scholarly journals KLASIFIKASI USIA DENGAN CITRA PADA REGISTRASI GAME ONLINE

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Ni Komang Sri Julyantari ◽  
Rukmi Sari Hartati ◽  
Made Sudarma
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Online Games ◽  
Training Data ◽  
Human Face ◽  
Online Game ◽  
Human Face Detection ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Backpropagation Artificial Neural Network

ABSTRACT The importance of the biggest age that is utilized in sharing information technology is one of them is registering online games. Nowadays the proliferation of online games among the wider community is no exception in the class of children who fall into the category of not enough age. When playing online games, players are required to register early to log in. In registering the player is realized to enter the date of birth and this can be manipulated by players who really should be old enough or do not deserve to play online games. To overcome the falsehood or manipulation of data, the player who will register can provide a format photo. JPG with this photo issued the system to be built to know us really from the person who made the registration. To be able to perform face detection and age classification using the Viola Jones method which will be used for human face detection and the backpropagation artificial neural network method is used for classification. In this study the data used is https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/ by entering 100 data as test data and 200 as training data, then the findings obtained are 75% appropriate results Keywords: Age Classification, Online Game, Face Recognition<br />ABSTRAK<br />Pentingnya usia banyak dimanfaatkan dalam berbagi bidang teknologi informasi salah satunya adalah. Saat ini maraknya game online dikalangan masyarakat luas tidak terkecuali di kalangan anak-anak yang termasuk dalam kategori belum cukup umur. Pada saat melakukan permainan game online, pemain wajib melakukan register awal untuk dapat melakukan login. Dalam melakukan register pemain diwajibkan untuk memasukkan tanggal lahir dan hal ini dpat dimanipulasi oleh pemain yang memang seharunya cukup umur atau belum pantas memainkan game online. Untuk mengatasi kepalsuan atau manipulasi data maka pemain yang akan melakukan register dapat memberikan foto dengan format. JPG dengan foto ini nantinya system yang akan dibangun dapat mengenal usis sebenarnya dari orang yang melakukan register tersebut. Untuk dapat melakukan deteksi wajah dan klasifikasi usia menggunakan metode viola jones yang akan digunakan untuk deteksi wajah manusia dan metode jaringan saraf tiruan backpropagation digunakan untuk klasifikasi. Dalam penelitian ini data yang digunakan 100 data sebagai data uji dan 200 sebagai data latih dari https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/, maka hasil akurasi yang didapat adalah 75% hasil sesuai.<br />Kata kunci : Kalsifikasi Usia, Game Online, Deteksi Wajah

scholarly journals Implementation of Backpropagation Artificial Network Methods for Early Children’s Intelligence Prediction

2021 ◽  
Vol 328 ◽  
pp. 04033
Author(s):  
I Budiman ◽  
A Mubarak ◽  
S Kapita ◽  
S Do. Abdullah ◽  
M Salmin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Childhood ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Backpropagation Artificial Neural Network

Intelligence is the ability to process certain types of information derived from human biological and psychological factors. This study aims to implement a Backpropagation artificial neural network for prediction of early childhood intelligence and how to calculate system accuracy on children's intelligence using the backpropagation artificial neural network method. The Backpropagation Neural Network method is one of the best methods in dealing with the problem of recognizing complex patterns. Backpropagation Neural Networks have advantages because the learning is done repeatedly so that it can create a system that is resistant to damage and consistently works well. The application of the Backpropagation Neural Network method is able to predict the intelligence of early childhood. The results of the calculation of the Backpropagation Artificial Neural Network method from 42 children's intelligence data being tested, with 27 training data and 15 test data, the results obtained 100% accuracy percentage results.

scholarly journals BRADYCARDIA AND TACHYCARDIA DETECTION SYSTEM WITH ARTIFICIAL NEURAL NETWORK METHOD

2016 ◽  
Vol 3 (2) ◽  
pp. 86
Author(s):  
Delima Ayu S ◽  
Franky Arisgraha ◽  
Retna Apsari
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Embedded System ◽  
Medical Devices ◽  
Detection System ◽  
Training Data ◽  
Backpropagation Neural Network ◽  
Neural Network Classification ◽  
Network Method ◽  
Artificial Neural Network Method

Heart disease is one disease with high mortality rate in the world. Based on WHO records from 112 countries at 2004, the rate is 29% of all deaths each year. Medical devices are necessary to diagnose one's health as an indication of a disease. Nowadays, Indonesia still imports medical devices, for the diagnosis of heart failure, from abroad. This research aims to assist the monitoring of cardiac patients with bradycardia and tachycardia appearances of message condition patient’s heart rate at the same time. The results were displayed with the output of bradycardia condition of the heart rate (heart rate less than 60 beats per minute) or tachycardia (heart rate over 100 beats per minute). The system displayed the data read from the heart to the PC embedded system to monitor the condition of the patients under decisions based on backpropagation neural network. Classification system could be performed quite well, training data and by testing the 10 pieces, the optimal weight gain was 1727 iteration, the learning rate was 0.1122, and the error was below 0.001 (0.0009997).

scholarly journals Automation of (Macro)molecular Properties Using a Bootstrapping Swarm Artificial Neural Network Method: Databases for Machine Learning

2019 ◽  
Author(s):  
Blerta Rahmani ◽  
Hiqmet Kamberaj
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Quantum Mechanics ◽  
Molecular Properties ◽  
Training Data ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

AbstractIn this study, we employed a novel method for prediction of (macro)molecular properties using a swarm artificial neural network method as a machine learning approach. In this method, a (macro)molecular structure is represented by a so-called description vector, which then is the input in a so-called bootstrapping swarm artificial neural network (BSANN) for training the neural network. In this study, we aim to develop an efficient approach for performing the training of an artificial neural network using either experimental or quantum mechanics data. In particular, we aim to create different user-friendly online accessible databases of well-selected experimental (or quantum mechanics) results that can be used as proof of the concepts. Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service. There are four databases accessible using the web-based service. That includes a database of 642 small organic molecules with known experimental hydration free energies, the database of 1475 experimental pKa values of ionizable groups in 192 proteins, the database of 2693 mutants in 14 proteins with given values of experimental values of changes in the Gibbs free energy, and a database of 7101 quantum mechanics heat of formation calculations.All the data are prepared and optimized in advance using the AMBER force field in CHARMM macromolecular computer simulation program. The BSANN is code for performing the optimization and prediction written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bonds properties, and for the macromolecular systems, they take into account the chemical-physical fingerprints of the region in the vicinity of each amino acid.Graphical TOC Entry

scholarly journals HUMAN FACE DETECTION METHODS USING COMBINED CASCADE OF CLASSIFIERS

2014 ◽  
pp. 114-125
Author(s):  
Ihor Paliy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Detection Methods ◽  
Classification Error ◽  
Asymmetric Structure ◽  
Human Face ◽  
High Detection Rate ◽  
Structure Generation ◽  
Human Face Detection

The paper presents the improved human face detection method using the combined cascade of classifiers with the improved face candidates’ verification approach, as well as methods and algorithms for the verification level (convolutional neural network) structure generation and training. The combined cascade shows a high detection rate with a very small number of false positives and the proposed candidates’ verification approach is in almost 3 times faster in comparison with the classic verification scheme. The network’s structure generation method allows creating the sparse asymmetric structure of the convolutional neural network automatically. The improved training method uses the adaptive training examples ratio to obtain a trained network with a very low classification error for the positive examples.

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