scholarly journals IMPLEMENTASI ALGORITMA WEIGHTED MOVING AVERAGE PADA (FUZZY EAs) UNTUK PERAMALAN KALENDER MASA TANAM BERBASIS CURAH HUJAN

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Fhira Nhita
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Fuzzy System ◽  
Moving Average ◽  
Grammatical Evolution ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Weighted Moving Average

<p>Peramalan merupakan proses memperkirakan sesuatu secara sistematis berdasarkan keadaan atau fakta sebelumnya. Peramalan bisa dilakukan melalui serangkaian metode ilmiah atau dengan subjektif belaka. Soft computing (SC) merupakan salah satu metode ilmiah yang dapat digunakan untuk kasus peramalan atau prediksi, Soft Computing (SC) memiliki Algoritma dasar yakni Fuzzy System, Artificial Neural Network  (ANN), dan Evolutionary Alghorithms (EAs). Pada Tugas akhir ini dilakukan penelitian mengenai peramalan kalender masa tanam tanaman jagung yang berbasis curah hujan di wilayah Soreang Kabupaten Bandung menggunakan salah satu jenis algoritma dasar Soft computing (SC) yakni Evolutionary Alghorithms (EAs). Data yang digunakan adalah data curah hujan wilayah Soreang Kabupaten Bandung selama 10 tahun terakhir (2006-2015), data ini akan melalui preprocessing terlebih dahulu dengan Weighted Moving Average (WMA). Pada representasi individu, EAs memiliki empat algoritma yang bisa digunakan, salah satunya <em>Grammatical Evolution </em>(GE) yang akan digunakan pada penelitian ini. Selanjutnya, dalam tugas akhir ini digunakan logika <em>Fuzzy </em>untuk pengoptimasian GE, dengan cara mendefinisikan beberapa parameter pada awal running , agar proses dapat berjalan dengan baik. Hasil akhir yang didapat menunjukkan bahwa logika <em>Fuzzy </em>membantu meningkatkan performansi Eas dan Fuzzy EAs menghasilkan performansi peramalan kalender masa tanam sebesar 76,93%. Hasil peramalan akan digunakan untuk pembuatan kalender masa tanam di Kabupaten Bandung selama 13 (tiga belas) bulan yang dimulai pada Oktober 2014 sampai Oktober 2015.</p><p><em> </em></p>

scholarly journals STUDI KOMPARASI PREDIKSI CURAH HUJAN METODE FAST FOURIER TRANSFORMATION (FFT), AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN ARTIFICIAL NEURAL NETWORK (ANN)

2015 ◽  
Vol 35 (02) ◽  
pp. 241
Author(s):  
Dyah Susilokarti ◽  
Sigit Supadmo Arif ◽  
Sahid Susanto ◽  
Lilik Sutiarso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Fast Fourier Transformation ◽  
Autoregressive Integrated Moving Average ◽  
Precipitation Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Optimum climate condition and water availability are essential to support strategic venue and time for plants to grow and produce.  Precipitation prediction is needed to determine how much precipitation will provide water for plants on each stage of growth. Nowadays, the high variability of precipitation calls for a prediction model that will accurately foreseethe precipitation condition in the future. The prediction conducted is based on time-series data analysis. The research aims to comparethe effectiveness of three precipitation prediction methods, which are Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN).  Their respective performances are determined by their Mean Square Error (MSE) values.  Methods with highest correlation values and lowest MSE shows the best performance. The MSE result for FFT is 14,92; ARIMA is 17,49; and  ANN is 0,07. This research concluded that Artificial Neural Network (ANN) method showed best performance compare to the other two because it had produced a prediction with the lowest MSE value.Keywords: Precipitation prediction, Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average ABSTRAKKondisi iklim dan ketersediaan air yang optimal bagi pertumbuhan dan perkembangan tanaman sangat diperlukan dalam upaya mendukung strategi budidaya tanaman sesuai ruang dan waktu. Prediksi curah hujan sangat diperlukan untuk untuk mengetahui sejauh mana curah hujan dapat memenuhi kebutuhan air pada setiap tahap pertumbuhantanaman. Variabilitas curah hujan yang tinggi saat ini, membutuhkan pemodelan yang dapat memprediksi secara akurat bagaimana kondisi curah hujan dimasa yang akan datang. Prediksi yang dilakukan adalah prediksi berdasarkan urutan waktu ().  Tujuan dari penelitian ini adalah untuk membandingkan akurasi prediksi curah hujan antara metode  (FFT),  (ARIMA) dan (ANN). Kinerja ketiga metode yang digunakan dilihat dari nilai  (MSE). Metode dengan nilai korelasi tertinggi dan nilai MSE terkecil menunjukkan kinerja terbaik. Hasil penelitan untuk FFT diperoleh nilai MSE = 14,92, ARIMA = 17,49 sedangkan ANN = 0,07. Ini menunjukkan bahwa metode   (ANN) menunjukkan kinerja yang paling baik diantara dua metode lainnya karena menghasilkan prediksi yangmempunyai nilai MSE terkecil.Kata kunci: Prediksi curah hujan,FFT, ARIMA dan ANN 

