scholarly journals ANALISA DAN PERBANDINGAN AKURASI MODEL PREDIKSI RENTET WAKTU ARUS LALU LINTAS JANGKA PENDEK

2015 ◽  
Vol 6 (3) ◽  
pp. 148
Author(s):  
Bambang Lareno
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy

<p>Abstrak <br />Terdapat banyak algoritma yang dapat dipakai untuk memprediksi arus lalu lintas, namun belum diketahui algoritma manakah yang memiliki kinerja lebih akurat untuk lalu lintas di Indonesia. Algoritma-algoritma tersebut perlu diuji untuk mengetahui algoritma manakah yang memiliki kinerja lebih akurat. Metode yang diusulkan adalah metode perbandingan tingkat akurasi dari algoritma berbasis neural network yang bisa digunakan untuk prediksi data rentet waktu. Algoritma yang akan diuji adalah back Propagation Neural Network (BP-NN), Adaptive Neuro Fuzzy Inference System (ANFIS), Wavelet Neural Network (WNN), dan Evolving Neural Network (ENN), yang digunakan untuk memprediksi arus lalulintas. Masing-masing algoritma akan implementasikan dengan menggunakan MatLab 2009b. Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) dan Mean Absolute Deviation (MAD). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dalam penelitian ini diketahui bahwa Algoritma ENN memprediksi arus lalu lintas dengan lebih akurat.</p>

scholarly journals Kajian Adaptive Neuro-Fuzzy Inference System (ANFIS) Dalam Memprediksi Penerimaan Mahasiswa Baru Pada Universitas Buana Perjuangan Karawang

2021 ◽  
Vol 6 (1) ◽  
pp. 44-54
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.   Keywords: ANFIS, Backpropagation, Hybrid, Prediction

Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang)

2021 ◽  
Vol 2 (2) ◽  
pp. 31-36
Author(s):  
Tatang Rohana ◽  
Bayu Priyatna
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang

COMPARATIVE PERFORMANCE FOR PREDICTIVE MODELLING IN MOTOR INSURANCE CLAIMS

2016 ◽  
Vol 78 (12-2) ◽  
Author(s):  
Zuriahati Mohd Yunos ◽  
Siti Mariyam Shamsuddin ◽  
Razana Alwee ◽  
Noriszura Ismail ◽  
Roselina Salleh@Sallehuddin
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Predictive Modelling ◽  
Back Propagation Neural Network ◽  
Insurance Claims ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Motor Insurance ◽  
Claim Frequency

The expected claim frequency and the expected claim severity are used in predictive modelling motor insurance claims. There are two categories of claims were considered, namely, third party property damage and own damage. Datasets from the year 2001 to 2003 are used to develop the predictive model. This paper proposes three different methods, namely, regression analysis, back propagation neural network and adaptive neuro fuzzy inference system to model claim frequency and claim severity as the two important elements in modelling the motor insurance claims. The experimental results showed that the back propagation neural network model produces more accurate as compared to regression analysis and adaptive neuro fuzzy inference system in predicting the claim frequency and claim severity. For both OD and TPPD claim, the results have shown the lowest MAPE with 0.2191 and 0.6515, and 0.2169 and 0.326, respectively.

scholarly journals ANFIS AND NEURAL NETWORK BASED FACIAL EXPRESSION RECOGNITION USING CURVELET FEATURES

2014 ◽  
pp. 255-261
Author(s):  
S.P. Khandait ◽  
R.C. Thool ◽  
P.D Khandait
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Curvelet Transform ◽  
Back Propagation Neural Network ◽  
Expression Recognition ◽  
Inference System ◽  
Promising Tool

Curvelet transform is a promising tool for multi-resolution analysis on images. This paper explains a new approach for facial expression recognition based on curvelet features extracted using curvelet transform. Curvelet transform is applied on the database images and curvelet coefficients are obtained for selected scale for image analysis. Facial curvelet features are compressed using singular value decomposition (SVD) approach. Back propagation neural network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used as classifiers for classifying expressions into one of the seven categories like angry, disgust, fear, happy, neutral, sad and surprise. Experimentation is carried out on JAFFE database. The experimental results show that the novel approach is a better option for extracting feature values and classifying facial expressions.

scholarly journals Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 211
Author(s):  
Aamer Bilal Asghar ◽  
Saad Farooq ◽  
Muhammad Shahzad Khurram ◽  
Mujtaba Hussain Jaffery ◽  
Krzysztof Ejsmont
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Fuzzy Inference System ◽  
Circulating Fluidized Bed ◽  
Fuzzy Inference ◽  
Mean Square ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural

Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System use four input features—pressure, single mean diameter, total valve opening and riser dp—and one output feature mass flow rate with multiple neurons in the hidden layers to estimate the flow of solid particles in the riser. Both Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System model worked on 217 data samples and output results are compared based on their Mean Square Error, Regression analysis, Mean Absolute Error and Mean Absolute Percentage Error. The experimental results show the effectiveness of Adaptive Neuro-Fuzzy Inference System (Mean Square Error is 0.0519 and Regression analysis R2=1.0000), as it outperformed Artificial Neural Network in terms of accuracy (Mean Square Error is 1.0677 and Regression analysis R2=0.9806).

