scholarly journals Forecasting Model Selection of Curly Red Chili Price at Retail Level

2019 ◽  
Vol 2 (1) ◽  
pp. 1-12
Author(s):  
Ketut Sukiyono ◽  
Miftahul Janah
Keyword(s):  
Moving Average ◽  
Price Policy ◽  
Exponential Smoothing ◽  
Future Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Monthly Data ◽  
Forecasting Models ◽  
Double Exponential ◽  
Double Exponential Smoothing

Chilli is one of strategic commodity in Indonesia due to its contribution to inflation level. For this reason, future price information is very importance for designing price policy. Future price merely can be provided by conducting a price forecasting. Various forecasting models can be applied for this purpose; the problem is which the best model for forecasting is. This study aims to select the most accurate forecasting model of curly red chili prices at the retail level. The data used are monthly data, from 2011 - 2017. Five forecasting models are applied and estimated including Moving Average, Single Exponential Smoothing, Double Exponential Smoothing, Decomposition, and ARIMA. The best model is selected based on the smallest MAPE, MSE and MAD values. The results show that the most accurate forecasting model is ARIMA (1,1,9).

scholarly journals PENDUGAAN MODEL PERAMALAN HARGA EKSPOR KOPI INDONESIA

2020 ◽  
Vol 7 (1) ◽  
pp. 22-30
Author(s):  
FAUZI EMLAN ◽  
Wawan Eka Putra ◽  
Andi Ishak ◽  
Herlena Bidi Astuti
Keyword(s):  
Trend Analysis ◽  
Moving Average ◽  
Export Price ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Monthly Data ◽  
Forecasting Models ◽  
Model Based ◽  
Single Exponential

ABSTRACT This study aims to examine the best forecasting model for the export price of Indonesian coffee. The data used in this study are monthly data on coffee prices from January 2012 to September 2019. Three price forecasting models used are moving average, single exponential smoothing and trend analysis are applied to determine the best model based on the lowest MAPE, MAD, and MSE values. The results showed the best model for forecasting the export price of coffee is the moving average (MA1) model because it has the smallest MAPE, MAD and MSE values ​​compared to other models. Keywords: Price, Coffee, Forecasting, Export

scholarly journals UPAYA PENINGKATAN PRODUKSI KOPI DENGAN PANEN PETIK MERAH DI KABUPATEN REJANG LEBONG

2020 ◽  
Vol 7 (1) ◽  
pp. 31-40
Author(s):  
Afrizon Afrizon ◽  
Andi Ishak ◽  
Darkam Mussaddad
Keyword(s):  
Trend Analysis ◽  
Moving Average ◽  
Export Price ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Monthly Data ◽  
Forecasting Models ◽  
Model Based ◽  
Single Exponential

This study aims to examine the best forecasting model for the export price of Indonesian coffee. The data used in this study are monthly data on coffee prices from January 2012 to September 2019. Three price forecasting models used are moving average, single exponential smoothing and trend analysis are applied to determine the best model based on the lowest MAPE, MAD, and MSE values. The results showed the best model for forecasting the export price of coffee is the moving average (MA1) model because it has the smallest MAPE, MAD and MSE values ​​compared to other models. Keywords: Price; Coffee; Forecasting; Export

scholarly journals USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

2021 ◽  
Vol 8 (2) ◽  
pp. 117-122
Author(s):  
Sambas Sundana ◽  
Destri Zahra Al Gufronny
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential ◽  
Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu  metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

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 PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

2021 ◽  
Vol 10 (3) ◽  
pp. 325-336
Author(s):  
Anes Desduana Selasakmida ◽  
Tarno Tarno ◽  
Triastuti Wuryandari
Keyword(s):  
Time Series ◽  
Precious Metal ◽  
Exponential Smoothing ◽  
Fuzzy Time Series ◽  
Monthly Data ◽  
Toxic Gases ◽  
Double Exponential ◽  
Speed Up ◽  
Past Data ◽  
Double Exponential Smoothing

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data. 

scholarly journals PREDIKSI INFLASI DI INDONESIA MENGGUNAKAN METODE MOVING AVERAGE, SINGLE EXPONENTIAL SMOOTHING DAN DOUBLE EXPONENTIAL SMOOTHING

2020 ◽  
Vol 1 (2) ◽  
pp. 123-129
Author(s):  
Nugroho Arif Sudibyo ◽  
Ardymulya Iswardani ◽  
Arif Wicaksono Septyanto ◽  
Tyan Ganang Wicaksono
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential

Tujuan dari penelitian ini adalah untuk mengetahui model peramalan yang paling baik digunakan untuk meramalkan inflasi di Indonesia dengan data inflasi Januari 2015 sampai dengan Mei 2020. Penelitian ini menggunakan beberapa metode peramalan. Berdasarkan metode peramalan yang dilakukan didapatkan hasil peramalan yang paling baik dilihat dari MAPE, MAD dan MSD adalah single exponential smoothing. Selanjutnya, hasil peramalan menunjukkan bahwa tingkat inflasi di Indonesia pada Agustus 2020 sebesar  1,41746%.

