scholarly journals Prediksi Tingkat Kelancaran Kredit BSU BMT Tunas Harapan Syari’ah Pringgasela Kabupaten Lombok Timur Menggunakan Algoritma Neural Network

2021 ◽  
Vol 4 (2) ◽  
pp. 205-216
Author(s):  
Amri Muliawan Nur ◽  
◽  
Imam Fathurrahman ◽  
Yahya Yahya ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Processing ◽  
Statistical Data ◽  
Network Data ◽  
Mining Method ◽  
The Neural Network ◽  
Using Data ◽  
Data Processing Methods

The role of credit in a cooperative is very important. With the credit can be a source of profit for the cooperative. The cooperative was founded with the aim of prospering its members. One of the advantages is that cooperative members can apply for credit loans. To approve the proposed loan, it is necessary to analyze the credit submitted by the members. This has become one of the difficulties for several cooperatives, one of which is KSU BMT Tunas Harapan Syari'ah which is located in thePringgasela village, Pringgasela District, East Lombok Regency. The problem that often arises is that the analysis conducted is often incorrect, resulting in a prolonged bad credit in installment payments. The reason is that cooperatives always use statistical data which is sometimes inaccurate because there is no processing using data processing methods. Therefore, the neural network data mining method can be used as a tool to analyze which customers are problematic and not problematic. From the results of the research that has been done, it produces an accuracy of 96.19% and an AUC of 0.976

scholarly journals Application of intellectual data analysis methods for digital educational platform

2020 ◽  
pp. 1-5
Author(s):  
Kristina Zhatkina ◽  
Oksana Kreider
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Services ◽  
Data Mining Techniques ◽  
Analysis Methods ◽  
The Neural Network ◽  
Competency Based ◽  
Using Data ◽  
Data Analysis Methods

This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.

scholarly journals Data Mining using Artificial Neural Network POS_NEG Rules

2013 ◽  
Vol 11 (6) ◽  
pp. 2709-2714
Author(s):  
Pushkar Shinde ◽  
Dr. Varsha Patil
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

scholarly journals Neural Network Model for Mathematic Scores Prediction: Case Study in SMK Negeri Pakis Aji, Jepara, Indonesia

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Adi Sucipto ◽  
Joko Minardi
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Mean Square ◽  
Mathematics Scores ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Conducting Research ◽  
Using Data

Aim of this research is to apply Neural Network Algorithm to predict score of mathematic in the national exam. During the time, the teacher only provided national exam materials and additional tryout tests without knowing how to predict the exam scores in mathematics subject. Data mining neural network algorithm obtained \Root Mean Square Error (RMSE) values which were used as basic improvement and clustering class By conducting research using data mining neural network algorithm, it proved that this model can be used to predict scores of Mathematics subject at SMK Negeri 1 Pakis Aji.. The result of this research by using data mining neural network algorithm found RMSE 0138 +/- 0.092. The lower the RMSE values the more accurate the neural network to predict mathematics scores of SMK Negeri 1 Pakis Aji.Received: 18 Agustus 2019; Accepted: 5 Januari 2020; Published: 14 January 2020

Neural Network and Data Mining Method in Aerodynamic Optimization

2011 ◽  
Vol 52-54 ◽  
pp. 1421-1426
Author(s):  
Xin Jin ◽  
Gang Sun ◽  
Chun Juan Liu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Qualitative Analysis ◽  
Optimization Process ◽  
Mining Method ◽  
Aerodynamic Optimization ◽  
Mining Technology ◽  
Quantitative Calculation ◽  
Using Data ◽  
Intelligent Approach

This document focuses on the case that optimization direction is hard to determine in aerodynamic optimization processes, and proposes an intelligent approach based on ANN learning, followed by result visualization using data mining technology. It can not only help visualize the optimization process (qualitative analysis), but also determine the optimization direction (quantitative calculation), which can save time of frequent CFD calculation and obviously improve efficiency in aerodynamic optimization.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

ANALYSIS OF THE FLOODING SYSTEM WITH APPLICATION OF STATISTICAL DATA PROCESSING METHODS

2020 ◽  
pp. 5-9
Author(s):  
A.Yu. Sentsov ◽  
◽  
I.V. Ryabov ◽  
A.A. Ankudinov ◽  
Yu.E. Radevich ◽  
...  
Keyword(s):  
Data Processing ◽  
Statistical Data ◽  
Processing Methods ◽  
Statistical Data Processing ◽  
Data Processing Methods

Use of Mathematical Modeling in Building Ceramics Functional Properties Studies

2016 ◽  
Vol 870 ◽  
pp. 191-195
Author(s):  
N.A. Vil'bitskaya ◽  
S.A. Vilbitsky ◽  
A.G. Avakyan
Keyword(s):  
Mathematical Modeling ◽  
Data Processing ◽  
Ceramic Material ◽  
Functional Properties ◽  
Statistical Data ◽  
Ceramic Tiles ◽  
Processing Methods ◽  
Statistical Data Processing ◽  
High Calcium ◽  
Data Processing Methods

The peculiarities of using mathematical and statistical data processing methods in studying the intensification in the process of sintering a ceramic material with a high content of high-calcium waste, and mineralizing sintering lithium-containing waste were studied. The region of optimal ceramic masses composition, which allows obtaining ceramic tiles with high functional properties, was defined.

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