scholarly journals PREDIKSI JUMLAH PENERIMAAN MAHASISWA BARU DENGAN METODE SINGLE EXPONENTIAL SMOOTHING (STUDI KASUS: AMIK ROYAL KISARAN)

JURTEKSI ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 125-132
Author(s):  
Wiwin Handoko
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Square ◽  
Computer Engineering ◽  
Study Program ◽  
History Of ◽  
Data History ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstract: A problem requires a solution to solve it. One of them is by using Prediction (Forcasting). Prediction is used to assess the prediction of conditions in the future. at AMIK Royal Kisaran, when it comes to making lecture schedules often hampered because there is no estimated number of students. The data used in this study is the data history of the last 15 Academic Years, from 2003/2004 to 2017/2018. Then the data is processed with the Single Exponential Smoothing Method. Alpha value 0 <α <1. Single Exponential Smoothing makes a comparison with the alpha value until alpha is found which has the minimum error. To find the value of the error, the MSE (Mean Square Error) method is used. The results of the testing of this method are in the academic year 2018/2019 prediction of the number of students for the Informatics Management Study Program as many as 89 people and for Students for the Computer Engineering Study Program as many as 30 people. The Single Exponential Smoothing method can predict the number of students in the next period. Keywords: Prediction; Number of Students; Single Exponential Smoothing; Alpha Value; MSE Abstrak: Suatu masalah memerlukan sebuah solusi untuk menyelesaikannya. Salah satunya dengan menggunakan Prediksi (Forcasting). Prediksi digunakan untuk menilai prakiraan keadaan dimasa. di AMIK Royal Kisaran, ketika akan membuat jadwal kuliah sering terhambat karena tidak adanya perkiraan jumlah mahasiswa. Data yang digunakan pada penelitian ini adalah histori data 15 Tahun Akademik terakhir, mulai 2003/2004 sampai dengan 2017/2018. Kemudian data diolah dengan Metode Single Exponential Smoothing. Nilai alpha 0<α<1. Single Exponential Smoothing melakukan perbandingan dengan nilai alpha tersebut sampai ditemukan alpha yang memiliki error paling minimum. Untuk mencari nilai Error digunakan Metode MSE (Mean Square Error). Hasil dari pengujian terhadap metode ini adalah pada Tahun akademik 2018/2019 prediksi jumlah Mahasiswa untuk Program Studi Manajemen Informatika sebanyak 89 orang  dan untuk Mahasiswa untuk Program Studi Teknik Komputer sebanyak 30 orang. Metode Single Exponential Smoothing dapat membantu prediksi jumlah mahasiwa pada satu periode kedepan Kata kunci: Prediksi; Jumlah Mahasiswa; Single Exponential Smoothing; Nilai Alpha; MSE 

scholarly journals Sistem Informasi Peramalan Persediaan Barang Menggunakan Metode SES Dan DES

2019 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
Ratih Yulia Hayuningtyas
Keyword(s):  
Medical Equipment ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Equal Weight ◽  
Mean Square ◽  
Double Exponential ◽  
Forecasting System ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstract: Sales is an activity in selling products that provide information about inventory. Arga Medical is a shop engaged in the sale of medical equipment, many of sales transactions in the Arga Medical will affect the inventory. Problems in the Arga Medical is predicting many of product that must available for the next month. Therefore this research makes inventory information forecasting system using Single Exponential Smoothing and Double Exponential Smoothing method. This inventory forecasting information system will result a inventory forecasting for next month. Single Exponential Smoothing Method gives equal weight to each data while Double Exponential Smoothing method is smoothing twice. The Data used in this research is the sales data during 2016. Both of these methods resulted inventory forecasting in the next month is Januari 2017 of 52 with Single Exponential Smoothing and 60 with Double Exponential Smoothing. Each method has a Mean Square Error value the smallest error value is the best method for forecasting inventory. Keywords: Forecasting, Inventory, Single Exponential Smoothing, Double Exponential Smoothing.

