scholarly journals FIT OF STATISTICAL FORECASTING MODEL BERDASARKAN VARIABEL ANGKA KEMISKINAN DI PROVINSI KEPULAUAN BANGKA BELITUNG

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
DESY YULIANA DALIMUNTHE
Keyword(s):  
Economic Development ◽  
Mean Squared Error ◽  
Exponential Smoothing ◽  
Poverty Rate ◽  
Percentage Error ◽  
Projection Model ◽  
A Value ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing

Poverty is one of the main problems in economic development and is considered to be a variable to measure the success of the economic development of a region. This study is limited to the analysis and determination of the best forecasting statistical model for the variable poverty rate in the Bangka Belitung Islands Province area based on R Square, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) assessments. This study uses the Exponential Smoothing forecasting method which emphasizes the procedure of continuous improvement of the latest observation objects which hopefully can provide the appropriate results. In general, the double exponential smoothing model from Holt's is the best projection model compared to other exponential smoothing models for projecting poverty data in the Bangka Belitung Islands Province with historical data for 2002-2018 with an increase in projections in 2019 of 0.37 % with Upper Criteria Limit (UCL) of 1.07% and Lower Criteria Limit (LCL) of -0.33% with a value of R Square of 0.627 which means that the independent variable can explain the variance of the dependent variable of 62.7% of this model, and the value of RMSE is 0.328 and MAPE is 22.162. The results of this model when compared to other models have relatively larger R Squared values ??and smaller RMSE and MAPE values.

scholarly journals Penerapan Model Exponential Smoothing berbasis Metode Evolutionary pada Kasus COVID-19 dan DBD di Bojonegoro

2021 ◽  
Vol 6 (3) ◽  
pp. 174
Author(s):  
Denny Nurdiansyah ◽  
Khoirul Wafa
Keyword(s):  
Mean Squared Error ◽  
Visual Basic ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Squared Error ◽  
Double Exponential ◽  
Reduced Gradient ◽  
Double Exponential Smoothing ◽  
Generalized Reduced Gradient

Latar Belakang: COVID-19 menjadi perhatian utama di Bojonegoro karena kasus terinfeksi meningkat sampai akhir tahun 2020. Selain itu, wabah demam berdarah dengue (DBD) juga perlu diantisipasi di musim penghujan agar tidak meningkat bersamaan dengan wabah COVID-19.Tujuan: Mengembangkan model exponential smoothing berbasis metode evolutionary untuk meramalkan banyaknya kasus terinfeksi COVID-19 dan DBD di Bojonegoro.Metode: Penelitian diawali dengan pembuatan aplikasi peramalan model exponential smoothing dengan metode evolutionary dan pemrograman Visual Basic yang dikembangkan di Excel dan Solver. Koefisien-koefisien model dioptimasi secara iteratif dengan metode evolutionary dan metode generalized reduced gradient. Model tersebut dievaluasi kinerjanya dengan nilai mean absolute percentage error (MAPE), mean absolute deviation (MAD), dan mean squared error (MSE). Sumber data penelitian menggunakan data sekunder dari Dinas Kesehatan Bojonegoro yang berisi data harian kasus terinfeksi COVID-19 dan data bulanan kasus DBD.Hasil: Model double exponential smoothing berbasis metode generalized reduced gradientmenghasilkan kesalahan model peramalan yang lebih kecil untuk nilai MAPE, MAD, dan MSE. Hasil peramalan menunjukkan bahwapeningkatan terjadi pada periode ke depan untuk kasus terinfeksi COVID-19 yang lebih besar dibandingkan DBD.Kesimpulan: Aplikasi peramalan model exponential smoothing dapat menjadi altenatif dalam meramalkan banyaknya kasus terinfeksi COVID-19 dan DBD di Bojonegoro.

