scholarly journals Penerapan Single Exponential Smoothing (SES) dalam Perhitungan Jumlah Permintaan Air Mineral Pada PT. Akasha Wira International

2020 ◽  
Vol 3 (2) ◽  
pp. 87
Author(s):  
Lolyka Dewi Indrasari
Keyword(s):  
Mineral Water ◽  
Forecast Error ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Forecasting Error ◽  
Data Value ◽  
Forecast Data ◽  
Projection Error ◽  
Single Exponential

Daily needs that are priceless but useful for health one of which is mineral water. The need for mineral water increases with the high demand in the market. The purpose of this study was to determine the forecasting of the number of requests for 330 ml shortneck mineral water products in the future using the Single Exponential Smoothing (SES) method. Limitation of the problem is discussing the number of requests in the first half of 2020, the data used were obtained from PT. Akasha Wira International from January 2014 to December 2019. The analytical method is to calculate the forecast error value of the different 𝛼 values to find one value that produces the smallest error with the calculation method Mean Absolute Deviation (MAD) and Single Exponential Smoothing (SES) can interpreted based on the calculation stage where the forecast data value in the period 𝑡 + 1 is the actual value in the period t plus the adjustment derived from forecasting error that occurred in the period t. The results obtained on the value of Mean Absolute Deviation (MAD) are taken at a = 0.9 because it produces the smallest value of the projected data projection error of 1860 units. Whereas in forecasting requests using Single Exponential Smoothing (SES), 330 ml shortneck mineral water in the first half of 2020 amounted to 2177634 units. Keyword : Mean Absolute Deviation, Single Exponential Smoothing, shortneck.Kebutuhan sehari – hari yang tidak ternilai harganya tapi berguna bagi kesehatan salah satunya adalah air mineral. Kebutuhan air mineral meningkat seiring dengan tingginya permintaan pada pasar. Tujuan penelitian ini, yaitu untuk mengetahui peramalan jumlah permintaan pada produk air mineral 330 ml shortneck dimasa mendatang menggunakan metode Single Exponential Smoothing (SES). Batasan masalah yaitu membahas jumlah permintaan dimasa mendatang semester I 2020, data yang digunakan diperoleh dari PT. Akasha Wira International pada Januari 2014 sampai dengan Desember 2019. Metode analisis yaitu Menghitung nilai kesalahan peramalan terhadap nilai 𝛼 yang berbeda beda untuk menemukan satu nilai 𝛼 yang menghasilkan kesalahan terkecil dengan metode perhitungan Mean Absolute Deviation (MAD) dan Single Exponential Smoothing (SES) dapat diartikan berdasarkan tahapan perhitungannya dimana nilai data ramalan pada periode 𝑡 + 1 merupakan nilai actual pada periode t ditambah dengan penyesuaian yang berasal dari kesalahan nilai peramalan yang terjadi pada periode t. Didapatkan hasil pada nilai Mean Absolute Deviation (MAD) diambil pada a = 0,9 karena menghasilkan nilai kesalahan proyeksi data pemrintaan paling kecil yaitu 1860 unit. Sedangkan pada peramalan permintaan menggunakan Single Exponential Smoothing (SES), air mineral 330 ml shortneck pada semester I tahun 2020 sebesar 2177634 unit.  Kata Kunci: Mean Absolute Deviation, Single Exponential Smoothing, shortneck 

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 Determining The Optimal Values Of Exponential Smoothing Constants Does Solver Really Work?

2013 ◽  
Vol 6 (3) ◽  
pp. 347-360 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Operations Management ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Management Science ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error ◽  
Optimal Values ◽  
The Impact

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants.

scholarly journals ANALISIS BULLWHIP EFFECT DENGAN MENGGUNAKAN METODE PERAMALAN PADA SUPPLY CHAIN DI DISTRIBUTOR PT. SEMEN TONASA (Studi Kasus: Distributor PT. Semen Tonasa)

ARIKA ◽  
2019 ◽  
Vol 13 (2) ◽  
pp. 113-126
Author(s):  
W. Latuny ◽  
Wisnu M. S. Picauly
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Tracking Signal ◽  
Single Exponential

