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

JUDICIOUS ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 134-137
Author(s):  
Siti Juriah
Keyword(s):  
Moving Average ◽  
Control Process ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Smoothing Factor ◽  
Planning And Control ◽  
Shoe Industry ◽  
Forecasting Method ◽  
Exponential Smoothing Method ◽  
And Control

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.

scholarly journals ANALISA PERAMALAN WINTER’S EXPONENTIAL SMOOTHING BROWN DIBANDINGKAN DENGAN AUTOREGRESSIVE INTEGRATED MOVING AVERAGE PADA DATA JUMLAH KEJADIAN HIPERTENSI

2019 ◽  
Vol 4 (1) ◽  
pp. 1-10
Author(s):  
Wisoedhanie Widi .A ◽  
Nanik Dwi A.
Keyword(s):  
Community Health Centers ◽  
Moving Average ◽  
Secondary Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Preventive Action ◽  
Autoregressive Integrated Moving Average ◽  
Planning And Control ◽  
Exponential Smoothing Method ◽  
And Control

In an attempt to see and examine the situation and conditions that occur in the future to do forecasting (forecasting). Hypertension is a major disease in the ten Clinics Together and almost every month new hypertension cases occur, so the incidence of hypertension is becoming the trend and forecasting needs to be done. The purpose of this research is to do forecasting on the data the number of incident hypertension in Clinics With the city of Malang with Exponential Smoothing method using winter's Brown compared to Autoregressive Integrated Moving Arima. This type of research is the study of non reactive (non reactive research) which is a type of secondary data for research.Unit samples in this research are patients who come for the medication and patients in Clinics With hypertension Malang. in 2013 to 2016. Research data using Minitab software. The results of this study showed that both methods of forecasting results shows that tend to decrease in the year 2018 with the lowest incidence in December that as many as 58 incidents on Exponential Smoothing method of winter's and some 80 events on the method of Autoregressive Integrated Moving Average. The existence of a trend of decrease in the incidence of hypertension can be supported by the growing health services at community health centers With has been doing various efforts in preventive action, promotif and collaborative in the handling of problems Hypertension.Through these research results, it is advisable to draw up a health center party planning and control and eradication programs work for transmission of diseases of hypertension (P2P) with reference to the results of the forecasting incidence of hypertension in the year 2018.

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 PERAMALAN PENJUALAN PRODUK KERIPIK PISANG KEMASAN BUNGKUS (Studi Kasus : Home Industry Arwana Food Tembilahan)

2017 ◽  
Vol 11 (3) ◽  
pp. 135 ◽  
Author(s):  
Siti Wardah ◽  
Iskandar Iskandar
Keyword(s):  
Data Processing ◽  
Trend Analysis ◽  
Standard Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
A Value ◽  
Forecasting Method ◽  
Past Data ◽  
Product Sales ◽  
Exponential Smoothing Method

Peramalan adalah metode untuk memperkirakan suatu nilai dimasa depan dengan menggunakan data masa lalu. Penelitian ini dilakukan pada Home Industry Arwana Food. Pada penelitian ini, penulis membahas mengenai analisis peramalan penjualan produk kripik pisang untuk jenis kemasan bungkus. Peramalan yang dilakukan mengggunakan tiga metode yaitu metode Moving Average, metode Exponential Smoothing with Trend dan metode Trend Anayisis dengan membandingkan tingkat kesalahan (error) terkecil, maka metode peramalan yang  terpilih yaitu metode Trend Analysis, dengan nilai MAD sebesar 161,3539, MSE sebesar 55744,16, dan standar error sebesar 242,947. Dari analisis pengolahan data yang telah dilakukan berdasarkan metode peramalan yang terpilih, peramalan penjualan terhadap produk kripik pisang jenis kemasan bungkus adalah sebanyak 1121,424 atau 1122 bungkus/bulan, artinya pihak Home Industry Arwana Food Tembilahan harus menyediakan produk kripik pisang kemasan bungkus adalah sebanyak 1122 bungkus untuk tiap bulannya.      ABSTRACT Forecasting is a method to estimate a value of the future using past data. This research was conducted at the Home Industry Arowana Food. In this study, the authors discuss the analysis of product sales forecasting banana chips for this type of packaging wrap. Forecasting that do use traditional three methods are methods Moving Average, Exponential Smoothing method with Trend and Trend Anayisis method by comparing the level of errors (error) the smallest, then the selected forecasting method is the method of Trend Analysis, with a value of 161.3539 MAD, MSE of 55744 , 16, and the standard error of 242.947. From the analysis of data processing that has been carried out based on the method chosen forecasting, sales forecasting for products banana chips are as many types of packaging wrap 1121.424 or 1 122 packs / month, meaning the Home Industry Arowana Food Tembilahan must provide products banana chips wrapped packs is as much as 1122 wrap for each month.

