scholarly journals Sistem Peringatan Dini Ketahanan Perbankan Terhadap Guncangan Internal dan Eksternal Dengan Model Artifical Neural Network

2020 ◽  
Vol 2 (1) ◽  
pp. 107-116
Author(s):  
Muhammad Iqbal ◽  
Azmia Ulfah ◽  
Selamet Riyadi
Keyword(s):  
Neural Network ◽  
Artifical Neural Network

Sistem stabilitas keuangan adalah sistem kompleks yang terbentuk dan terkait dengan kebijakan ekonomi dan moneter di negara tersebut. Oleh karena itu diperlukan suatu model untuk dapat memprediksi secara cepat dan akurat ketidakstabilan sistem keuangan yang mungkin terjadi. Penelitian ini bertujuan untuk membangun model Artifical Neural Network (ANN) sebagai sistem peringatan dini untuk memprediksi kegagalan perbankan berdasarkan kepatuhan bank. Penelitian ini menggabungkan faktor internal dan eksternal yang mempengaruhi kinerja perbankan sebagai indikator. Hasil penelitian ini membuktikan bahwa ANN dapat digunakan sebagai metode alternatif untuk mendeteksi tingkat keberlanjutan suatu bank.

scholarly journals Functional Simulation of Human Blood Identification Device using Feed-Forward Artificial Neural Network for FPGA Implementation

2018 ◽  
Author(s):  
Denny Darlis ◽  
Heri Murwati ◽  
Rizki Ardianto Priramadhi ◽  
Mohamad Ramdhani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Success Rate ◽  
Human Blood ◽  
Fpga Implementation ◽  
Blood Type ◽  
Network Algorithms ◽  
Artifical Neural Network ◽  
Functional Simulation ◽  
Hardware Description

The identification of human blood type stillrequires a fast and accurate device considering the number ofblood samples that need to be distributed and transfusedimmediately. In this study we propose a hardwareimplementation of human blood type identification devices usingfeedforward neural network algorithms on grayscale images ofblood samples. The images to be used are 32x32 pixels, 48x48pixels, 64x64, 80x80, and 9x96 pixels. The algorithm wereimplemented using VHSIC Hardware Description Language.With artifical neural network implemented on Xilinx FPGASpartan 3S1000, the success rate of detection by grouping by themean and median ratios of the number of '1' bits is more than75%.

scholarly journals Classification by Artificial Neural Network according to the values affecting Electricity Generation

2018 ◽  
pp. 001-004
Author(s):  
Esmeray Furkan ◽  
Korkmaz Sevcan Aytaç
Keyword(s):  
Neural Network ◽  
Electricity Generation ◽  
Superheated Steam ◽  
Electricity Production ◽  
Water Tank ◽  
Neural Network Classifier ◽  
Gas Temperature ◽  
Clustering Method ◽  
Artifical Neural Network ◽  
Fan Speed

Predicting the amount of electricity produced in a power plant is very important for today’s economy. Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, Feeding Water Tank data affect the electricity production. In this article, Etikrom A.Ş. The electricity production amount to be produced in Elazığ Etikrom A.Ş. was estimated by using the data of Oven Power (MW), Water Inlet Gas Temperature, Steam Vapor Volume, ID-Fan Speed, Feeding Water Tank data. Electricity generation amount is used as verification data. That is, by the k-means clustering method, the electricity generation amount is divided into 3 classes (low, medium, and high). 3621 data including Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, and Feeding Water Tank data were used after class 3 separation. With the K-means clustering method, 2742 of these data were clustered as low electricity, 296 as medium electricity and 583 as high electricity. This clustered data was given to the Artifical Neural Network classifier. The success rate obtained as a result of this classification is 85.81%. Classified data were analyzed by ROC curve.

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