scholarly journals Implementation of Backpropagation Neural Network Method in Classification System of Timeliness of Graduation

2019 ◽  
Author(s):  
Yanuar Nurdiansyah ◽  
Diksi Media ◽  
Fadhel Hizam
Keyword(s):  
Neural Network ◽  
Classification System ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method

Tumor diagnosis using the backpropagation neural network method

1998 ◽  
Author(s):  
Lixing Ma ◽  
Sydney Sukuta ◽  
Reinhard F. Bruch ◽  
Natalia I. Afanasyeva ◽  
Carl G. Looney
Keyword(s):  
Neural Network ◽  
Tumor Diagnosis ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method

scholarly journals Smart guided missile using accelerometer and gyroscope based on backpropagation neural network method for optimal control output feedback

2020 ◽  
Vol 11 (2) ◽  
pp. 55
Author(s):  
Kamil Faqih ◽  
Sujito Sujito ◽  
Siti Sendari ◽  
Faiz Syaikhoni Aziz
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Data Processing ◽  
Output Feedback ◽  
Training System ◽  
Backpropagation Neural Network ◽  
Target Speed ◽  
Neural Network Method ◽  
Network Method ◽  
Control Output

As a maritime country with a large area, besides the need to defend itself with the military, it also needs to protect itself with aerospace technology that can be controlled automatically. This research aims to develop an air defense system that can control guided missiles automatically with high accuracy. The right method can provide a high level of accuracy in controlling missiles to the targeted object. With the backpropagation neural network method for optimal control output feedback, it can process information data from the radar to control missile’s movement with a high degree of accuracy. The controller uses optimal control output feedback, which is equipped with a lock system and utilizes an accelerometer that can detect the slope of the missile and a gyroscope that can detect the slope between the target direction of the missile to follow the target, control the position, and direction of the missile. The target speed of movement can be easily identified and followed by the missile through the lock system. Sampling data comes from signals generated by radars located in defense areas and from missiles. Each part’s data processing speed is calculated using a fast algorithm that is reliable and has a level of accuracy and fast processing. Data processing impacts on the accuracy of missile movements on any change in the position and motion of targets and target speed. Improved maneuvering accuracy in the first training system can detect 1000 files with a load of 273, while in the last training, the system can detect 1000 files without a load period. So the missile can be guided to hit the target without obstacles when maneuvering.

scholarly journals Identifikasi Mutu Buah Pepaya California (Carica Papaya L.) Menggunakan Metode Jaringan Syaraf Tiruan

2021 ◽  
Vol 10 (1) ◽  
pp. 113-119
Author(s):  
Muhammad Ezar Al Rivan ◽  
Gabriela Repca Sung
Keyword(s):  
Neural Network ◽  
Carica Papaya ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Hidden Layer ◽  
The Tropics

Papaya is one of the fruits that grows in the tropics area, one of the kinds that people’s love the most is papaya California. The quality identification of papaya California fruit can be measured using color, defect, and size. Color, defect and size extracted from image of papaya. The dataset that used in this research are 150 images papaya California. The dataset consist of 3 quality there are good, fair and low.  Identification of papaya using the backpropagation neural network method with 17 training function in each training data with 3 different neurons in the hidden layer. The best result of the test is using training function trainrp with 10 neurons is 81,33% for accuracy, 73,37% for precision, and 72% for recall, with 20 neurons is 82,67% for accuracy, 75,24% for precision, and 74% for recall, and with 25 neurons is 80,89% for accuracy, 74,42% for precision, and 71,33% for recall.

scholarly journals The application of backpropagation neural network method to estimate the sediment loads

2017 ◽  
Vol 101 ◽  
pp. 05016 ◽  
Author(s):  
Taufik Ari Gunawan ◽  
M. Syahril Badri Kusuma ◽  
M. Cahyono ◽  
Joko Nugroho
Keyword(s):  
Neural Network ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Sediment Loads ◽  
Network Method

scholarly journals Prediksi Jumlah Penjualan Produk di PT Ramayana Pematangsiantar Menggunakan Metode JST Backpropagation

2020 ◽  
Vol 7 (1) ◽  
pp. 175
Author(s):  
Muhammad Syafiq ◽  
Dedy Hartama ◽  
Ika Okta Kirana ◽  
Indra Gunawan ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Retail Industry ◽  
Backpropagation Neural Network ◽  
Accuracy Rate ◽  
Neural Network Method ◽  
Network Method ◽  
The One ◽  
Product Sales ◽  
Retail Products

Product is the one of thing which more important in the business especially for the retail industry. Ramayana is the one exact place for selling retail products such as clothing, shoes, or slipper. On 2012-2018, the number of sales of products in Ramayana experience curve up and down. That thing can cause profit and lose for Ramayana, to avoid that thing need to be held a prediction for the next months so that Ramayana side can know what will happen in the next months in selling it’s product and can take a step for more effective in selling it’s products. The data which used in this research is the data report monthly product sales of shoes & sandal sourced from Ramayana from 2012 until 2018. This research uses the Backpropagation neural network method using 5 architectures namely 3-26-1, 3-31-1, 3-35-1, 3-39-1 and 3-40-1. The best architecture is 3-35-1 with an accuracy rate of 92%. The results obtained are the results of the prediction of the number of sales for 2019, 2020, 2021 and 2022

scholarly journals Website Visitors Forecasting using Recurrent Neural Network Method

2021 ◽  
Vol 6 (2) ◽  
pp. 137-145
Author(s):  
Putu Bagus Arya ◽  
Wayan Firdaus Mahmudy ◽  
Achmad Basuki
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Comparison Method ◽  
Backpropagation Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Neural Network Method ◽  
Network Method ◽  
Long Short Term Memory

Abstract. The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.

scholarly journals Application Design of the Medicines Usage Prediction Based on Backpropagation Neural Network Method and PHP

2019 ◽  
Vol 2 (2) ◽  
pp. 40
Author(s):  
I Putu Arya Dharmaadi ◽  
Gusti Made Arya Sasmita
Keyword(s):  
Neural Network ◽  
Information System ◽  
System Development ◽  
Hospital Staff ◽  
Stock Management ◽  
Backpropagation Neural Network ◽  
Drug Usage ◽  
Neural Network Method ◽  
Medical Centers ◽  
Network Method

The development of information technology makes many organizations utilizing it in their business process. For example, hospitals use certain information systems in medicine management. We observe that most medicines applications do not provide the drug usage prediction feature so that this situation causes the hospital staff being difficult in providing enough medicines. Therefore, in this experimental research, we developed an application in the form of a simple design for helping the hospitals in predicting daily medicine usage. This application also provides medicines stock management and doctor diagnosis features. The Brainy library is used to facilitate implementing the backpropagation neural network method in PHP programming language. We choose PHP because this server script is widely used in information system development. We demonstrated that the mock-up as the result of this development is able to work properly. For further study, we suggest expanding this mock-up become a full hospital information system that covers many functions in medical centers.

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