scholarly journals Forecasting Rainfall in Mauritius using Seasonal Autoregressive Integrated Moving Average and Artificial Neural Networks

2018 ◽  
Vol 7 (1) ◽  
pp. 115
Author(s):  
Jayrani Cheeneebash ◽  
Ashvin Harradon ◽  
Ashvin Gopaul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moving Average ◽  
Geographical Location ◽  
Absolute Difference ◽  
Autoregressive Integrated Moving Average ◽  
Percentage Difference ◽  
Absolute Percentage Difference ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper, two forecasting methods namely, the autoregressive integrated moving average (ARIMA) and the artificial neural network (ANN) are studied to forecast the amount of rainfall in Mauritius. Indeed due to the geographical location of Mauritius, the rainfall pattern is deeply affected by the season prevailing whereby the period of summer receives a relatively high amount of rainfall when compared to winter. As such, forecasting rainfall can help the local authorities to manage the distribution of water in the country especially during droughts. The results obtained from both methods are compared in terms of their mean square error, mean absolute difference and mean absolute percentage difference. It is then seen that artificial neural network is a much better model as it is more accurate. This is due to its nonlinearity characteristic and ability to learn and train itself.

scholarly journals Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN)

The Winners ◽  
2008 ◽  
Vol 9 (2) ◽  
pp. 112
Author(s):  
Harjum Muharam ◽  
Muhammad Panji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Back Propagation ◽  
Series Data ◽  
Future Value ◽  
Auto Regressive ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H) exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average) which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN) Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.

Optimizing Material Removal Rate Using Artificial Neural Network for Micro-EDM

2018 ◽  
pp. 209-233 ◽  
Author(s):  
Ananya Upadhyay ◽  
Ved Prakash ◽  
Vinay Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Machining Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Unconventional Machining

Machining can be classified into conventional and unconventional processes. Unconventional Machining Process attracts researchers as it has many processes whose physics is still not that clear and they are highly in market-demand. To predict and understand the physics behind these processes soft computing is being used. Soft computing is an approach of computing which is based on the way a human brain learns and get trained to deal with different situations. Scope of this chapter is limited to one of the soft computing optimizing techniques that is artificial neural network (ANN) and to one of the unconventional machining processes, electrical discharge machining process. This chapter discusses about micromachining on Electric Discharge Machining, its working principle and problems associated with it. Solution to those problems is suggested with the addition of powder in dielectric fluid. The optimization of Material Removal Rate (MRR) is done with the help of ANN toolbox in MATLAB.

Optimizing Material Removal Rate Using Artificial Neural Network for Micro-EDM

2022 ◽  
pp. 843-866
Author(s):  
Ananya Upadhyay ◽  
Ved Prakash ◽  
Vinay Sharma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Machining Process ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Unconventional Machining

Machining can be classified into conventional and unconventional processes. Unconventional Machining Process attracts researchers as it has many processes whose physics is still not that clear and they are highly in market-demand. To predict and understand the physics behind these processes soft computing is being used. Soft computing is an approach of computing which is based on the way a human brain learns and get trained to deal with different situations. Scope of this chapter is limited to one of the soft computing optimizing techniques that is artificial neural network (ANN) and to one of the unconventional machining processes, electrical discharge machining process. This chapter discusses about micromachining on Electric Discharge Machining, its working principle and problems associated with it. Solution to those problems is suggested with the addition of powder in dielectric fluid. The optimization of Material Removal Rate (MRR) is done with the help of ANN toolbox in MATLAB.

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