Bullwhip effect merupakan fenomena pada supply chain, dimana adanya perbedaan jumlah permintaan konsumen ditiap periode baik itu semakin sedikit atau semakin banyak yang dapat berpengaruh pada semua tingkatan dalam supply chain. Hal itu juga yang dialami dari Sub Distributor PT. Padi Mas Prima yang mendistribusikan Produk Semen Tonasa pada tiap ritel di kota ambon yaitu ritel Aneka Guna,Ritel Benua dan Ritel Wayame Adapun tujuan penelitian ini Menganalisis Bullwhip Effect dengan metode peramalan dan meminimalisasi terjadinya bullwhip effect. Perhitungan bullwhip effect menggunakan pendekatan model Moving Average dan Single Exponential Smoothing yang akan dipilih berdasarkan Mean Absolute Deviation dan Tracking Signal Hasil dari penelitian model yang dipilih adalah model Single Exponential Smoothing diperoleh hasil dari peramalan selama 12 periode, dari hasil peramalan tersebut menunjukkan adanya penurunan nilai bullwhip effect pada Sub Distributor PT. Padi Mas Prima, yang sebelumnya 1.02 nilainya menjadi 0.18 dengan tingkat presentase penurunan sebesar 82.4%, Ritel Aneka Guna yang nilainya 1.07 menjadi 0.71 dengan tingkat presentase penurunan sebesar 33.6%, Ritel Benua yang nilainya 1.03 nilainya menurun menjadi 0.86 dengan tingkat presentase penurunan sebesar 16.5%, dan Ritel Wayame yang sebelumnya 1.10 nilainya menurun menjadi 0.96 dengan  tingkat presentase penurunan sebesar 12.7%. Dimana nilai bullwhip effect > 1.01 dapat diartikan bahwa terjadi amplifikasi permintaan, sedangkan nilai bullwhip effect < 1.01 dapat diartikan bahawa permintaan masih stabil atau terjadi penghalusan pola permintaan usaha perbaikan dilakukan dengan melakukan pemesanan produk pada supplier dengan memperhatikan jumlah persediaan yang ada, menjaga arus informasi permintaan dan penjualan produk, serta menjaga lead time agar tetap stabil.

scholarly journals Determining The Optimal Values Of Exponential Smoothing Constants – Does Solver Really Work?

2016 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Handanhal V. Ravinder
Keyword(s):  
Operations Management ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Management Science ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Squared Error ◽  
Optimal Values ◽  
The Impact

A key issue in exponential smoothing is the choice of the values of the smoothing constants used.  One approach that is becoming increasingly popular in introductory management science and operations management textbooks is the use of Solver, an Excel-based non-linear optimizer, to identify values of the smoothing constants that minimize a measure of forecast error like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE).  We point out some difficulties with this approach and suggest an easy fix. We examine the impact of initial forecasts on the smoothing constants and the idea of optimizing the initial forecast along with the smoothing constants.  We make recommendations on the use of Solver in the context of the teaching of forecasting and suggest that there is a better method than Solver to identify the appropriate smoothing constants.

scholarly journals Central Jakarta Rainfall Intensity Forecast using Single Exponential Smoothing

2019 ◽  
Vol 8 (4) ◽  
pp. 2105-2108
Keyword(s):  
Rainfall Intensity ◽  
Forecast Error ◽  
Precipitation Amount ◽  
Exponential Smoothing ◽  
Vital Role ◽  
Capital City ◽  
Good Prediction ◽  
Intensity Prediction ◽  
Forecasting Method ◽  
Single Exponential

Rainfall is the precipitation amount that is falling from clouds. In extreme conditions, rainfall could arise many problems. It is the leading cause of landslides and flood disasters. In D.K.I. Jakarta, the capital city of Indonesia, rainfall intensity plays a very vital role since it could easily be puddled and caused floods in many areas. Therefore, in this study, we try to make a rainfall intensity prediction in Central Jakarta using a very popular forecasting method, i.e., the Single Exponential Smoothing (SES). Based on the experiments conducted using Phatsa, it can be concluded that the SES method has been successfully used to predict rainfall intensity. However, it cannot give a very good prediction result due to its high forecast error values.

