Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation

Syahfitra, Febrian Dhimas; Syahputra, Ramadoni; Putra, Karisma Trinanda

doi:10.18196/jet.1316

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…To obtain results by expectations, it must go through a training and testing process where the parameters to be used have been determined [22]. These parameters include [15] :…”

Section: Results and Discussion Analysismentioning

confidence: 99%

Prediction of the Tuban Regency Open Unemployment Rate in 2022 with the Backpropagation Method

Putri

Hamid

Novitasari

2022

Numerical: J.Mat and Pend.Mat

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The Open Unemployment Rate is the number of unemployed from the total labor force in an area or region in the form of a percentage. In Tuban Regency, unemployment has experienced a very significant increase from 2019 to 2020. Therefore, it is necessary to make predictions in 2021 and 2022 to find out the description of the Open Unemployment Rate in that year so that actions can be taken by the government to deal with this. By using the Backpropagation method, this study obtained predictions that in 2021 it will be 4.58% of the total workforce in Tuban Regency in 2021 and in 2022 the open unemployment rate will be 4.7% of the total workforce in Tuban Regency in 2022, with network architecture 2-70-1-1, the learning rate used is 0.1 and the MAPE value is 8.363%. The test was stopped at the 276th epoch. At the time of testing between the output and the target there was a correlation (R) of 0.993 where the best result was 1.

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“…To obtain results by expectations, it must go through a training and testing process where the parameters to be used have been determined [22]. These parameters include [15] :…”

Section: Results and Discussion Analysismentioning

confidence: 99%

Prediction of the Tuban Regency Open Unemployment Rate in 2022 with the Backpropagation Method

Putri

Hamid

Novitasari

2022

Numerical: J.Mat and Pend.Mat

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“…ANN is an information processing system with characteristics similar to biological neu-ral networks. This study uses the backpropagation ANN algorithm with several units in one or more hidden screens [22] defined as Equation 4 [23].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

2022

J Biomed Phys Eng

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Background: Obstructive Sleep Apnea (OSA) is a respiratory disorder due to obstructive upper airway (mainly in the oropharynx) periodically during sleep. The common examination used to diagnose sleep disorders is Polysomnography (PSG). Diagnose with PSG feels uncomfortable for the patient because the patient's body is fitted with many sensors.Objective: This study aims to propose an OSA detection using the Fast Fourier Transform (FFT) statistics of electrocardiographic RR Interval (R interval from one peak to the peak of the pulse of the next pulse R) and machine learning algorithms. Material and Methods:In this case-control study, data were taken from the Massachusetts Institute of Technology at Beth Israel Hospital (MIT-BIH) based on the Apnea ECG database (RR Interval). The machine learning algorithms were Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). Results:The OSA detection technique was designed and tested, and five features of the FFT were examined, namely mean (f1), Shannon entropy (f2), standard deviation (f3), median (f4), and geometric mean (f5). The OSA detection found the highest performance using ANN. Among the ANN types tested, the ANN with gradient descent backpropagation resulted in the best performance with accuracy, sensitivity, and specificity of 84.64%, 94.21%, and 64.03%, respectively. The lowest performance was found when LDA was applied. Conclusion:ANN with gradient-descent backpropagation performed higher than LDA, SVM, and KNN for OSA detection.

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“…Generally, in backpropagation, the error between the output pattern and the associated target will be propagated to the layer below. The weight of each unit will be updated so that it can minimize the error and get a closer result [9].…”

Section: Classification Using Backpropagationmentioning

confidence: 99%

Classification of Rice Quality Using Backpropagation Based on Shape and Color

Farisi¹,

Pratamasunu²,

Sulaihah³

2022

FIJ

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The distribution of mixed rice on the market makes it difficult for consumers to determine the rice quality. In determining rice quality, the consumers consider and compare the texture, size and shape, color, aroma, purity, and homogeneity manually. This process is prone to errors and mistakes, due to the limited ability of each human's vision. Therefore, a method to determine the quality of rice automatically based on the physical characteristics of rice is needed. In this paper, we proposed an automatic rice quality classification method using backpropagation based on the shape and color of the rice. There are four parameters used to determine the classification process, namely compactness, circularity, mean, and skewness. Compactness and circularity were used to determine the ratio between the whole rice and broken rice. While mean and skewness were used to determine the color distribution of the rice. Experiments have been performed on 100 images consisting of 50 premium and 50 medium rice images. The experimental results show that the proposed method can classify rice based on its shape and color effectively with an accuracy rate of 95%.

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Implementation of Backpropagation Artificial Neural Network as a Forecasting System of Power Transformer Peak Load at Bumiayu Substation

Cited by 9 publications

References 7 publications

Prediction of the Tuban Regency Open Unemployment Rate in 2022 with the Backpropagation Method

Prediction of the Tuban Regency Open Unemployment Rate in 2022 with the Backpropagation Method

Obstructive Sleep Apnea Detection using Frequency Analysis of Electrocardiographic RR Interval and Machine Learning Algorithms

Classification of Rice Quality Using Backpropagation Based on Shape and Color

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