scholarly journals Optimasi Parameter Pemulusan Algoritma Brown Menggunakan Metode Golden Section Untuk Prediksi Data Tren Positif dan Negatif

2018 ◽  
Vol 2 (1) ◽  
pp. 307-314 ◽  
Author(s):  
Fiqih Akbari ◽  
Arief Setyanto ◽  
Ferry Wahyu Wibowo
Keyword(s):  
Golden Section ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Good Forecast

Algoritma DES (Double Exponential Smoothing) Brown merupakan algoritma peramalan yang digunakan untuk memprediksi data deret berkala baik berpola tren positif maupun tren negatif. Namun algoritma ini mempunyai kelemahan yaitu dalam menentukan nilai parameter optimum untuk meminimasi error peramalan (MAPE), nilai parameter tersebut dicari menggunakan metode Golden Section dimana sebelumnya dicari secara manual menggunakan percobaan berulang kali. Penelitian ini menggunakan 60 data berpola tren yang dianalisis untuk pengelompokan pola data tren positif dan negatif dimana selanjutnya dilakukan proses peramalan, evaluasi dan pengujian untuk mengetahui jenis pola data tren apa yang terbaik. Dari hasil perhitungan dan pengujian diketahui bahwa parameter optimasi menghasilkan nilai MAPE yang optimum, dimana selanjutnya nilai parameter tersebut dilakukan proses peramalan pada kelompok pola data tren positif dan negatif yang menghasilkan rata-rata nilai MAPE sebesar 9,73401% (highly accurate) untuk data berpola tren positif dan 15,78467% (good forecast) untuk data berpola tren negatif. Algoritma peramalan DES Brown dengan metode optimasi parameter menghasilkan nilai pendekatan terhadap data asli jika data tersebut menunjukkan penambahan atau penurunan nilai disekitar nilai rata-rata. Sebaliknya, akan menghasilkan nilai MAPE yang tinggi (tidak akurat) jika data tersebut memiliki lonjakan periode nilai data. Dari kedua kelompok nilai MAPE tersebut dilakukan uji t statistik yang menyatakan bahwa data berpola tren positif () menghasilkan nilai rata-rata MAPE lebih baik dibandingkan data berpola tren negatif (μ2).  

scholarly journals A Comparison between Brown’s and Holt’s Double Exponential Smoothing for Forecasting Applied Generation Electrical Energies in Kurdistan Region

2021 ◽  
Vol 5 (2) ◽  
pp. 56-63
Author(s):  
Ameera W. Omer ◽  
Hazhar T. A. Blbas ◽  
Dler H. Kadir
Keyword(s):  
Time Series ◽  
Golden Section ◽  
Evaluation Process ◽  
Electrical Energy ◽  
Exponential Smoothing ◽  
Electricity Production ◽  
Kurdistan Region ◽  
Double Exponential ◽  
The Difference ◽  
Double Exponential Smoothing

The process of producing electricity from sources of energy is known as electricity production. Electric also isn't freely accessible in environment, thus it should be "manufactured" (i.e., converting another kinds of energy to electrical energy) by utilities with in electricity industry (transportation, distributing, and so on).Moreover, the objective of this study is to compared of Brown’s as well as Holt’s Double Exponential Smoothing also build a best forecasting time series model among two smoothing model forecasting, as well as focuses on optimizing characteristics to use the golden section technique.  This exponential smoothing approach has been one of the time series forecasting methods that would be used to forecast (Generation Electrical) with in Kurdistan area. The issue that arises with this technique is determining the appropriate parameters to reduce predict inaccuracy. In addition, Data used in this paper are (Generation Electrical) in Kurdistan region for (132) months from 2010 to 2020. The study revealed that such data is trending modeled, indicating that a double exponential smoothing (DES) approach from Brown & Holt can be used with the (Stratigraphic & Minitab) software. There are the same results but the Result of analysis more depend on the R-program. The difference among the forecast findings acquired with optimum parameters as well as the assaying data was utilized to assess the feasibility of the forecast by completing normality and randomness tests. Ultimately, the outcomes of parameterization show that the optimal value of α that in DES Brown is (0.22) as well as the optimal MAPE is 9.23616 percent, whereas in DES Holt the optimal is (0.95) as well as the optimal β is (0.05) via the optimal MAPE of 8.08586 percent. This MAPE of a DES Brown technique is greater than the MAPE of a DES Holt approach. Feasibility experiments revealed that both approaches are capable of predicting. Depending on the value of MAPE as well as evaluation process, DES Holt's was recognized as the main prediction model.