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 METODE EXPONENTIAL SMOOTHING UNTUK PERAMALAN JUMLAH KLAIM DI BPJS KESEHATAN PAMEKASAN

2016 ◽  
Vol 2 (1) ◽  
pp. 46 ◽  
Author(s):  
Faisol Faisol ◽  
Sitti Aisah
Keyword(s):  
Time Series ◽  
Average Method ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Smoothing Method ◽  
Repair Procedure ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method

Time series model is the model used to predict the future using past data, one example of a time series model is exponential smoothing. Exponential smoothing method is a repair procedure performed continuously at forecasting the most recent data. In this study the exponential smoothing method is applied to predict the number of claims in the health BPJS Pamekasan using data from the period January 2014 to December 2015, the measures used to obtain the output of this research there are four stages, namely 1) the identification of data, 2) Modeling, 3) forecasting, 4) Evaluation of forecasting results with RMSE and MAPE. Based on the research methodology, the result for the period 25 = 833.828, the 26 = 800.256, period 27 = 766.684, a period of 28 = 733.113, period 29 = 699.541, and the period of 30 = 655, 970. Value for RMSE = 98.865 and MAPE = 7.002, In this case the moving average method is also used to compare the results of forecasting with double exponential smoothing method. Forecasting results for the period 25 = 899.208, the 26 = 885, 792, 27 = 872.375 period, a period of 28 = 858.958, period 29 = 845.542, and the period of 30 = 832.125. Value for RMSE = 101.131 and MAPE = 7.756. Both methods together - both have very good performance because the value of MAPE is below 10%, but the method of exponential smoothing has a value of RMSE and MAPE are smaller than the moving average method.

scholarly journals FORECASTING USES DOUBLE EXPONENTIAL SMOOTHING METHOD AND FORECASTING VERIFICATION USES TRACKING SIGNAL CONTROL CHART (CASE STUDY: IHK DATA OF EAST KALIMANTAN PROVINCE)

2020 ◽  
Vol 14 (1) ◽  
pp. 013-022
Author(s):  
Humairo Dyah Puji Habsari ◽  
Ika Purnamasari ◽  
Desi Yuniarti
Keyword(s):  
Control Chart ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Signal Control ◽  
East Kalimantan ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Tracking Signal

