scholarly journals Penerapan Metode Triple Exponential Smoothing Pada Sistem Prediksi Keuntungan Bisnis Ayam Broiler Guna Meningkatkan Pengelolaan Keuangan Peternak

Kilat ◽  
2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Dine Tiara Kusuma ◽  
Muhammad Farid Rifai ◽  
M Yoga Sudirman
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
The Difference ◽  
Exponential Smoothing Method

Profitable business predictions are used to help chicken breeder in anticipating profit earned in the next harvest. The existence of a profitable prediction, enables breeder to predict when the next harvest is experiencing little profit or harvest failures. In addition, to be the breeder still has risky capital and bankruptcy. In this research, the author compare two methods accordingly in this case, there are triple exponential smoothing method and monte carlo method. The data used in the calculation of news data is profitable on the previous harvest. To find the value of two methods are match, the author's use mean absolute percentage error (MAPE) to learn the percentage of the value of the error. Based on the value of MAPE, triple exponential smoothing method have value of 12.10% with α value = 0.3 and monte carlo method have value of 40.58%. Meanwhile with the anticipated value of profit testing for the next 2 harvest grab the difference up to Rp. 19,935,410.

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 Peramalan Jumlah Populasi Sapi Potong di Kalimantan Selatan Menggunakan Metode Moving Average, Exponential Smoothing dan Trend Analysis

2019 ◽  
Vol 6 (1) ◽  
pp. 41
Author(s):  
Jaka Darma Jaya
Keyword(s):  
Trend Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Polynomial Trend ◽  
Squared Error

Perkembangan produksi daging sapi di Indonesia selama 30 tahun terakhir secara umum cenderung meningkat. Kebutuhan daging sapi di Indonesia masih belum bisa dicukupi oleh supply domestik, sehingga diperlukan impor daging sapi dari luar negeri.  Diperlukan kajian tentang proyeksi ketersediaan populasi sapi potong di masa mendatang agar diambil kebijakan yang tepat dalam menjaga stabilitas dan keterpenuhan supply daging nasional.  Penelitian ini bertujuan untuk melakukan peramalan jumlah populasi sapi potong menggunakan 3 (tiga) metode peramalan yaitu metode moving average, exponential smoothing dan trend analysis.  Hasil peramalan ini selanjutnya diukur akurasinya menggunakan MAD (Mean Absolud Deviation), MSE (Mean Squared Error) dan MAPE (Mean Absolute Percentage Error).  Proyeksi populasi sapi potong pada tahun 2019 (periode berikutnya) menggunakan 3 metode peramalan adalah: 195.100 (moving average); 218.225 (exponential smooting) dan 262.899 (trend analysis). Pengukuran akurasi menggunakan MAD, MSE dan MAPE menunjukkan bahwa metode peramalan jumlah populasi sapi potong yang paling akurat adalah peramalan menggunakan metode polynomial trend analysis (MAD 14.716,12;  MSE 327.282.084,17; dan MAPE 0,09) karena memiliki tingkat kesalahan yang lebih kecil dibandingkan hasil peramalan menggunakan metode moving average dan exponential smoothing.

scholarly journals Forecasting of Groundwater Tax Revenue Using Single Exponential Smoothing Method

2019 ◽  
Vol 125 ◽  
pp. 23006
Author(s):  
Dyna Marisa Khairina ◽  
Aqib Muaddam ◽  
Septya Maharani ◽  
Heliza Rahmania
Keyword(s):  
Reference Value ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Tax Revenue ◽  
Regional Office ◽  
Forecasting Error ◽  
The Government ◽  
Exponential Smoothing Method ◽  
Single Exponential

Setting the target of groundwater tax revenues for the next year is an important thing for Kutai Kartanegara Regional Office of Revenue to maximize the regional income and accelerate regional development. Process of setting the target of groundwater tax revenue for the next year still using estimation only and not using a mathematical calculation method that can generate target reference value. If the realization of groundwater tax revenue is not approaching the target, the implementation of development in the Government of Kutai Kartanegara can be disrupted. The mathematical method commonly used to predict revenue value is the Single Exponential Smoothing (SES) method, which uses alpha constant value which is randomly selected for the calculation process. Forecasting of groundwater tax revenue for 2018 using groundwater tax revenue data from 2013 to 2017. Single Exponential Smoothing method using alpha constant value consists of 0.1, 0.2, 0.3, 0.4 and 0.5. The forecasting error value of each alpha value is calculated using the Mean Absolute Percentage Error (MAPE) method. The best result is forecasting using alpha value 0.1 with MAPE error value was 45.868 and the best forecasting value of groundwater tax for 2018 is Rp 443.904.600,7192.

scholarly journals Aplikasi Triple Exponential Smoothing Untuk Forecasting Jumlah Penduduk Miskin

2020 ◽  
Vol 3 (2) ◽  
pp. 76-82
Author(s):  
Padrul Jana
Keyword(s):  
Poverty Reduction ◽  
Mean Squared Error ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Poor People ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Absolute Percentage Error ◽  
The Government

