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 Analisis Error Terhadap Peramalan Data Penjualan

Techno Com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 1-9
Author(s):  
Alyauma Hajjah ◽  
Yulvia Nora Marlim
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Squared Error

Tujuan penelitian ini membahas tentang peramalan permintaan lampu LED bermerk Sanyo. Penelitian ini menggunakan metode Moving Average dan Exponential Smoothing. Pada Metode Moving Average digunakan untuk peramalan periode 3 bulan dan 5 bulan, sedangkan metode Exponential Smoothing menggunakan parameter  = 0,1; 0,5; 0,7 dan 0,9. Dari hasil peramalan setiap metode dibandingkan nilai dari error, adapun nilai error yang dibahas adalah Mean Absolut Deviation (MAD), Mean Squared Error (MSE), dan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil penelitian ini metode yang memiliki tingkat kesalahan terkecil adalah metode Exponential Smoothing dengan parameter , yang memiliki nilai MAD= 1.214,54; MSE = 2.758.993 dan MAPE = 9,17%. Dapat disimpulkan bahwa metode yang paling optimal digunakan untuk meramalkan jumlah permintaan lampu pada bulan berikutnya adalah metode Exponential Smoothing dengan parameter . Hasil peramalan permintaan lampu pada bulan Januari 2020 dengan menggunakan metode Exponential Smoothing dengan  adalah 15.800,88. Sehingga untuk peramalan permintaan untuk bulan berikutnya peneliti merekomendasikan menggunakan metode Exponential Smoothing dengan perameter .

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

scholarly journals Features of building a predictive neuro-fuzzy network

2020 ◽  
pp. 13-17
Author(s):  
А.И. Епихин ◽  
Е.В. Хекерт ◽  
А.Б. Каракаев ◽  
М.А. Модина
Keyword(s):  
Mean Squared Error ◽  
Least Squares Method ◽  
Finite Impulse Response ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Piston Engine ◽  
Absolute Percentage Error ◽  
Squared Error ◽  
Neuro Fuzzy ◽  
Fuzzy Network

В статье рассматриваются особенности построения прогностической нейро-фаззи сети. В процессе исследования представлена структура адаптивного нейро-фаззи-предиктора и многомерного нейро-фаззи-нейрона. Рассмотрен принцип обработки информации, поступающей в режиме реального времени, о работе поршневого двигателя СЭУ с использованием TSK-системы нулевого порядка с применением быстродействующих оптимизационных процедур второго порядка типа рекуррентного метода наименьших квадратов для настройки синаптических весов. Определена архитектура искусственной нейро-фаззи сети для прогноза ресурсной прочности поршневого двигателя СЭУ марки RND 105, состоящая из пяти последовательно соединенных слоев. Представлена структура динамических нейронов-фильтров с конечной импульсной характеристикой. Рассмотрена процедура обучения нейросети. При проведения численного эксперимента использовались следующие критерии оценки: MSE (mean squared error, среднеквадратичная погрешность); SMAPE (Symmetric mean absolute percentage error, симметрично абсолютная процентная погрешность) - характеризует погрешность прогноза в процентах. Экспериментальный анализ разработанной сети проводился на примере прогнозирования ресурсной прочности восьмицилиндрового двухтактного судового дизеля марки RND 105. The article discusses the features of building a predictive neuro-fuzzy network. During the research, the structure of an adaptive neuro-fuzzy predictor and a multidimensional neuro-fuzzy neuron is presented. The principle of processing information received in real time about the operation of a piston engine of a SEP using a TSK-system of zero order with the use of high-speed optimization procedures of the second order such as the recurrent least squares method for adjusting synaptic weights is considered. The architecture of an artificial neuro-fuzzy network for predicting the resource strength of a piston engine SEU brand RND 105, consisting of five layers connected in series, has been determined. The structure of dynamic filter neurons with finite impulse response is presented. The procedure for training a neural network is considered. During the numerical experiment, the following evaluation criteria were used: MSE (mean squared error); SMAPE (Symmetric mean absolute percentage error) - characterizes the forecast error in percentage. An experimental analysis of the developed network was carried out on the example of predicting the resource strength of an eight-cylinder two-stroke marine diesel engine of the RND 105 brand.