Abstrak Peramalan merupakan suatu teknik untuk memperkirakan suatu nilai pada masa yang akan datang dengan memperhatikan data masa lalu maupun data saat ini. Data yang menunjukan suatu trend, cocok dengan metode peramalan double exponential smoothing dari Brown atau metode double exponential smoothing dari Holt. Peramalan metode double exponential smoothing pada penelitian ini diaplikasikan pada data IHK Provinsi Kalimantan Timur periode Bulan Januari Tahun 2016 hingga Bulan Februari Tahun 2019 yang berpola trend. Tujuan dari penelitian ini adalah memperoleh hasil perbandingan akurasi metode peramalan double exponential smoothing berdasarkan nilai MAPE terkecil, memperoleh hasil verifikasi metode peramalan double exponential smoothing terbaik berdasarkan grafik pengendali tracking signal, dan memperoleh hasil peramalan menggunakan metode double exponential smoothing terbaik. Hasil penelitian menunjukkan metode peramalan terbaik adalah metode double exponential smoothing dari Holt dengan parameter  dan berdasarkan nilai MAPE terkecil sebesar 0,361% dan nilai tracking signal yang keseluruhan terkendali pada grafik pengendali tracking signal.   Kata kunci: Double Exponential Smoothing, IHK, MAPE, Tracking signal.   Abstract Forecasting is a technique for estimating a value in the future by looking at past and current data. Data that shows a trend, matches the Brown’s  exponential smoothing forecasting method or Holt's double exponential smoothing method. Forecasting of double exponential smoothing method in this study was applied to the IHK data of East Kalimantan Province for the period of January 2016 to February of 2019 which has a trend pattern. The purpose of this study was to obtain the results of the accuracy comparison of the double exponential smoothing forecasting method based on the smallest MAPE value, obtain the best verification results of the double exponential smoothing forecasting method based on tracking signal control charts, and obtain the best forecasting results using the double exponential smoothing method. The results showed that the best forecasting method was Holt's double exponential smoothing method with parameters  and based on the smallest MAPE value of 0.361% and the overall tracking signal value was controlled on the tracking signal control chart.  Keywords: Double Exponential Smoothing , IHK, MAPE, Tracking signal.  

scholarly journals Peramalan Produksi Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) di PT. XYZ

2019 ◽  
Vol 2 (1) ◽  
pp. 27
Author(s):  
Mohammad Buchori ◽  
Tedjo Sukmono
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Production Costs ◽  
Planning And Control ◽  
Forecasting Method ◽  
Time Series Method ◽  
Long Term Forecasting ◽  
Short Term Forecasting ◽  
And Control

In production planning and control the first step is to forecast to determine how much production, the company forecasting is still not optimal, because forecasting has an important role in a company. PT. XYZ is a food company that produces chicken meatballs and chicken dumplings. So from that this study uses the forecasting method Autoregressive Integreted Moving Average (ARIMA). ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the forecasting accuracy is not good. The purpose of this research is to get a good ARIMA model, used to forecast production in the company. So that the production becomes optimal and not excessive which can cause waste of raw materials, which will make production costs a lot. Data processing is done with the help of an Eviews computer program to determine a good ARIMA model, from processing data obtained by ARIMA (1.0,0). With the results obtained forecasting in the period 37 to period 48. 

scholarly journals Forecasting The Amount of Tuna/Madidihang (Yellowfin tuna) Landed in PPS Kutaraja Banda Aceh City With The Triple Exponential Smoothing Method

2019 ◽  
Vol 10 (1) ◽  
pp. 08-14
Author(s):  
Muhammad Haikal Nasution ◽  
Samsul Anwar ◽  
Aida Fitri ◽  
Aja Fatimah Zohra
Keyword(s):  
International Markets ◽  
Exponential Smoothing ◽  
Yellowfin Tuna ◽  
Smoothing Method ◽  
Local Market ◽  
Monthly Data ◽  
Forecasting Method ◽  
Aceh Province ◽  
Exponential Smoothing Method ◽  
Banda Aceh

The Kutaraja Ocean Fisheries Port (PPS) located in Banda Aceh City is central to the fisheries sector in Aceh Province. Various types of fish have been landed at Kutaraja PPS, one of which is tuna/madidihang (yellowfin tuna). Tuna is not only in demand by the local market, but also international markets, especially Japan and America. This study aims to estimate the amount of tuna/madidihang (yellowfin tuna) production landed at Kutaraja PPS in 2018 and 2019. These estimates can help the Aceh Government in controlling the ordering of tuna/madidihang (yellowfin tuna) from within and outside the country, so that the number of tuna/madidihang (yellowfin tuna) caught and ordered can be balanced so that stock control can run well. The forecasting method used in this study is the Triple Exponential Smoothing method by using monthly data on the amount of tuna/madidihang (yellowfin tuna) production landed at Kutaraja PPS from January 2010 to December 2017. Based on the results of forecasting with the best models, the amount of tuna/madidihang (yellowfin tuna) production will landed in the Kutaraja PPS in 2018 and 2019 are predicted to be 2,395,615.8 Kg and 2,451,207.5 Kg respectively.

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