PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE

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The effect of Kurtosis on the accuracy of artificial neural network predictive model

MATEC Web of Conferences ◽

10.1051/matecconf/201820402018 ◽

2018 ◽

Vol 204 ◽

pp. 02018

Author(s):

Aisyah Larasati ◽

Anik Dwiastutik ◽

Darin Ramadhanti ◽

Aal Mahardika

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Predictive Models ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Misclassification Rates ◽

Hidden Layer ◽

Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

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Pengujian Algoritma Artificial Neural Network (ANN) Untuk Prediksi Kecepatan Angin

Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) ◽

10.32672/jnkti.v2i1.1056 ◽

2019 ◽

Vol 2 (1) ◽

pp. 43

Author(s):

Syukri Syukri ◽

Samsuddin Samsuddin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

Angin memiliki peran yang penting dalam kehidupan manusia, antara lain pada pembangkit listrik, pelayaran dan penerbangan. Ketiga sektor tersebut erat kaitannya dengan kondisi angin. Angin dapat muncul setiap saat dan setiap waktu serta perubahan geografis pada suatu wilayah. Hal ini mengakibatkan sulitnya menentukan kecepatan angin, maka untuk mengatasi masalah tersebut diperlukan prediksi kecepatan angin. Saat ini berbagai metode prediksi telah banyak dikembangkan, salah satu metode yang dapat digunakan untuk melakukan prediksi dengan akurasi yang tinggi yaitu algoritma Artificial Neural Network (ANN) Backpropagation. Arsitektur ANN yang digunakan adalah 4 parameter input layer, hidden layer (5, 10, 15, 20, 25 dan 30) dan output layer (1 parameter). Data pembelajaran dan pengujian didapatkan dari stasiun BMKG Blang Bintang Aceh Besar, berupa data kecepatan angin jam per harian periode Januari 2011 sampai dengan Desember 2015 yang terdiri dari arah angin, suhu, tekanan, kelembaban dan suhu. Hasil pengujian menunjukkan bahwa metode ANN Backpropagation cukup baik diterapkan untuk proses prediksi, kemampuan ANN dalam melakukan prediksi memiliki tingkat akurasi rata – rata yang lebih baik yaitu 96 %. Sedangkan nilai rata – rata kerapatan daya angin jam per harian yaitu 45.030 W/m2

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Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

Algorithms, Methods, and Applications in Mobile Computing and Communications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5693-0.ch007 ◽

2019 ◽

pp. 158-177

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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Rapid Method for Prediction of Escherichia coli Numbers Using an Electronic Sensor Array and an Artificial Neural Network

Journal of Food Protection ◽

10.4315/0362-028x-67.8.1604 ◽

2004 ◽

Vol 67 (8) ◽

pp. 1604-1609 ◽

Cited By ~ 10

Author(s):

UBONRATANA SIRIPATRAWAN ◽

JOHN E. LINZ ◽

BRUCE R. HARTE

Keyword(s):

Neural Network ◽

Escherichia Coli ◽

Artificial Neural Network ◽

Transfer Function ◽

Sensor Array ◽

E Coli ◽

Output Layer ◽

Electronic Sensor ◽

Artificial Neural ◽

Hidden Layer

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37°C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 105 CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography–mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.

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Neural Network Modeling for Family Welfare Classification

Tech-E ◽

10.31253/te.v1i2.62 ◽

2018 ◽

Vol 1 (2) ◽

pp. 26

Author(s):

Sri Redjeki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Modeling ◽

Gradient Algorithm ◽

Neural Network Modeling ◽

Family Welfare ◽

Gradient Descent Algorithm ◽

Artificial Neural ◽

Input Variables ◽

Hidden Layer

Welfare in general can be defined as the level of a person's ability to meet their basic needs in the form of clothing, food, boards, education, and health. Welfare can be assessed in terms of family welfare. This study aims to perform analysis of artificial neural network modeling backpropagation method. The model will compare the optimization algorithm of artificial neural network results. The data used are 251 data of pre prosperous family in Banguntapan District, Bantul Regency. There are 16 input variables with 14 variables from BPS and 2 additional variables. There is one variable that has constant data so that this variable is not used in artificial neural network model analysis. There is a hidden layer with a number of dynamic neurons. Output layer there are 4 neurons which is the family welfare category. Data is processed using Matlab and SPSS. The system results show that the best accuracy for training is 68% of the Scale Conjugate Gradient algorithm while for best test results it is 68.8% of the Gradient Descent algorithm.