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).

scholarly journals PERBANDINGAN METODE REGRESI LINEAR DAN NEURAL NETWORK BACKPROPAGATION DALAM PREDIKSI NILAI UJIAN NASIONAL SISWA SMP MENGGUNAKAN SOFTWARE R

JOUTICA ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 331
Author(s):  
Masruroh Masruroh
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Output Data ◽  
Root Mean Squared Error ◽  
Absolute Percentage Error ◽  
Squared Error

Metode regresi linear dan neural network backpropagation merupakan metode yang kerap digunakan dalam model prediksi. Penelitian ini bertujuan untuk membandingkan akurasi metode regresi linear dan backpropagation dalam prediksi nilai Ujian Nasional siswa SMP. Data yang digunakan berupa data nilai ujian akhir semester dan ujian sekolah sebagai input dan nilai ujian nasional sebagai output. Data didapatkan dari SMPN 1 dan SMPN 2 Lamongan.. Jumlah dataset sebanyak 701 dibagi menjadi 75% data training dan 25% data testing. Simulasi prediksi dilakukan menggunakan software R. Parameter akurasi yang digunakan adalah Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan model prediksi menggunakan metode regresi linear menghasilkan RMSE sebesar 9,04 dan MAPE sebesar 3,94%, sedangkan model prediksi menggunakan backpropagation menghasilkan RMSE sebesar 7,28 dan MAPE sebesar 0,55%. Dengan demikian dalam penelitian ini metode neural network backpropagation memiliki akurasi yang lebih baik dalam prediksi nilai Ujian Nasional siswa SMP.

scholarly journals Rainfall Analysis and Forecasting Using Deep Learning Technique

2021 ◽  
Vol 2 (2) ◽  
pp. 1-11
Author(s):  
Pragati Kanchan ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Root Mean Squared Error ◽  
Rainfall Prediction ◽  
Absolute Percentage Error ◽  
Squared Error

Rainfall forecasting is very challenging due to its uncertain nature and dynamic climate change. It's always been a challenging task for meteorologists. In various papers for rainfall prediction, different Data Mining and Machine Learning (ML) techniques have been used. These techniques show better predictive accuracy. A deep learning approach has been used in this study to analyze the rainfall data of the Karnataka Subdivision. Three deep learning methods have been used for prediction such as Artificial Neural Network (ANN) - Feed Forward Neural Network, Simple Recurrent Neural Network (RNN), and the Long Short-Term Memory (LSTM) optimized RNN Technique. In this paper, a comparative study of these three techniques for monthly rainfall prediction has been given and the prediction performance of these three techniques has been evaluated using the Mean Absolute Percentage Error (MAPE%) and a Root Mean Squared Error (RMSE%). The results show that the LSTM Model shows better performance as compared to ANN and RNN for Prediction. The LSTM model shows better performance with mini-mum Mean Absolute Percentage Error (MAPE%) and Root Mean Squared Error (RMSE%).

scholarly journals Forecast Error Calculation with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE)

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Ansari Saleh Ahmar
Keyword(s):  
Decision Making ◽  
Mean Squared Error ◽  
Forecast Error ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Forecasting Accuracy ◽  
Error Calculation ◽  
Absolute Percentage Error ◽  
Squared Error ◽  
High Level

Calculation errors in forecasting a data are very important from a forecasting process. The high level of forecasting accuracy will affect the level of confidence in forecasting decision making.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

2018 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
M. Tirtana Siregar ◽  
S. Pandiangan ◽  
Dian Anwar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Mean Absolute Percentage Error ◽  
Programming Method ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

APLIKASI PREDIKSI JANGKA PENDEK HARGA BITCOIN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 25 (3) ◽  
pp. 160-174
Author(s):  
Nur Fitrian Bintang Pradana ◽  
Sri Lestanti
Keyword(s):  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
Absolute Percentage Error

Bitcoin merupakan mata uang digital yang sekarang paling banyak digunakan. Perubahan harga yang sewaktu-waktu dapat berubah membuat pengguna bitcoin harus teliti ketika melakukan penukaran. Kepopuleran bitcoin terus meningkat dan menjadi aset untuk investasi bagi para penggunanya. Untuk mengatasi perubahan harga yang tidak menentu maka dibutuhkan sebuah aplikasi prediksi harga bitcoin untuk membantu para penggunanya dalam memprediksi harga bitcoin kedepannya. Prediksi dilakukan dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA) yang mampu menghasilkan tingkat akurasi tinggi dalam prediksi jangka pendek. Metode ini mengabaikan variabel independen dalam membuat prediksi, sehingga cocok untuk data statistik saling terhubung serta memiliki beberapa asumsi yang harus dipenuhi seperti autokorelasi, trend, maupun musiman. Evaluasi hasil prediksi menggunakan Mean Absolute Percentage Error (MAPE). Hasil pengujian menujukkan bahwa model ARIMA (3,1,3) menghasilkan prediksi dengan nilai MAPE terkecil daripada kandidat model lainnya. Rata-rata nilai MAPE yang dihasilkan adalah sebesar 0,84 dan rentang nilai 1,34 untuk prediksi hari pertama dan 0,98 untuk prediksi hari ketujuh. Dengan demikian model ARIMA (3,1,3) mampu menghasilkan prediksi dengan akurasi yang baik dan layak untuk digunakan sebagai metode prediksi bitcoin untuk satu sampai tujuh hari kedepan.

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