This study aims to predict the number of poor in Indonesia for the next few years using a triple exponential smoothing method.The purpose of this research is the result of the forecast number of poor people in Indonesia accurate forecast results are used as an alternative data the government for consideration of government to determine the direction of national poverty reduction policies. This research includes the study of literature research, by applying the theory of forecasting to generate predictions of poor people for coming year. Furthermore, analyzing the mistakes of the methods used in terms of the count: Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean absolute percentage error (MAPE) and Mean Percentage Error (MPE). The function of this error analysis is to measure the accuracy of forecasting results that have been conducted.These results indicate that the number of poor people in 2017 amounted to 24,741,871 inhabitants, in 2018 amounted to 24,702,928 inhabitants, in 2019 amounted to 24,638,022 inhabitants and in 2020 amounted to 24,547,155 people. The forecasting results show an average reduction in the number of poor people in Indonesia last five years (2016-2020 years) ranges from 0.16 million. Analysis forecasting model obtained an mean absolute deviation (MAD) obtained by 0.246047. Mean squared error (MSE) of forecasting results with the original data by 1.693277. Mean absolute percentage error (MAPE) of 3.040307% and the final Mean percentage error (MPE) of 0.888134%.Kata Kunci: Forecasting, Triple Exponential Smoothing

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 Modelling the Employment in Tourism – Case Study of Croatia

2017 ◽  
Vol 8 (3) ◽  
pp. 37 ◽  
Author(s):  
Maja Mamula ◽  
Kristina Duvnjak
Keyword(s):  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Reliable Estimate ◽  
Monthly Data ◽  
Absolute Percentage Error ◽  
Forecasting Error ◽  
Forecasting Methods ◽  
Percentage Share

According to the data on the share of employees in the category Hotels and similar accommodation in the total employees (16.6% in 2015), it can be concluded that this percentage share is quite significant. In this paper the number of employees in tourism (in the category Hotels and similar accommodation) is modelled and predicted on the basis of monthly data from the period 2005 to 2015, collected from the First Release of the Croatian Bureau of Statistics. Taking into consideration the seasonal character of the phenomenon being analysed, taking into account the criteria of reliability of demonstrated forecasts, in this study following methods were used: the seasonal naive models, Holt - Winters Model trend seasonality exponential smoothing and Holt- Winters no seasonal exponential smoothing model. All obtained results were compared by forecasting error Mean Absolute Percentage error (MAPE). The obtained results indicate that forecasting methods which take into account the seasonal character of the phenomenon result in smaller forecasting error, and more reliable estimate, compared to models which don´t take into account the character of the phenomenon being analysed.

scholarly journals PENENTUAN HARGA KONTRAK OPSI TIPE EROPA MENGGUNAKAN METODE QUASI MONTE CARLO DENGAN BARISAN KUASI-ACAK HALTON

2014 ◽  
Vol 3 (4) ◽  
pp. 154
Author(s):  
I GUSTI PUTU NGURAH MAHAYOGA ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Monte Carlo ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Quasi Monte Carlo ◽  
Absolute Percentage Error ◽  
Black Scholes

Penelitian ini bertujuan untuk mengetahui keakuratan hasil simulasi harga saham untuk menentukan harga opsi call dari metode Monte Carlo dan metode Quasi Monte Carlo dengan menggunakan program Matlab. Harga standar yang digunakan untuk membandingkan kedua metode tersebut akan dihitung dengan metode Black-Scholes. Nilai error yang dihitung menggunakan metode MAPE (Mean Absolute Percentage Error) digunakan sebagai acuan dalam perbandingan. Selain keakuratan simulasi harga saham, kecepatan eksekusi program Matlab kedua metode juga dihitung untuk efisiensi waktu. Tahap pertama, menentukan variabel-variabel yang digunakan untuk menghitung lintasan harga saham pada waktu ke-t pada saat mensimulasikan harga saham. Tahap kedua, menghitung harga standar menggunakan metode Black-Scholes. Tahap ketiga, mensimulasikan harga saham dengan metode Monte Carlo dan Quasi Monte Carlo. Setelah mensimulasikan harga saham, catat waktu eksekusi program Matlab, lalu dihitung nilai pay-off dari opsi call, kemudian menaksir harga opsi call dengan merata-ratakan seluruh nilai pay-off dari masing-masing iterasi. Tahap terakhir, menghitung error dari kedua metode simulasi dengan metode MAPE lalu membandingkannya. Hasil penelitian ini menunjukkan bahwa metode Quasi Monte Carlo lebih akurat karena menghasilkan nilai error yang lebih kecil, artinya hasil simulasinya mendekati harga standar. Sedangkan untuk waktu eksekusi program, metode Monte Carlo lebih baik di semua iterasi.

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 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 COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

2018 ◽  
Vol 47 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Syed Misbah Uddin ◽  
Aminur Rahman ◽  
Emtiaz Uddin Ansari
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

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