scholarly journals PERAMALAN PENERIMAAN BEA CUKAI INDONESIA

2018 ◽  
Vol 2 (1) ◽  
pp. 137
Author(s):  
Yolanda Sari ◽  
Nurlia Fusfita
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Rational Approach ◽  
Ordinary Least Square ◽  
Export Duty ◽  
Double Exponential ◽  
Double Exponential Smoothing

The revenue of customs and excise is very important in APBN. By making accurate estimation, target of revenue can be better determined. In addition, the revenue of customs and excise is also influenced by many external factors that are difficult to predict therefore a rational approach is needed to estimate revenue. This research uses Double Exponential Smoothing, Ordinary Least Square (OLS) model and Moving Average in predicting customs and excise revenue. Data used in this research is secondary data in time coherent pattern. The data includes import duty, export duty and excise obtained from the Directorate General of Customs and excise (DJBC) in the form of annual and quarterly data. This data starts from 2002 to 2016 with out of sample from 2017 to 2019. Some of these models are compared to each other to obtain the best model, and from the best model is also obtained estimating results in 3 years ahead. This study shows that the Double Exponential Smoothing model is better for predicting import duties compared to OLS and Moving Average models, which are models that have the smallest Sum Square Error (SSE) value. While the export and excise duty is best estimated by using OLS model which is shown with coefficient of determination value (R2)  regression model of export duty is 0.8, while the excise regression model has coefficient of determination of 0.9.Keywords:  Customs Estimation, Double Exponential Smoothing, Ordinary Least Square, Moving Average

scholarly journals Peramalan Gelombang Covid 19 Menggunakan Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing di Indonesia dan Prancis

2021 ◽  
Vol 3 (1) ◽  
pp. 37-51
Author(s):  
I Gusti Bagus Ngurah Diksa
Keyword(s):  
Time Series ◽  
Nonlinear Regression ◽  
Forecast Accuracy ◽  
Exponential Smoothing ◽  
Forecasting Model ◽  
Double Exponential ◽  
Single Method ◽  
Double Exponential Smoothing ◽  
The Government ◽  
Second Wave

ABSTRAKIndonesia dan Prancis adalah dua Negara yang mengalami Covid 19 dengan pola pergerakan kasus Covid 19 yang berbeda. Kondisi Indonesia masih mengalami siklus one wave namun Prancis sudah masuk pada pola second wave. Makna second wave adalah kondisi epidemi Covid 19 yang baru muncul setelah epidemi sebelumnya dianggap selesai. Dalam peramalan kasus Covid 19 baik itu terkait informasi puncak dari terjadinya kasus Covid 19 serta ramalan terkait akan berakhirnya pandemi kasus Covid 19 suatu negara merupakan hal penting bagi pemerintah suatu Negara. Model hybrid meningkatkan akurasi ramalan dibandingkan model time series yang dilakukan secara terpisah. Tujuan penelitian ini adalah melakukan peramalan kasus Covid 19 di Indonesia dan Prancis dengan menggunakan metode hybrid dan membandingkan dengan peramalan dengan salah satu metode tunggal. Metode yang digunakan adalah metode tunggal yaitu Nonlinear Regression Logistic dan metode Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing. Hasilnya adalah model peramalan Hybrid Nonlinear Regression Logistic and Doubel Exponential Smoothing lebih bagus digunakan dalam peramalan kasus Covid 19 di Indonesia dan Prancis. Terlihat bahwa nilai MAPE model Hybrid Nonlinear Regression Logistic–Double Eksponensial Smoothing jauh lebih kecil dibandingkan model peramalan Nonlinear Regression Logistic. ABSTRACTIndonesia and France are two countries that have experienced Covid 19 with different patterns of movement of Covid 19 cases. Indonesia's condition is still experiencing a one wave cycle but France has entered into the second wave pattern. The meaning of the second wave is the condition of the Covid 19 epidemic which only emerged after the previous epidemic was considered over. In forecasting the Covid 19 case, whether it is related to the peak information on the occurrence of the Covid 19 case and predictions regarding the end of the pandemic of the Covid 19 case in a country, it is important for the government of a country. The hybrid model improves forecast accuracy compared to the time series model which is carried out separately. The purpose of this study is to forecast the cases of Covid 19 in Indonesia and France using the hybrid method and comparing with forecasting with one single method. The method used is a single method, namely Nonlinear Logistic Regression and Hybrid Nonlinear Regression Logistic-Double Exponential Smoothing methods. The result is that the Hybrid Nonlinear Regression Logistic and Double Exponential Smoothing forecasting model is better used in forecasting the Covid 19 cases in Indonesia and France. It can be seen that the MAPE value of the Hybrid Nonlinear Regression Logistic – Double Exponential Smoothing model is much smaller than the Nonlinear Regression Logistic forecasting model.

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