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 Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

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 Penjualan dengan Metode Exponential Smoothing (Studi Kasus : Penjualan Bakso Kemasaan/Kiloan Rumah Bakso Bang Ipul)

2019 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Hisyam Ihsan ◽  
Rahmat Syam ◽  
Fahrul Ahmad
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Exponential Smoothing ◽  
Trial And Error ◽  
Squared Error ◽  
Forecasting Method ◽  
Operational Activities ◽  
Exponential Smoothing Method ◽  
Single Exponential

Abstrak. Peramalan penjualan memungkinkan sebuah perusahan memilih kebijakan yang optimal untuk membuat keputusan yang sesuai dan mempertahankan efisiensi dari kegiatan operasional. Rumah Bakso Bang Ipul adalah salah satu usaha yang melakukan penjualan yakni penjualan bakso kemasaan/kiloan. Oleh sebab itu,. Rumah Bakso Bang Ipul sangat memerlukan peramalan penjualan untuk meningkatkan keuntungan dan menghindari terjadinya kelebihan atau kekurangan persedian bakso kemasaan/kiloan. Penelitian ini dilakukan peramalan dengan metode exponential smoothing. Adapun parameter atau a yang digunakan dalam meramalkan penjualan adalah a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, dan 0.9. Singel exponential smoothing melakukan perbandingan dalam menentukan nilai a, dengan mencari nilai a tersebut secara trial and error sampai menemukan a yang memiliki error minimum dengan pencarian menggunakan metode mean absolute error (MAE) dan metode Mean Squaered error (MSE). Sehingga dipilih a = 0.1 dengan nilai MAE = 6.23 dan nilai MSE = 58.32. berdasarkan hasil ini, dengan menggunakan metode singel exponential smoothing dan a =0.1 diperoleh hasil peramalan penjualan bakso bang ipul pada bulan juni 2018 sebanyak 48 kilogram.Kata Kunci: Peramalan, Metode Exponential Smoothing, Metode Singel Exponential SmoothingAbstract. Sales forecasting enables an optimal policy of the company had to make the appropriate decision and maintain the efficiency of operational activities. Rumah Bakso Bang Ipul is a business that sells packaged meatballs. Therefore, Rumah Bakso Bang Ipul is in need of sales forecasting to increase profit and avoid the occurrence or lack of supply of packaged meatballs. This research was conducted by the method of exponential smoothing forecasting. As for parameter or a used predicting sales is a = 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8, and 0.9. single exponential smoothing do a comparison in determining the value of a, by searching for the value of such a trial and error to find a that has minimum error with search method using the mean absolute error (MAE) and mean squared error (MSE). So that selected a = 0.1 with MAE value = 6.23 and MSE Value = 58.32. Based on  these results, using the method of single exponential smoothing and retrieved results forecasting Rumah Bakso Bang Ipul in July 2018 as much as 48 kilograms.Keywords: Forecasting, Method of exponential smoothing, Method of single exponential smoothing.

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.

scholarly journals Analisis Pengendalian Persediaan Spare Part Mesin Produksi di PT. Prima Sejati Sejahtera dengan Metode Continuous Review

2017 ◽  
Vol 16 (2) ◽  
Author(s):  
Endah Budiningsih ◽  
Wakhid Ahmad Jauhari
Keyword(s):  
Inventory Control ◽  
Inventory Management ◽  
Control Method ◽  
Spare Part ◽  
Exponential Smoothing ◽  
Spare Parts ◽  
Optimal Number ◽  
Continuous Review ◽  
Forecasting Method ◽  
Single Exponential

<em>PT. Prima Sejati Sejahtera as one of the subsidiaries of PT. Pan Brothers Tbk. which is engaged in garment production. The company's mechanical department in managing spare part inventory is still using intuitive method, where the number of spare part order for certain periods based on spare part demands data onto the previous period. The company’s mechanical department often stock out of spare parts. Spare part’s inventory management becomes a complex issue because of the need for fast response to handle the downtime of machines, and the risk of obsolescence of spare parts. So in this research will discuss about spare part inventory control which is started with spare parts grouping by using ABC analysis method to determine the appropriate inventory control method for each group. There are 23 spare parts which included in group A. The forecast of spare part’s demands to use Croston, Syntetos-Boylan Approximation (SBA) and Single Exponential Smoothing (SES). Comparison of each forecasting method will be determined by the value of forecasting errors (MAD). It is known that there are 12 spare parts with Croston method in the best forecasting method, 6 spare parts in Syntetos-Boylan Approximation (SBA) method and 5 spare parts with Single Exponential Smoothing (SES) method. Based on the best forecasting result, it will be calculated the value of safety stock (SS), reorder point (ROP) and the optimal number of ordering (Q) using Continuous Review method for each spare part.</em>

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 Accuracy Analysis of Predictive Value in Transaction Data of Service Company Using Combination of K-Means Clustering and Time Series Methods

2021 ◽  
Vol 5 (1) ◽  
pp. 30-39
Author(s):  
Santi Ika Murpratiwi ◽  
Dewa Ayu Indah Cahya Dewi ◽  
Arik Aranta
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Transaction Data ◽  
Service Companies ◽  
Forecasting Method ◽  
Time Series Method ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Single Exponential

Profit decline is a frightening problem for service companies. The solution to prevent this is by analyzing data transactions using data mining and forecasting. K-Means used to cluster the level of car damage based on the number of panels repaired and the duration of repaired. The results of K-Means used as material for analysis the best time-series method for transaction data. The methods analyzed include the moving average, single exponential smoothing, double exponential smoothing, and winter's method. Single exponential smoothing is the most suitable forecasting method with transaction data. Based on the MAPE value obtained for minor damage of 12.58%, forecasting for moderate damage of 16.83%, forecasting for major damage of 17.31%, and forecasting for overall data of 8.0975%. It concluded that single exponential smoothing can apply with K-Means clustering and the company can use it to make strategies to prepare the number of workers and production materials required.

scholarly journals GOLDEN EXPONENTIAL SMOOTHING: A SELF-ADJUSTED METHOD FOR IDENTIFYING OPTIMUM ALPHA

2020 ◽  
Vol 5 (2) ◽  
pp. 587
Author(s):  
Fong Yeng Foo ◽  
Azrina Suhaimi ◽  
Soo Kum Yoke
Keyword(s):  
Golden Section ◽  
Exponential Smoothing ◽  
Series Data ◽  
Training Process ◽  
Times Series ◽  
Golden Section Search ◽  
Training Stage ◽  
Forecasting Method ◽  
Double Exponential Smoothing ◽  
Accuracy Of Prediction

The conventional double exponential smoothing is a forecasting method that troubles the forecaster with a tremendous choice of its parameter, alpha. The choice of alpha would greatly influence the accuracy of prediction. In this paper, an integrated forecasting method named Golden Exponential Smoothing (GES) was proposed to solve the problem. The conventional method was reformed and interposed with golden section search such that an optimum alpha which minimizes the errors of forecasting could be identified in the algorithm training process.  Numerical simulations of four sets of times series data were employed to test the efficiency of GES model. The findings show that the GES model was self-adjusted according to the situation and converged fast in the algorithm training process. The optimum alpha, which was identified from the algorithm training stage, demonstrated good performance in the stage of Model Testing and Usage.

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