Implementasi Kendali PID Menggunakan Jaringan Syaraf Tiruan Backpropagation

Dewantoro, Gunawan; Sukamto, Johanes Nico

doi:10.26418/elkha.v11i1.29959

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…In the hidden layer and output layer, a calculation process occurs depending on the weight and bias of each neuron. The result calculates the output value of the hidden and output layers based on the activation function used [10]. In the hidden layer and output layer, a calculation process occurs depending on the weight and bias of each neuron.…”

Section: Backpropagation Neural Network Architecturementioning

confidence: 99%

Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

Dwipayana,

Sugiartawan

2023

Indonesian J. Comput. Cybern. Syst.

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The distribution of pests in rice plants results in significant losses in production and damage to rice plants for farmers, seen from data on the area of rice borer attacks in the province of Bali in Tabanan district. Therefore, by predicting the distribution of rice pests, we can know the pattern of pest attacks so that we can anticipate them because predicting can provide accuracy and error values through the test results. One of the prediction models is BPNN, where BPNN's advantages for solving complex problems are very suitable for use where large amounts of data are involved and many input/output variables, BPNN is also capable of modeling nonlinear relationships between input and output variables, which may be difficult to capture by this type of predictive model. other. Backpropagation includes supervised learning, which means it can learn from labeled examples and can make accurate predictions on new, unlabeled data. Split data using K-fold cross-validation serves to assess the process performance of an algorithmic method by dividing random data samples and grouping the data as many as K k-fold values.

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Section: Backpropagation Neural Network Architecturementioning

confidence: 99%

Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

Dwipayana,

Sugiartawan

2023

Indonesian J. Comput. Cybern. Syst.

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“…The creation of this demonstration tool has a positive impact on practical learning and also hydrokinetic energy for students in control engineering laboratories, as it facilitates the understanding of concepts related to water level control using a PID system. g) It is clear from the study [11] that the goal of this research is to substitute an artificial neural network trained using the Backpropagation method for conventional controllers (PID). The usage of artificial neural networks in the context of learning can benefit the learning of electronic control systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Electronic Control System Learning through Op-Amp Trainer Implementation

Pramudita,

Ibrohim,

Somantri

et al. 2023

CRC

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This research aims to determine the effectiveness of the implementation of Op-Amp trainer in the learning of electronic control systems on the learning outcomes of students in the cognitive and psychomotor domains. The research was conducted using a quasi-experiment method and a sample of 32 students from the experimental and control classes. Data analysis was conducted using hypothesis testing and N-Gain. The results showed that the average learning outcomes of students in both the cognitive and psychomotor domains are good and there was a significant difference in learning outcomes with the experimental class higher. The conclusion of this research is that the implementation of Op-Amp trainer in the learning of electronic control systems is effective in improving student learning outcomes in the cognitive and psychomotor domains when compared to simulation-based learning media.

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“…JST banyak digunakan pada berbagai riset di berbagai bidang. Salah satunya dalam bidang pengendalian sistem [1]- [3]. JST dapat juga digunakan sebagai sebuah pendekatan dalam pengendalian sistem yang parameter-parameternya tidak diketahui atau sulit untuk ditentukan [4].…”

Section: Pendahuluanunclassified

Performance Analysis of the RBF-SOM Network for Iris Data Classification as an Effort to Overcome System Control Problems

Elviralita¹,

Hidayat

2022

motivection

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One way to solve system control problems is by using pattern recognition. Many studies are related to pattern recognition, including artificial neural networks. This study develops an algorithm that combines artificial neural networks with Radial Basis Function (RBF) and Self-Organizing Maps (SOM). The proposed RBF-SOM algorithm was successfully realized with the MATLAB routine program and tested with the case of iris data recognition. The results of the recognition rate show that the developed artificial neural network has a good performance with an average of 98%. Salah satu upaya dalam menyelesaikan permasalahan pengendalian system adalah dengan melakukan pengenalan pola. Banyak penelitian yang terkait dengan pengenalan pola diantaranya dengan jaringan syaraf tiruan. Penelitian ini mengembangkan sebuah algoritma perpaduan antara jaringan saraf tiruan Radial Basis Function (RBF) dan Self-Organizing Maps (SOM). Algoritma RBF-SOM ini berhasil direalisasikan dengan program MATLAB dan diuji dengan kasus pengenalan data bunga iris. Hasil recognition rate menunjukkan bahwa jaringan saraf tiruan yang dikembangkan tersebut memiliki performa yang baik dengan rata-rata sebesar 98 %.

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Implementasi Kendali PID Menggunakan Jaringan Syaraf Tiruan Backpropagation

Cited by 5 publications

References 9 publications

Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

Predictive Analysis of Rice Pest Distribution in Bali Province Using Backpropagation Neural Network

Optimization of Electronic Control System Learning through Op-Amp Trainer Implementation

Performance Analysis of the RBF-SOM Network for Iris Data Classification as an Effort to Overcome System Control Problems

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