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.

scholarly journals Pengendalian Persediaan Bahan Baku Nalco Water Treatment Dengan Menggunakan Metode Lot Sizing

2019 ◽  
Vol 2 (2) ◽  
pp. 80
Author(s):  
Masad Hariyadi ◽  
Boy Isma Putra
Keyword(s):  
Water Treatment ◽  
Raw Materials ◽  
Unit Cost ◽  
Exponential Smoothing ◽  
Raw Material ◽  
Economic Order Quantity ◽  
Economic Order ◽  
Order Quantity ◽  
Double Exponential ◽  
Double Exponential Smoothing

The limited supply of Nalco raw materials from producers has become a problem for PT ABC, this has led to the control of raw material inventory at PT ABC not including good management, because in the management of raw materials the company still records inventory with manual systems and in ordering raw materials only based on estimates. From the results of the study, the forecasting method used is the Double Exponential Smoothing Holt's, Brown, and Holt Winters Additive Algorithm methods, from the three methods that are most suitable is the Double Exponential Smoothing Brown method with the smallest Mean Square Error of 256.2. Calculation of Sizing Lot by using Economic Order Quantity method, Least Unit Cost method, and Silver Meal method, of the three methods the most optimal is the Economic Order Quantity method because it has the lowest cost of Rp. 12,651,145. The calculation of Safety Stock gets 17 Pail results. and for Reorder Points for Nalco Water Treatment raw material, which is 29 Pail.

scholarly journals Peramalan Kurs IDR Terhadap USD Menggunakan Double Moving Averages Dan Double Exponential Smoothing

2019 ◽  
Vol 2 (2) ◽  
pp. 48-55
Author(s):  
Padrul Jana ◽  
Rokhimi Rokhimi ◽  
Ismi Ratri Prihatiningsih
Keyword(s):  
Exponential Smoothing ◽  
Moving Averages ◽  
Double Exponential ◽  
Double Exponential Smoothing

Kurs IDR terhadap USD yang fluktuatif sangat mempengaruhi ekonomi Indonesia saat ini, dibutuhkan suatu metode untuk meramalkan Kurs IDR terhadap USD agar bisa diprediksi. Diharapkan  para pemangku kepentingan segera mengambil kebijakan strategis demi stabilitas ekonomi nasional. Metode peramalan dalam tulisan ini menggunakan Double Moving Averages dan Double Exponential Smoothing dengan . Hasil peramalan menggunakan metode Double Moving Averages diperoleh IDR/USD, IDR/USD, IDR/USD dan Double Exponential Smoothing diperoleh IDR/USD, IDR/USD, IDR/USD. 14"> Kata Kunci: IDR, USD, Double Moving Averages, Double Exponential Smoothing.

scholarly journals Sistem Informasi Produksi Pakan Ternak Menggunakan Metode Triple Exponential Smoothing (TES) Pada PT. Charoen Pokhpand Medan

2021 ◽  
Vol 4 (2) ◽  
pp. 169
Author(s):  
Umi Pratiwi ◽  
Fhery Agustin
Keyword(s):  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing

<p><em>PT. Charoen Pokhpand Medan merupakan perusahaan yang bergerak di bidang produksi dan penjualan produk pakan ternak. Namun ada beberapa kendala yang dihadapi oleh perusahaan yaitu sistem yang berjalan masih menggunakan microsoft excell dalam proses pencatatan dan pembuatan laporan produksi pakan ternak. Dan PT. Charoen Pokhpand Medan harus mendata satu persatu hasil produksi pakan ternak yang terjadi. Bagian produksi mengalami kendala dalam pembuatan laporan peramalan produksi pakan ternak dan untuk prediksi produksi pakan ternak pada periode berikutnya. Dan proses perhitungan peramalan produksi pakan ternak masih menggunakan perhitungan sederhana sehingga dalam penyampaian laporan produksi pakan ternak kepada pimpinan membutuhkan waktu yang lama tidak efektif dan efisien. Dalam proses perhitungan peramalan produksi pakan ternak sering terjadi kesalahan dan tidak sinkron dengan data penjualan sesungguhnya, dibutuhkan metode dalam perhitungan produksi pakan ternak ke periode berikutnya. Dengan menerapkan perbandingan metode triple exponential smoothing dan double exponential smoothing dapat membantu perusahaan dalam mengatasi masalah yang dihadapi oleh perusahaan. Karena triple exponential smoothing </em><em>digunakan ketika terdapat unsur trend dan perilaku musiman yang ditunjukkan pada data. Metode Exponential Smoothing yang dapat digunakan untuk hampir segala jenis data stasioner atau non –stasioner sepanjang data tersebut tidak mengandung faktor musiman.</em></p>

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