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 Aplikasi Triple Exponential Smoothing Untuk Forecasting Jumlah Penduduk Miskin

2020 ◽  
Vol 3 (2) ◽  
pp. 76-82
Author(s):  
Padrul Jana
Keyword(s):  
Poverty Reduction ◽  
Mean Squared Error ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Poor People ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Absolute Percentage Error ◽  
The Government

This study aims to predict the number of poor in Indonesia for the next few years using a triple exponential smoothing method.The purpose of this research is the result of the forecast number of poor people in Indonesia accurate forecast results are used as an alternative data the government for consideration of government to determine the direction of national poverty reduction policies. This research includes the study of literature research, by applying the theory of forecasting to generate predictions of poor people for coming year. Furthermore, analyzing the mistakes of the methods used in terms of the count: Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean absolute percentage error (MAPE) and Mean Percentage Error (MPE). The function of this error analysis is to measure the accuracy of forecasting results that have been conducted.These results indicate that the number of poor people in 2017 amounted to 24,741,871 inhabitants, in 2018 amounted to 24,702,928 inhabitants, in 2019 amounted to 24,638,022 inhabitants and in 2020 amounted to 24,547,155 people. The forecasting results show an average reduction in the number of poor people in Indonesia last five years (2016-2020 years) ranges from 0.16 million. Analysis forecasting model obtained an mean absolute deviation (MAD) obtained by 0.246047. Mean squared error (MSE) of forecasting results with the original data by 1.693277. Mean absolute percentage error (MAPE) of 3.040307% and the final Mean percentage error (MPE) of 0.888134%.Kata Kunci: Forecasting, Triple Exponential Smoothing

scholarly journals Peramalan Penerbitan Ijin Mendirikan Bangunan Dengan Single Moving Average Dan Exponential Smoothing

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Yogha Pramana ◽  
Rukmi Sari Hartati ◽  
Komang Oka Saputra
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Mean Error ◽  
Absolute Percent Error

Ijin Mendirikan Bangunan adalah ijin yang diberikan oleh Kepala Daerah pada pemilik bangunan untuk mendirikan bangunan, mengubah, memperluas, mengurangi atau merawat bangunan sesuai dengan persyaratan administratif dan persyaratan teknis yang berlaku. Peramalan adalah merupakan perkiraan mengenai terjadinya suatu kejadian pada masa depan. Peramalan merupakan sebuah alat bantu yang penting dalam perencanaan yang efesien dan efektif. Prosesnya untuk mengetahui kebutuhan di masa datang antara lain kebutuhan ukuran kuantitas, kualitas, waktu dan lokasi untuk pemenuhan permintaan barang ataupun jasa. Peramalan merupakan bagian awal dari pengambilan suatu keputusan akhir. Data Ijin Mendirikan Bangunan (IMB) di hitung dengan metode Simple Moving Average dan Exponential Smoothing untuk mengetahui nilai dari Mean Error, Mean Absolute Deviation, Mean Square Error, Standar Error, Mean Absolute Percent Error.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PREDIKSI JUMLAH PENUMPANG UNTUK EVALUASI KAPASITAS HALTE BUS TRANS JOGJA DENGAN METODE EXPONENTIAL SMOOTHING DAN LEAST SQUARE

Compiler ◽  
2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Dwi Prasetiyo ◽  
Anton Setiawan Honggowibowo ◽  
Yuliani Indrianingsih
Keyword(s):  
Time Series ◽  
Waiting Time ◽  
Exponential Smoothing ◽  
Small Error ◽  
Least Square ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Bus Stop ◽  
Trend Forecasting ◽  
Bus Stops

The increasing number o f passengers Trans Jogja bus stops can result in the existing capacity can not accommodate the number of passengers comfortably. Problems that often arise include delays resulting bus passenger waiting time is longer and there is a buildup of the number of passengers at stops. As a result of these problems, the capacity o f passenger stops can be full so that prospective passengers waiting outside the bus stop. Forecasting is one very important element in the decision. In this study using stationary and trend forecasting the data because the data are not significant changes between time and swell in certain periods and a normal in periods others. Time series methods for forecasting the number o f passengers on the Trans Jogja stop using exponential smoothing calculation and least square. From these calculations the value sought MAD (Mean Absolute Deviation) or least square error is exponential smoothing and forecasting results with small error. Forecasting will be better if it contains fewer possible errors.

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