Forecasting Bitcoin using Double Exponential Smoothing Method Based on Mean Absolute Percentage Error

2020 ◽  
Vol 4 (2) ◽  
pp. 91
Author(s):  
Febri Liantoni ◽  
Arif Agusti
Keyword(s):  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Business World ◽  
Absolute Percentage Error ◽  
A Value ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

Abstract— After being introduced in 2008, the rise in the price of bitcoin and the popularity of other cryptocurrencies triggered a growing discussion about how much energy was consumed during the production of this currency. Making cryptocurrency the most expensive and most popular, both the business world and the research community have begun to study the devel-opment of bitcoin. In this study bitcoin price predictions are performed using the double exponential smoothing method based on the mean absolute percentage error (MAPE). The MAPE value is used to find the best alpha (α) parameter as the basis for bitcoin price forecasting. The dataset used is the price of bitcoin from 2017 to 2019. The dataset was obtained from www.cryptocompare.com. As for the value of the alpha parameter (α), using a value of 0.1 to 0.9. Based on the test results using the double exponential smoothing method obtained the smallest MAPE value of 2.89%, with the best alpha (α) at 0.9. The prediction is done to see the price of bitcoin on January 1, 2020. The error rate generated on the predicted price of bitcoin uses an amount of 0.0373%. This shows that the system built can be used as a support for decision making when trading bitcoin.

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 Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing

2021 ◽  
Vol 3 (4) ◽  
pp. 45-53
Author(s):  
Tresna Maulana Fahrudin ◽  
Prismahardi Aji Riyantoko ◽  
Kartika Maulida Hindrayani ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Time Series Data ◽  
Polynomial Regression ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Training Set ◽  
Double Exponential ◽  
Forecasting Performance ◽  
One Step ◽  
Double Exponential Smoothing

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.

scholarly journals TOPSIS dan Double Exponential Smoothing untuk Perangkingan dan Peramalan Penjualan Laptop

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suto Sugiraharjo ◽  
Rina Candra Noor Santi
Keyword(s):  
Data Mining ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Topsis Method ◽  
Data Mining Techniques ◽  
A Value ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Using Data ◽  
Exponential Smoothing Method

Problems that occur CV. Mustika Rajawali, which deals with laptop sales ranking and forecasting, is how to predict future laptop sales based on previous sales data. Forecasting is very influential in determining the sales target that must be achieved by CV. Mustika Rajawali. The method has not been used in predicting laptop sales at CV. Mustika Rajawali so that consumers' needs can be seen, whether it has met the sales target or not. The products to be developed in this study are laptop sales ranking and forecasting using the TOPSIS method and double exponential smoothing. To calculate the potential sales as accurately as possible, it can be done using data mining techniques using double exponential smoothing, while the TOPSIS method is used for ranking. Ranking of laptop sales using the TOPSIS method obtained the sales order of Asus A490JA laptops, Asus A409JP, Asus A409MA, Asus E402YA, Asus TP203NAH. Prediction of laptop sales at CV. Mustika Rajawali with a value of α = 0.1 to α = 0.9 obtained the smallest MAE value using α = 0.9, which is 178,237,067 so that the prediction of CV sales. Mustika Rajawali with the exponential smoothing method using a value of α = 0.9.

Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

2020 ◽  
Vol 12 (2) ◽  
pp. 95-103
Author(s):  
Andini Diyah Pramesti ◽  
Mohamad Jajuli ◽  
Betha Nurina Sari
Keyword(s):  
Population Distribution ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Population Census ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Better Than ◽  
Population Numbers

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

scholarly journals PERBANDINGAN METODE FUZZY TIME SERIES DAN HOLT DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN JUMLAH MAHASISWA BARU INSTITUT PERTANIAN BOGOR

2013 ◽  
Vol 12 (2) ◽  
pp. 25
Author(s):  
S. STEVEN ◽  
S. NURDIATI ◽  
F. BUKHARI
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Peramalan merupakan kegiatan memprediksi nilai suatu variabel di masa yang akan datang. Tujuan penelitian ini adalah memprediksi jumlah mahasiswa baru Institut Pertanian Bogor dengan menggunakan metode fuzzy time series dan metode pemulusan eksponensial ganda dari Holt serta membandingkan kedua metode tersebut dengan cara melihat tingkat ketepatan peramalan Mean Absolute Percentage Error (MAPE). Metode fuzzy time series menggunakan himpunan fuzzy dalam proses peramalannya sedangkan metode pemulusan eksponensial ganda dari Holt menggunakan pemulusan nilai dari serentetan data dengan cara menguranginya secara eksponensial. Dalam meramalkan jumlah mahasiswa baru Institut Pertanian Bogor, metode fuzzy time series menghasilkan tingkat ketepatan peramalan yang lebih baik dengan nilai MAPE sebesar 6.41 % dibandingkan dengan metode pemulusan eksponensial ganda dari Holt dengan nilai MAPE sebesar 7.75 %. Setelah dilakukan studi kasus, metode pemulusan eksponensial ganda dari Holt akan lebih akurat hasil peramalannya jika data yang digunakan lebih banyak.