scholarly journals Peramalan Perencanaan Produksi Batu Tela Dengan Metode Exponential Smoothing (Studi Kasus Pada CV. Sinar Sowi Kabupaten Manokwari)

2019 ◽  
Vol 2 (2) ◽  
pp. 54-59
Author(s):  
Suwoko ◽  
Dirarini Sudarwadi ◽  
Nurwidianto
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Forecasting Error ◽  
Alpha Models ◽  
The Mean ◽  
Exponential Smoothing Method

This study aims to find out how much forecasting the production of concrete brick at CV. Sinar Sowi. The data analysis method used is the Exponential Smoothing method by using forecasting error measurements namely Mean Square Error (MSE) and Mean Absolute Deviation (MAD). From the data that has been analyzed, the writer can conclude that the use of alpha model 0.1 Exponential Smoothing method, the value of the Exponential Smoothing method, the value of Mean Square Error is 11,114,950 and the value of Mean Absolute Deviation is 962. The use of alpha 0.5 model Exponential Smoothing method, the value of Mean Square Error is 1,114,776 and the value of Mean Absolute Deviation is 305. While the use of the alpha 0.9 model is Exponential Smoothing, the Mean Square Error value is -9.374 and the Mean Absolute Deviation value is -28. Of the three existing alpha models, namely 0.1; 0.5 and 0.9, then what will be used in forecasting is alpha 0.9 because the error value is the lowest, namely the Mean Square Error of -9,374 and Mean Absolute Deviation is -28. From the calculation of concrete brick forecasting at CV. Sinar Sowi in Manokwari Regency, the forecasting results were 39,698 units.

scholarly journals Comparative Analysis of Single Exponential Smoothing and Holt's Method for Quality of Hospital Services Forecasting in General Hospital

2020 ◽  
Vol 1 (2) ◽  
pp. 80-86
Author(s):  
Rachmat Rachmat ◽  
Suhartono Suhartono
Keyword(s):  
Forecast Error ◽  
Comparison Method ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Absolute Deviation ◽  
Quality Health ◽  
History Of ◽  
The Difference ◽  
Exponential Smoothing Method ◽  
Single Exponential

The quality health service is one of the basic necessities of any person or customer. To predict the number of goods can be done in a way predicted. The comparison method of Single Exponential Smoothing and Holt's method is used to predict the accuracy of inpatient services will be back for the coming period. Single Exponential Smoothing the forecasting methods used for data stationary or data is relatively stable. Holt's method is used to test for a trend or data that has a tendency to increase or decrease in the long term. The outcome of this study is the Single Exponential Smoothing method is more precise than Holt's method because of the history of hospitalized patients who do not experience an increase or no trend. In addition, the percentage of error (the difference between the actual data with the forecast value) and Mean Absolute Deviation (MAD) to calculate the forecast error obtained from the Single Exponential Smoothing method is smaller compared to Holt's method.

scholarly journals PERBANDINGAN HASIL PERAMALAN JUMLAH WISATAWAN MANCANEGARA DENGAN METODE BOX-JENKINS DAN EXPONENTIAL SMOOTHING

2021 ◽  
Vol 2 (1) ◽  
pp. 1-13
Author(s):  
EMMA NOVITA SARI ◽  
BAMBANG SUSANTO ◽  
ADI SETIAWAN
Keyword(s):  
Mean Square Error ◽  
Original Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Square ◽  
Smoothing Methods ◽  
The Future ◽  
Exponential Smoothing Method