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Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

10.4018/978-1-6684-2408-7.ch022 ◽

2022 ◽

pp. 471-489

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

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Artificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging

ASEAN Journal of Chemical Engineering ◽

10.22146/ajche.69521 ◽

2021 ◽

Vol 21 (2) ◽

pp. 241

Author(s):

Joselito Abierta Olalo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optimization Techniques ◽

Fuel Oil ◽

Oil Yield ◽

Coconut Husk ◽

Input Layer ◽

Artificial Neural ◽

Predicted Values ◽

Hidden Layer

Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.

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Konstruksi Model Matematika Pola Curah Hujan Menggunakan Artificial Neural Network (ANN) dengan Metode Backpropagation

Justek : Jurnal Sains dan Teknologi ◽

10.31764/justek.v1i1.400 ◽

2018 ◽

Vol 1 (1) ◽

pp. 10

Author(s):

Habibi Ratu Perwira Negara ◽

Irzani Irzani ◽

Ripai Ripai

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Rainfall Pattern ◽

Output Layer ◽

South Central ◽

Southern Central ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

The Mathematical Model

Abstrak: Penelitian ini bertujuan untuk menentukan model pola curah hujan menggunakan Artificial Neural Network (ANN) dengan metode Backpropagatiaon. Sampel dalam penelitian ini adalah 5 pos pencataan hujan di daerah Lombok Tengah bagian Selatan dan 2 pos pencatatan hujan di daerah Lombok Timur bagian Selatan. Data penelitian berupa data setengah bulanan yang dicatat dari tahun 1973 sampai tahun 2010 untuk daerah Lombok Tengah bagian Selatan dan data dari tahun 1974 sampai tahun 2010 untuk daerah Lombok Timur bagian Selatan. Metode penilitian dilakukan dengan melakukan pembelajaran data curah hujan menggunakan 5 arsitektur yang berbeda. Pembelajaran dilakukan untuk menemukan arsitektur terbaik. Arsitektur untuk daerah Lombok Tengah bagian Selatan adalah 120 layer inputan, 240 layer hidden 1, 12 layer hidden 2, dan 1 layer output. Sedangkan arsitektur untuk daerah Lombok Timur bagian Selatan adalah 363 layer inputan, 54 layer hidden 1, 24 layer hidden 2, dan 1 layer output. Model matematika pola curah hujan yang diperoleh adalah .Abstract: This study aims to determine the model of rainfall pattern using Artificial Neural Network (ANN) with Backpropagatiaon method. The sample in this research is 5 rainfall checkpoint in South Central Lombok and 2 post recording of rain in South East of Lombok. The research data are semi-monthly data recorded from 1973 to 2010 for the southern part of Central Lombok and data from 1974 to 2010 for the southern part of Lombok Timur. Methods of research conducted by conducting rainfall data learning using 5 different architectures. Learning is done to find the best architecture. The architecture for the Southern Central Lombok area is 120 layers of input, 240 hidden layers 1, 12 hidden layers 2, and 1 output layer. While the architecture for the southern part of East Lombok is 363 layers inputan, 54 hidden layer 1, 24 hidden layer 2, and 1 output layer. The mathematical model of the obtained rainfall pattern is .

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Live to learn: learning rules-based artificial neural network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v21.i1.pp558-565 ◽

2021 ◽

Vol 21 (1) ◽

pp. 558

Author(s):

Aseel Shakir I. Hilaiwah ◽

Hanan Abed Alwally Abed Allah ◽

Basim Akhudir Abbas ◽

Tole Sutikno

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ann Model ◽

Output Layer ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Intelligent Model ◽

Human Nervous System

An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.

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Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

Journal of Applied Intelligent System ◽

10.33633/jais.v4i2.3089 ◽

2020 ◽

Vol 4 (2) ◽

pp. 75-85

Author(s):

Chrisani Waas ◽

D. L. Rahakbauw ◽

Yopi Andry Lesnussa

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Vector Quantization ◽

Processing System ◽

Disease Diagnosis ◽

Learning Vector Quantization ◽

Learning Rate ◽

Training Data ◽

Neural Network Learning ◽

Artificial Neural

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

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