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 Sistem Informasi Peramalan Penjualan Barang Dengan Metode Double Exponential Smoothing (Studi Kasus Istana Sayur)

2020 ◽  
Vol 6 (3) ◽  
pp. 9-14
Author(s):  
Yuri Ariyanto ◽  
Ahmadi Yuli Ananta ◽  
Muhammad Robbi Darwis Darwis
Keyword(s):  
Forecast Error ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Abstrak—Istana Sayur merupakan salah satu toko yang menjual beberapa macam sayuran, buah buahan dan bahan makanan yang selalu berusaha meningkatkan dan menjaga kualitas layanan, mencoba mengurangi kerugian dari pengendalian persediaan stok barang secara manual yang kurang baik akibat kelebihan dan kekurangan stok yang dialami saat ini, maka diperlukan fitur sebagai sistem informasi kasir dan peramalan stok barang. Tujuan dari pembuatan sistem informasi ini adalah analisa Forecasting secara manual ke dalam sebuah sistem informasi agar lebih praktis, dengan pemrograman PHP berframework CodeIgniter dan MySQL sebagai databasenya. Dengan menggunakan metode Double Exponential Smoothing Holt untuk pengambilan keputusan dalam jangka waktu tertentu dan pemanfaatkan pergerakan data pada masa lalu yang bersifat trend dimana datanya bersifat linier. Setelah dilakukan observasi pada Istana Sayur, Malang, didapat data transaksi penjualan dan barang pada tahun 2016-2018. Dari hasil perhitungan metode yang dipakai pada sistem ini kemudian dihitung Forecast Error-nya dengan menggunakan metode Mean Absolute Percentage Error. Dari analisa yang telah dilakukan, didapatkan hasil bahwa dengan menggunakan Mean Absolute Percentage Error didapat nilai untuk Sawi Caisim Manis dengan nilai 15.05%, Telor Ayam dengan nilai 15.78%, Cabe Hijau dengan nilai 12.45%, Buncis dengan nilai 22.22%, Cengkeh dengan nilai 34.69%, Bawang Putih dengan nilai 19.53%, Tempe dengan nilai 20.60% dan Kentang dengan nilai 17.58%. Sehingga Sawi Caisim Manis, Telor Ayam, Cabe Hijau, Bawang Putih dan Kentang tergolong kedalam kategori baik karena memiliki nilai diantara 10%-20%. Sedangkan untuk Buncis, Cengkeh dan Tempe tergolong kedalam kategori cukup karena memiliki nilai diantara 20%-50%. Saran untuk pengembangan aplikasi ini adalah perlunya penambahan metode lain sebagai pembanding tingkat keakuratan.

scholarly journals MINIMASI BIAYA PRODUKSI PADA PRODUK WINKER RELAY MELALUI PERENCANAAN PRODUKSI AGREGAT

2019 ◽  
Vol 4 (2) ◽  
pp. 143-152
Author(s):  
Yevita Nursyanti
Keyword(s):  
Quantitative Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
Aggregate Production Planning ◽  
Regression Methods ◽  
A Value ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Minimum Costs

The purpose of this research is to determine demand forecasting on relay winker products and to determine the aggregate planning of relay winker products that produce minimum costs. The method used in this study is a comparative quantitative method. The data used in conducting aggregate planning are data from forecasting calculations with the chosen method, forecasting is calculated using several methods, namely the Moving Average, Exponential Smoothing, Double Exponential Smoothing, Quadratic and Linear Regression methods. Select this method by looking for the MAPE value. The method that has the smallest MAPE value is Double exponential Smoothing with a value of 2.7%. Aggregate production planning is carried out for 2017 with the method of transportation (least cost), permanent labor and level strategy. The results of data processing show the best method for aggregate planning is transportation method (least cost) which has the lowest cost of Rp. 9,858,625,445.

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