Implementation artificial neural network nguyen widrow algorithm for lupus prediction

Aisyah, Siti; Harahap, Mawaddah; Dharma, Abdi; Turnip, Mardi

doi:10.1088/1742-6596/1361/1/012067

Cited by 3 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The BPNN architecture 5-11-1 with Nguyen-Widrow initial weights can achieve excellent forecasting performance. The application of the Nguyen-Widrow Initialization algorithm has been proven to improve prediction accuracy, as shown in previous research (Aisyah et al, 2019;Rosmaliati et al, 2018). This architecture is subsequently utilized for forecasting for the next 60 days, specifically from July 11, 2023, to October 5, 2023.…”

Section: Momentummentioning

confidence: 90%

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

Haris,

Setyaningsih,

Fauzi

et al. 2024

JTAM

View full text Add to dashboard Cite

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.

show abstract

Section: Momentummentioning

confidence: 90%

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

Haris,

Setyaningsih,

Fauzi

et al. 2024

JTAM

View full text Add to dashboard Cite

show abstract

“…Inisialisasi bobot; dalam penelitian ini ada dua macam inisialisasi bobot, yaitu inisialisasi bobot pada jaringan saraf swa organisasi dengan memilih salah satu dari vektor data dan inisialisasi bobot pada jaringan saraf propagasi balik yang ditentukan secara random dan metode Nguyen-Widrow. Inisialisasi ini banyak digunakan karena terbukti dapat meningkatkan waktu eksekusi [7]- [12].…”

Section: Parameter Yang Digunakan Sebagai Variabel Pada Jaringan Sara...unclassified

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

Elviralita¹,

Hidayat

2022

motivection

View full text Add to dashboard Cite

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 %.

show abstract

“…Artificial neural network (ANN) is a topic of study that continues to be explored and implemented in a variety of applications. [1]- [8]. ANN is an algorithm that imitates neural performance, as its name suggests.…”

Section: Introductionmentioning

confidence: 99%

ANN Activation Function Comparative Study for Sinusoidal Data

Putra

Azmi²,

Muslikhin³

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

An artificial neural network (ANN) has neurons that are interconnected. Inside the neuron, there is an activation function that determines the value issued by the neuron. There are several types of activation functions. The choice of activation function will determine the result of an ANN. This paper describes the performance comparison of several activation functions used in an ANN in processing sinusoidal signals. There are three types of activation functions being compared, those are Sigmoid, Tansig, and ReLU. The sinusoidal dataset has come from simulation data of the PMSM FOC control process. The results indicated that the Tansig activation function is the best choice for sinusoid data.

show abstract

Implementation artificial neural network nguyen widrow algorithm for lupus prediction

Cited by 3 publications

References 7 publications

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

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

ANN Activation Function Comparative Study for Sinusoidal Data

Contact Info

Product

Resources

About