An ANFIS Based Model for Predicting Frost Heaving in Seasonal Frozen Regions

2014 ◽  
Vol 501-504 ◽  
pp. 391-394
Author(s):  
Yi Ming Xiang ◽  
Xue Yan Liu ◽  
Gui Xiang Ling ◽  
Bin Du
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Subtractive Clustering ◽  
Forecast Errors ◽  
Frost Heaving ◽  
Inference System ◽  
Neuro Fuzzy

An adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict frost heaving in seasonal frozen regions. The structure of ANFIS is initialized by the subtractive clustering algorithm. The hybrid learning algorithm consisting of back-propagation and least-squares estimation is used to adjust parameters of ANFIS and automatically produce fuzzy rules. The data of frost heaving test obtained from a literature are used to train and check the system. The predicted results show that the proposed model outperforms the back propagation neural network (BPNN) in terms of computational speed, forecast errors, and efficiency. The ANFIS based model proves to be an effective approach to achieve both high accuracy and less computational complexity for predicting frost heaving.

Comparative Analysis of the Heating Values of Biomass Based on GA-ANFIS and PSO-ANFIS Models

2019 ◽  
Author(s):  
Obafemi Olatunji ◽  
Stephen Akinlabi ◽  
Nkosinathi Madushele ◽  
Paul Adedeji ◽  
Samuel Fatoba
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Input Parameter ◽  
Volatile Matter ◽  
Proximate Analysis ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Wide Range

Abstract This article applied a hybridized, adaptive neuro-fuzzy inference system ANFIS-genetic algorithm (GA-ANFIS) and ANFIS -Particle swarm optimization (PSO-ANFIS) to predict the HHV of biomass. The minimum input parameter for the prediction model is based on the proximate values of biomass which are fixed carbon (FC), ash content (A) and volatile matter (VM). The 214 data which cover a wide range of biomass classes were extracted from reliable literature for the training and testing of the models. The optimal results obtained based on each modelling algorithm were compared. The proposed algorithms were evaluated by statistical indices which are the Coefficient of Correlation (CC), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) estimated at 0.9189, 1.2369,7.4575 and 1.3560 respectively for PSO-ANFIS and 0.9088, 1.1200, 6.3960, 0.8895 respectively for GA-ANFIS. The GA showed exceptional ability to generalize in term of MAPE though at the expense of lesser CC which is obtained in the case of PSO. The reported indices showed that PSO-ANFIS and GA-ANFIS could be applied as an approach to the prediction of HHV based on proximate analysis instead of lengthy experiment procedures.

scholarly journals Plant Seeds Growth Prediction on Greenhouse Using Adaptive Neuro Fuzzy Inference System (ANFIS) Method

2020 ◽  
Vol 202 ◽  
pp. 14008
Author(s):  
Siska Ayu Widiana ◽  
Suryono Suryono ◽  
Budi Warsito
Keyword(s):  
Food Security ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Inference System ◽  
Plant Seeds ◽  
Neuro Fuzzy ◽  
Anfis Method

Food security is a problem that every country had, especially for poor and developing countries. To improve the food security one of the solutions that can be applied is to collaborate technology and agriculture such as greenhouse. The technology that is applied to greenhouse can produce plants with good quality. Good quality plant can be predicted with prediction on the plant seeds in order to develop the plants production just as we expected. Prediction on plant seeds is using the adaptive neuro fuzzy inference system (ANFIS) model which is a combination of fuzzy and neural network. ANFIS will process the data with high complexity and it will provide the prediction result with high accuracy. Plant seeds prediction is using 65 data which divided into two data, specifically 50 training data and 15 testing data. The prediction provides accurate result and will generate 14/15 x 100% = 93.3333% precision with Mean Absolute Deviation (MAD) is 64.3391 from 15 prediction data about 4.2893, Mean Absolute Percentage Error (MAPE) is 5.3485 from 15 prediction data about 0.35657, Mean Square Deviation (MSD) is 9.159 from 15 prediction data about 0.6106.

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