Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Sasongko, Sudi Mariyanto Al; Jayanti, Erni Dwi; Ariessaputra, Suthami

doi:10.30630/joiv.6.4.1532

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…Researchers have also investigated ANNs aiming to build energy management systems. In general, choosing output and input variables for an ANN has a large impact on the performance and the utilization or generalization of the network (Al Sasongko et al, 2022;Rudzki et al, 2022;Zheng Chen et al, 2014). It is noticed that the ECMS, an instantaneous optimization algorithms' representative, is considered the most potential online EMS and is currently widely applied in the real world (Kommuri et al, 2020;Wang and Huang, 2020).…”

Section: Artificial Neural Network For Energy Management and Forecastingmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

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The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.

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Section: Artificial Neural Network For Energy Management and Forecastingmentioning

confidence: 99%

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Le,

Priya,

et al. 2024

Int. J. Renew. Energy Dev.

View full text Add to dashboard Cite

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“…The hidden layer may have many layers, affecting the neural network's training process [30]. Research on the Neural Network Backpropagation method was carried out by [31] to predict the results of the Self-Assessment Questionnaire (SAQ) in 2021 on the Regional Government of East Java Province website.…”

Section: F Neural Network Backpropagationmentioning

confidence: 99%

Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method

Kurniawan,

Almais,

Hariyadi

et al. 2023

JOIV : Int. J. Inform. Visualization

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Performance allowance is a form of appreciation given by an agency to its human resources. The Office of the Ministry of Religion of Batu City provides performance allowances to civil servants who work in the agency. Several things that affect the provision of performance allowances, such as grade, deduction, taxable income, income tax, and total tax, are used in this study to produce the total gross performance allowances and total performance allowances received. Based on the data obtained, there are some missing data from the parameters of taxable income, income tax, and total tax. This study aims to predict performance allowance when there is missing data. The method used is Neural Network Backpropagation. This study uses 480 data with split data ratios of 50:50, 60:40, 70:30, and 80:20, with epochs 40,000 and a learning rate 0,9. Four types of models used in this study are distinguished based on the number of hidden layers and epochs used. Model A uses two hidden layers to produce the highest accuracy with a 50:50 data split ratio of 65,16%. Model B uses four hidden layers to produce the highest accuracy with a 50:50 data split ratio of 69,34%. Model C uses six hidden layers to produce the highest accuracy with a 50:50 data split ratio of 68,18%. Model D uses eight hidden layers to produce the highest accuracy with a 50:50 data split ratio of 70,90%.

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“…Songket and batik are human creations in Southeast Asia, especially Indonesia and Malaysia. Songket cloth is a traditionally woven product with a decorative and beautiful motif [1], [2]. It is made from mixed colorful cotton threads and uses a tool called Panta; a loom made of wood [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Ariessaputra,

Vidiasari,

Sasongko

et al. 2024

JOIV : Int. J. Inform. Visualization

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Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.

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Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Cited by 4 publications

References 23 publications

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

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