Multivariate Data Model Prediction Analysis Using Backpropagation Neural Network Method

Anggreani, Desi; Zaeni, Ilham Ari Elbaith; Handayani, Anik Nur; Azis, Huzain; Manga, Abdul Rachman

doi:10.1109/eiconcit50028.2021.9431879

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…The algorithm can solve complex problems by mathematically calculating the weighting of the network [21]. Backpropagation is an algorithm developed in a machine learning concept by adopting supervised learning [22]. Research in the process of analyzing this disease classification results in optimal machine learning performance and presents new knowledge in a pattern for detecting Gingivitis disease [23].…”

Section: Introductionmentioning

confidence: 99%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

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<span lang="EN-US">Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also used as a process of measuring analysis patterns to ensure the analytical model presents maximum results. The results study indicate that the optimization of fuzzy and MRL methods provides excellent output. These results are based on the fuzzy output which can provide a pattern of 40 rules. The MRL method is can present the level of correlation of each analysis variable with a significant output having an average value of 94.2%. Based on the results of this study, the analysis model that is optimized with the fuzzy logic method and MRL contributes to maximizing the process of diagnosing Gingivitis.</span>

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Section: Introductionmentioning

confidence: 99%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…Some of these include the analysis and prediction of five multivariate data models using Backpropagation ANN. The accuracy and execution time vary in the prediction process for these five multivariate data models, considering different variables and data sizes [10]. Backpropagation Neural Networks are highly effective for predicting bitcoin prices compared to ARIMA models [11].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Prediction Model Back Propagation on Blood Demand and Blood Supply

Tedy,

Batarius,

Samane

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition must be maintained to reduce unused or expired blood supplies. Despite the situation in UTD-PMI, where the blood supply exceeds demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying the forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the mean square error (MSE). Ultimately, the research results present the prediction model for the four types of blood with respect to the demand and supply. These findings can serve as a reference to regulate future blood donation activities carried out by the UTD-PMI.

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Unveiling Algorithm Classification Excellence: Exploring Calendula and Coreopsis Flower Datasets with Varied Segmentation Techniques

Azis,

Nirmala,

Syafie

et al. 2024

2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)

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Multivariate Data Model Prediction Analysis Using Backpropagation Neural Network Method

Cited by 5 publications

References 24 publications

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Artificial Neural Network-Based Prediction Model Back Propagation on Blood Demand and Blood Supply

Unveiling Algorithm Classification Excellence: Exploring Calendula and Coreopsis Flower Datasets with Varied Segmentation Techniques

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