Optimization of Genetic Algorithms on Backpropagation Neural Network to Predict National Rice Production Levels

Sugiyarto, Aditya Wisnugraha; Wutsqa, Dhoriva Urwatul; Hendiyani, N.; Rasjava, Achmad Ramadhanna’il

doi:10.1109/icaiti48442.2019.8982118

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The significance level in this research can be categorized as having excellent forecasting capabilities. Additionally, research related to predicting the national rice production rate in Indonesia using the BPNN method resulted in a MAPE value of 2.3394%, albeit after optimization with a genetic algorithm [16]. This study is similar to the research conducted in [15], [16], with the only difference being the algorithm used.…”

Section: Resultssupporting

confidence: 66%

“…Additionally, research related to predicting the national rice production rate in Indonesia using the BPNN method resulted in a MAPE value of 2.3394%, albeit after optimization with a genetic algorithm [16]. This study is similar to the research conducted in [15], [16], with the only difference being the algorithm used. Thus, the BPNN approach can provide a solution for predictive issues, as demonstrated in this case study to predict the sales value of PVC ceilings.…”

Section: Resultssupporting

confidence: 66%

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Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling

Purnawan,

Putra,

Fauzi

et al. 2024

informatech

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Sales predictions on building material products today have applied an artificial neural network approach. One of the products of building material that need to be predicted for sales is polyvinylchloride (PVC) ceilings. Most companies haven’t implementing prediction technique for the sale of PVC ceilings, so this study aims to predict PVC ceiling sales with the backpropagation neural network (BPNN) method using the R algorithm. Unit gradients are calculated using the average absolute per cent error value (MAPE) to minimize the total square errors of network output. The results showed that the network architecture used was 4 to 6-1 and obtained an accuracy of 88% based on the lowest MAPE value.

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Section: Resultssupporting

confidence: 66%

Section: Resultssupporting

confidence: 66%

Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling

Purnawan,

Putra,

Fauzi

et al. 2024

informatech

View full text Add to dashboard Cite

show abstract

“…Currently known as artificial intelligence (AI), a mechanical simulation system for gathering knowledge and information distribute them to parties who meet the requirements in the form of actionable intelligence [21]. Machine learning (ML) is part of AI, which is used to design algorithms based on data trends and historical relationships between data [22], [23]. In ML, many methods can be used, including fuzzy systems, artificial neural networks, deep learning, and evolutionary algorithms [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Classification of heart disease based on PCG signal using CNN

2021

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Cardiovascular disease is the leading cause of death in the world, so early detection of heart conditions is very important. Detection related to cardiovascular disease can be conducted through the detection of heart signals interference, one of which is called phonocardiography. This study aims to classify heart disease based on phonocardiogram (PCG) signals using the convolutional neural networks (CNN). The study was initiated with signal preprocessing by cutting and normalizing the signal, followed by a continuous wavelet transformation process using a mother wavelet analytic morlet. The decomposition results are visualized using a scalogram, then the results are used as CNN input. In this study, the PCG signals used were classified into normal, angina pectoris (AP), congestive heart failure (CHF), and hypertensive heart disease (HHD). The total data used, classified into 80 training data and 20 testing data. The obtained model shows the level of accuracy, sensitivity, and diagnostic specificity of 100%, 100%, and 100% for training data, respectively, while the prediction results for testing data indicate the level of accuracy, sensitivity, and specificity of 85%, 80%, and 100%, respectively. This result proved to be better than the mother wavelet or other classifier methods, then the model was deployed into the graphical user interface (GUI).

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“…Oryza sativa L. is one of the most important staple plants in the world (Londo et al 2006).O. sativa produces rice which is a staple food in most parts of the world, especially Indonesia (Sugiyarto et al 2019;Kumarathilaka et al 2018).The demand for rice is increasing every year along with the increasing population (Sakiko 2019).Nevertheless, the need for rice that continues to increase is not proportional with the increase of rice production (Adekoya and Ekeh 2019).This condition is likely caused by the rice plants condition and the growth environment that not optimal, so the rice production decreases (Li et al 2015;Asseng et al 2017). Lower rice production by rice plants is possible due to the influence of weather, overheating air temperature, soil and air humidity, and disease (Jabran et al 2015;Hubert et al 2015;Kumar et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

Rasjava

Sugiyarto

Kurniasari

et al. 2020

icse

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As a rice-producing plant, rice plant (Oryza sativa L.) is one of the most important crops in Indonesia. Rice production is increasing every year along with an increase in rice demand and population.The amount of rice production is affected by the condition of the rice plants. The worse the condition of rice plants, the rice production will also lower. Rice plant is very susceptible to diseases or pests that can reduce its productivity, including brown spot disease, leaf smut and bacterial leaf blight. As the development of science and technology, currently known as Artificial Intelligence. Artificial intelligence is a combination of several scientific disciplines such as mathematics, statistics, computer science, and even social science. Using artificial intelligence, the system now have the ability to interpret external data correctly to learn from the data and then use the learning to achieve certain goals through flexible adaptation. The artificial intelligence fields consists of several branches, such as machine learning and deep learning. Neural Network (NN) is one of the methods used in the deep learning.NN has many types, one of which is the Convolutional Neural Network (CNN). CNN is the best-knownmethod used for processingimages data compared to other types of NN. Therefore, in this study the identification of rice plants diseases was carriedout using CNN method. From this study,better results were obtained compared to other methods, obtaining 100% accuracy for training data and 86,67% for testing data. The model obtained by the CNN method can be used for detecting 3 different types of rice plants diseases, there are brown spots, leaf smuts, or bacterial leaf blight disease based on the physical images of rice plant leaves.

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Optimization of Genetic Algorithms on Backpropagation Neural Network to Predict National Rice Production Levels

Cited by 7 publications

References 10 publications

Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling

Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling

Classification of heart disease based on PCG signal using CNN

Detection of Rice Plants Diseases Using Convolutional Neural Network (CNN)

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