Investigation of the Attitudes for Environment and Evaluation of Artificial Neural Networks

This study was conducted to evaluate the attitudes of the students in a secondary school in Aksaray towards the environment according to some variables. The research group of the study was constituted of 426 students who were attending in the 1st, 2nd, 3rd, and 4th grade at a secondary school in the academic year of 2015-2016. The research done by using environmental attitude scale concluded that the secondary students have a positive attitude towards the environment. It was found that there was a meaningful difference according to gender, age group, father profession status, mother profession status. It was also concluded that students did not differ according to mother education level, father education level, grade level, family income level, and number of siblings variables. Similar evaluations were made with artificial neural networks. In this study, it has been shown that artificial neural networks can be used in the studies conducted in the field of education.

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Penerapan Algoritma Backpropagation Pada Game Pengenalan Nahwu Di Mi Darul Falah Jepara

POSITIF Jurnal Sistem dan Teknologi Informasi ◽

10.31961/positif.v5i2.831 ◽

2019 ◽

Vol 5 (2) ◽

pp. 103

Author(s):

R Hadapiningradja Kusumodestoni ◽

Adi Sucipto ◽

Sela Nur Ismiati ◽

M Novailul Abid

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Technological Development ◽

Technological Advancement ◽

Artificial Neural ◽

Academic Year ◽

Class Iv ◽

The Way

Nahwu is a science that studies Arabic grammar. Nevertheless, the interest of students in learning nahwu is currently decreasing. It happens because the technological advancement vastly develops but the way of learning it is still conventional and tends to be boring. Based on the school data for the academic year of 2017/2018 at MI Darul Falah Sirahan Cluwak Pati, there were only 20 students able to undestand nahwu well out of 60 students who started learning nahwu in class IV. Technological development has brought many changes to things around us. One of the most developed at the meantime is game. Lately games have become something very fast developing. Using the game to be used as a secondary learning media for students in learning nahwu is considered quite effective. The method used in designing this game was Backpropagation meaning an algorithm based on artificial neural networks which is used to determine and take decision that is used to determine scores and levels in the Nahwu Introduction Game. The tools used in this game-making are construct 2, an HTML5-based game maker specifically for the 2d platform. The results of this study were an Android-based Nahwu Introduction Game.

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Artificial neural networks applied for predicting and explaining the education level of Twitter users

Social Network Analysis and Mining ◽

10.1007/s13278-021-00832-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexandru Razvan Florea ◽

Monica Roman

Keyword(s):

Neural Networks ◽

Social Media ◽

Artificial Neural Networks ◽

Education Level ◽

Social Characteristics ◽

The Social ◽

Ann Models ◽

Data Source ◽

Artificial Neural ◽

Twitter Users

AbstractThis paper provides a novel procedure to estimate the education level of social network (SN) users by leveraging artificial neural networks (ANN). Additionally, it provides a robust methodology to extract explanatory insights from ANN models. It also contributes to the study of socio-demographic phenomena by utilizing less explored data sources, such as social media. It proposes Twitter data as an alternative data source for in-depth social studies, and ANN for complex patterns recognition. Moreover, cutting edge technology, such as face recognition, on social media data are applied to explain the social characteristics of country-specific users. We use nine variables and three hidden layers of neurons to identify high-skilled users. The resulted model describes well the level of education by correctly estimating it with an accuracy of 95% on the training set and an accuracy of 92% on a testing set. Approximately 30% of the analyzed users are highly skilled and this share does not differ among the two genders. However, it tends to be lower among users younger than 30 years old.

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PREDIKSI PERILAKU POLA JUMLAH MAHASISWA MENGGUNAKAN JARINGAN SYARAF TIRUAN DENGAN METODE BACKPROPAGATION

JURNAL TEKNOLOGI INFORMASI ◽

10.36294/jurti.v1i2.288 ◽

2018 ◽

Vol 1 (2) ◽

pp. 145

Author(s):

Yustria Handika Siregar

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Training Data ◽

School Year ◽

Optimal Output ◽

Architectural Patterns ◽

Backpropagation Method ◽

Artificial Neural ◽

Academic Year

Abstrack - This study aims to predict the behavior of student patterns so that they can predict based on the number of students. To achieve optimal output, this study uses Artificial Neural Networks with the Backpropagation method. Case study conducted at the Asahan University Faculty of Engineering. The data used are data on the number of students in the academic year 2011 to 2013 as training data and 2014 school year data until 2016 as testing data. Furthermore, the data is analyzed with several network architectural patterns and the best patterns will be selected to be implemented into the Matlab R2010 program. The system results show a correlation between the number of students that occurred. Keywords - Prediction, Artificial Neural Networks, Backpropagation Method, Number of Students

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