and methods of improving the efficiency of the polyclinic were proposed. In order to anticipate the main indicators of morbidity, a forecast was built. The indicators of morbidity of the child population of previous years were used as data for forecasting. Forecasting is carried out using the exponential smoothing method using a linear trend and the choice of optimal smoothing parameters. Exponential smoothing is an intuitive method that weighs the observed time series unevenly. Recent observations are weighed more intensively than distant observations. The main purpose of the analysis and forecasting is to identify the main trends in the change in the structure of morbidity, as well as to determine the impact of the quality and availability of medical services provided in the polyclinic on the health of the children's population of the Kamensky district

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

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.

PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDAR UDARA INTERNASIONAL JUANDA MENGGUNAKAN METODE EXPONENTIAL SMOOTHING EVENT-BASED

The increase in the number of airplane passengers occurs at certain times, such as Eid al-Adha, Eid al-Fitr, and Christmas holidays. Of course, an excessive rise in the number of passengers can cause extreme flight traffic density so that which can cause flight delays, decreased airport service level performance, and other impacts. This study predicts the number of aircraft passengers at Juanda International Airport using the Exponential Smoothing Event-Based method. The Exponential Smoothing Event-Based method is a forecasting method that considers special events using the Exponential Smoothing method as the initial calculation. This study uses data on the number of passengers from January 2014 to December 2020. From the forecasting model, MAPE is 11.8905%, and MSE is 4202958561.0706, so that the resulting forecast can be categorized as good.

PEMILIHAN PARAMETER OPTIMUM MENGGUNAKAN EXPONENTIAL SMOOTHING DENGAN METODE GOLDEN SECTION UNTUK PERAMALAN JUMLAH TITIK PANAS DI KALIMANTAN TIMUR

The exponential smoothing method is one method that can be used to predict time series data by smoothing the data. In this study, the method used was exponential smoothing with one smoothing parameter from Brown. The data used is the number of hotspots in East Kalimantan from January 2019 to September 2019. The purpose of this study is to obtain the optimum smoothing parameter values for exponential smoothing from the results of the optimization process using the golden section method to minimize the MAPE value, to obtain forecasting results for each method in exponential smoothing for the number of hotspots in East Kalimantan from October to December 2019, and obtain a good exponential smoothing method to predict data on the number of hotspots in East Kalimantan. From this analysis, the researchers chose the methods used were DES and TES. The optimum smoothing parameter obtained at DES was 0,558430 and TES was 0,376352. The results of forecasting the number of hotspots obtained in DES in October were 2.142, November was 2.707, and December was 3.271 with a MAPE value of 95%. The TES method forecasting results were obtained in October as many as 2.193, November as much as 2.975, and December as many as 3.852 with a MAPE value of 108%. Based on the comparison of the MAPE values in the two methods, the DES method is better than the TES for calculating the predicted value of the number of hotspots in East Kalimantan, although the two methods are not yet suitable for handling this case.

Bootstrapped Holt Method with Autoregressive Coefficients Based on Harmony Search Algorithm

Exponential smoothing methods are one of the classical time series forecasting methods. It is well known that exponential smoothing methods are powerful forecasting methods. In these methods, exponential smoothing parameters are fixed on time, and they should be estimated with efficient optimization algorithms. According to the time series component, a suitable exponential smoothing method should be preferred. The Holt method can produce successful forecasting results for time series that have a trend. In this study, the Holt method is modified by using time-varying smoothing parameters instead of fixed on time. Smoothing parameters are obtained for each observation from first-order autoregressive models. The parameters of the autoregressive models are estimated by using a harmony search algorithm, and the forecasts are obtained with a subsampling bootstrap approach. The main contribution of the paper is to consider the time-varying smoothing parameters with autoregressive equations and use the bootstrap method in an exponential smoothing method. The real-world time series are used to show the forecasting performance of the proposed method.

Implementasi Metode Penghalusan Ekponensial Tunggal Dalam Prediksi Penjualan Buku

The prediction of the quantity of product sales in the future is intended to control the amount of existing product stock, so that product shortages or excess stock can be minimized. When the quantity of sales can be predicted accurately, the fulfillment of consumer demand can be sought on time and the cooperation of the store with the relationship is maintained well so that the store can avoid losing both sales and consumers. The purpose of this study is to compare the effectiveness of the use of the Single Exponential Smoothing method and methods Double Exponential Smoothing with a smoothing parameter value a = 0.5 for forecasting sales by comparing the error values in the two methods using the Mean Squared Error (MSE) method, the MSE results of the Single Exponential Smoothing method is 4967.75 while the MSE Double Exponential Smoothing is 5113.03. Thus, the Single Exponential Smoothing method is more accurate than Double Exponential Smoothing in calculating book sales forecasting because it has a low MSE value.

Comparison of Exponential Smoothing Methods for Forecasting Marine Fish Production in Pekalongan Waters, Central Java

Pekalongan waters, a part of the Java Sea, has potency to develop marine fisheries sector to increase regional income and community livelihoods. The fluctuation of marine fish production every year requires serious attention in planning and policy strategies for the utilization of the fishery resources. Time series fish production data can be used to predict fish production in the following years through the forecasting process. The data used in this study is fish production data from Pekalongan Fishing Port, Central Java, from January 2011 to December 2020. The method used is data exponential smoothing by comparing three exponential smoothing methods consisting of single/simple exponential smoothing, double exponential smoothing and Holt-Winters’ exponential smoothing. The criterion that used to measure the forecasting performance is the mean absolute percentage error (MAPE) value. The smaller MAPE value shows the better the forecasting result. The smallest MAPE value is obtained by finding the optimal smoothing constant value which is usually calculated using the trial and error method. However, in this study, the constant value was calculated using the add-in solver approach in Microsoft Excel. The forecasting results obtained show that forecasting using the Holt Winter Exponential Smoothing method is reasonable with a MAPE value of 37.878.

Methodological Approaches to Forecasting Bread Prices in Ukraine

The article examines the current state of the bread and bakery market in Ukraine on the basis of the exponential smoothing method. An important aspect of the analysis of the bakery industry state is the issue of pricing for the number one product in Ukraine – bread and bakery products. The purpose of this study was to analyze the level of bread prices in the regional context, to identify trends and factors influencing them and to propose models on the basis of which it is advisable to make operational forecasts of bread prices. The study was performed on the basis of monitoring data of average consumer prices for wheat bread from first grade flour by months of 2017 and 2018 in the selected regions, Kyiv and Ukraine as a whole. The choice of areas is done due to their territorial location, and the choice of bread type – due to steady popularity among consumers. The dynamics of product prices, in particular in the regional aspect, was analyzed. The example of wheat bread made from first grade flour shows significant differences in the price level for these products by regions. Trends in price changes and their dependence on such factors as the price of flour, the price of gasoline A-95, wages have been identified. The expediency of using for the estimation of bread prices of models based on series of dynamics by exponential smoothing is shown. High accuracy of the received models is confirmed. The proposed approach in this study can be used by industry to construct models of product price forecasting as a benchmark for making management decisions about the real price. Performing these calculations online on a computer will provide businesses with particular advantages over their competitors, as well as the ability to plan their economic performance at the desired level