Forecasting the number of tourist visits is needed by tourism businesses to provide an overview of the number of tourists in the future so that problems that might occur can be overcome properly. This study aims to compare the results of forecasting the number of foreign tourists using the Box-Jenkins and Exponential Smoothing methods. There are two data used, namely data on the number of foreign visitors visiting Indonesia from January 2008 to December 2017 (Data I) and Bali according to the entrance of Ngurah Rai Airport from January 2009 to March 2020 (Data II). The best forecast results are obtained by comparing the Root of Mean Square Error (RMSE) values. The comparison of forecasting results in Data I shows that the Holt-Winters Exponential Smoothing method is more appropriate to predict the number of foreign tourists visiting Indonesia because it has a smaller RMSE value. While, the results of forecasting periods 2 and 3 in Data II show results that are far different from the original data. After tracking, it turns out this is caused by an unexpected factor, the Covid-19 pandemic which caused the number of tourists to drop significantly during this period.

scholarly journals Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method

2019 ◽  
Vol 125 ◽  
pp. 23006
Author(s):  
Dyna Marisa Khairina ◽  
Aqib Muaddam ◽  
Septya Maharani ◽  
Heliza Rahmania
Keyword(s):  
Reference Value ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Tax Revenue ◽  
Regional Office ◽  
Forecasting Error ◽  
The Government ◽  
Exponential Smoothing Method ◽  
Single Exponential

Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.

scholarly journals Forecasting production,consumption and the food gap of rice crop in Iraq using Exponential Smoothing method

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Iman K, Mustafa ◽  
Osamah.K. Jbara
Keyword(s):  
Exponential Smoothing ◽  
Smoothing Method ◽  
The Self ◽  
Rice Crop ◽  
Factors Affecting ◽  
Production Ratio ◽  
Self Sufficiency ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

"The aim of this research is to predict the production, consumption and food gap of the rice crop in Iraq, as well as the economic factors that affect the self-sufficiency ratio and the quantity of imports with the time series (2015-1980). Based on the statistical program (Minitab & SPSS) Is the Exponential Smoothing method for Forecasting the production, consumption, and nutritional gap of the rice crop. Two types of single and double (2016-2025) was the single Exponential Smoothing method for having the lowest MSE value of (11450.4) . As for the consumption of the rice yield for the period (2025-2016), the double Exponential Smoothing method was the most accurate (MSE), which is 87100.7. As for the food gap, the single Exponential Smoothing is the best predictor for the same period in terms of the lowest value (MSE) 84100.1. The self-sufficiency ratio was affected by five factors (cultivated area, Imports, available for consumption, import / production ratio, the dummy variable representing years of blockade), and Factors affecting the quantity of imports (rice production,available for consumption, border prices, the number of the population Al- Muthanna University All rights reserved"

scholarly journals PERBANDINGAN METODE SINGLE EXPONENTIAL SMOOTHING DAN MOVING AVERAGE PADA PERAMALAN PENJUALAN PRODUK MINYAK GORENG DI PT TUNAS BARU LAMPUNG

2021 ◽  
Vol 6 (2) ◽  
pp. 101
Author(s):  
Niken Chaerunnisa ◽  
Ade Momon
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Oil Products ◽  
Cooking Oil ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Product Sales ◽  
Exponential Smoothing Method ◽  
Single Exponential

PT Tunas Baru Lampung is a company that produces palm cooking oil products under the Rose Brand brand. In product sales, companies sometimes experience ups and downs. Based on the sales data from Rose Brand Cooking Oil, the size of 1 L has fluctuated or in each period it changes and is not always boarding. Even though product sales are one of the important things to be evaluated from time to time on an ongoing basis. To predict future sales, forecasting is done. The forecasting method used is Double Exponential Smoothing and Moving Average. The method of accuracy will be compared using MSE, MAD, and MAPE. The results showed a comparison of the accuracy and the smallest error value in each method. By using the weight values ​​0.1, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8 on the Single Exponential Smoothing method the weight value is 0.8 or α = 0.8, namely MSE of 250,570,764.80, MAD of 12,922.32 and MAPE of 33.55 Then, using the movement value n = 3 in the Moving Average method has an accuracy of 438,980,942.75 MSE, 18,142.14 MAD, and 41.37 MAPE. After comparing the accuracy of the two methods, the Single Exponential Smoothing method is the best method to predict sales of Rose Brand 1 L Cooking Oil products.

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