Soft Solution of Soft Set Theory for Recommendation in Decision Making

Hakim, R. B. Fajriya; Sari, Eka Novita; Herawan, Tutut

doi:10.1007/978-3-319-07692-8_30

Cited by 18 publications

(8 citation statements)

References 16 publications

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“…Artificial Intelligence is one area that is quite reliable in solving problems such as prediction (forecasting) (Wanto, Zarlis, et al 2017). AI is a very important discipline and it includes a number of well recognized and mature areas including Neural Network (Chiroma, et al 2014) (Hakim, et al 2014) (Lasisi, et al 2014). Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention.…”

Section: Artificial Intelligencementioning

confidence: 99%

Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction

Wanto¹,

Andani²,

Poningsih³

et al. 2018

Preprint

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Gradient Descent (GD) is used to find the local minimum value, its purpose is to find variables on the errorfunction so that a function can model the data with minimum error. Therefore, the purpose of this researchis to see how much iteration is needed and how big is the accuracy level in predicting the data when usingGradient Descent (GD) Standard and GD With Momentum and Adaptive Learning Rate (GDMALR)functions. In this study, the data to be processed using the gradient descent function is the data of SchoolParticipation Rate (SPR) in Indonesia aged 19-24 years, which began in 2011 to 2017. The reason forselection This age range is one of the factors that determine success education in a country, especiallyIndonesia. SPR is known as one of the indicators of successful development of education services in an areaof either Province, Regency or City in Indonesia. The higher the value of SPR, then the area is consideredsuccessful in providing access to education services. SPR data are taken from Indonesian Central Bureau ofStatistics. This study uses 3 models of network architecture, namely: 5-5-1, 5-15-1 and 5-25-1. From 3models, the best model is 5-5-1 with epoch 6202 iteration, 94% accuracy and MSE 0.0008658637. Thismodel is then used to predict SPR in Indonesia for the next 3 years (2018-2020). These results will beexpected to help the Indonesian government to further improve the scholarship and improve the quality ofeducation in the future

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Section: Artificial Intelligencementioning

confidence: 99%

Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction

Wanto¹,

Andani²,

Poningsih³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In the learning process, to reduce the in accuracy of ANNs, BPLAs use the gradient decent search method to adjust the connection weights. The structure of a backpropagation ANN is shown in Figure 2 [19]. Each of these layers must be either of the following: 1.…”

Section: Architecture Of Backpropogationmentioning

confidence: 99%

ANN: Model of Back-Propagation Architecture on the Logs Production by Type of Wood

Siregar

2018

IJISTECH

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“…As the development of artificial intelligence, several other topics have been combined into artificial intelligence such as Expert Systems [2,3,4,13], Fuzzy Logic [5,6,7,8,9], Genetic Algorithms [10,11,12] , Language Processing, Logic Programming, Planning and Scheduling, Neural Networks and Robotics [12].…”

Section: Rudimentarymentioning

confidence: 99%

Artificial Neural Network with Backpropogation Method in Predicting Sukuk Retail State Based on Age Categories in Promoting Economic Growth

dedisuhendro¹

2018

Preprint

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Abstract:Sukuk Retail State has fixed remuneration that paid every month. The government gains equity from the use of public funds, while the public gets a profit from the investment. The contribution of this research provides benefits for promoting optimally on the next sukuk issuance. Referral data sourced from Ministry of Finance through website www.djppr.kemenkeu.go.id. The data are sukuk sales data series 003 -009 which are grouped into several categories namely geography, profession and age category. The method used is Artificial Neural Network Backpropogation. The input variables used are age category <25 (X1), age category 25 -40 (X2), age category 41 -55 (X3), and age category> 55 (X4) with model of training architecture and test of 4 architecture ie 4-2-1, 4-5-1, 4-2-5-1 and 4-5-2-1. The results of this study provide the best architecture 4-2-1 with epoch 1593, MSE 0.00099950214 and 71% accuracy rate. Furthermore, the sensitivity analysis was performed to determine the best performing variables, resulting in the 41-55 (X3) age category variable with a score of 0.4089. Thus obtained the prediction of most investors on the purchase of sukuk series 010 is the age category 41 -55.

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Soft Solution of Soft Set Theory for Recommendation in Decision Making

Cited by 18 publications

References 16 publications

Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction

Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction

ANN: Model of Back-Propagation Architecture on the Logs Production by Type of Wood

Artificial Neural Network with Backpropogation Method in Predicting Sukuk Retail State Based on Age Categories in Promoting Economic Growth

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