Deteksi Kesamaan Teks Jawaban pada Sistem Test Essay Online dengan Pendekatan Neural Network

E-learning is an online learning system that applies information technology in the teaching process. E-learning used to facilitate information delivery, learning materials and online test or assignments. The online test in evaluating students’ abilities can be multiple choice or essay. Online test with essay answers is considered the most appropriate method for assessing the results of complex learning activities. However, there are some challenges in evaluating students essay answers. One of the challenges is how to make sure the answers given by students are not the same as other students answers or 'copy-paste'. This study makes a similarity detection system (Similarity Checking) for students' essay answers that are automatically embedded in the e-learning system to prevent plagiarism between students. In this paper, we use Artificial Neural Network (ANN), Latent Semantic Index (LSI), and Jaccard methods to calculate the percentage of similarity between students’ essays. The essay text is converted into array that represents the frequency of words that have been preprocessed data. In this study, we evaluate the result with mean absolute percentage error (MAPE) approach, where the Jaccard method is the actual value. The experimental results show that the ANN method in detecting text similarity has closer performance to the Jaccard method than the LSI method and this shows that the ANN method has the potential to be developed in further research.

Download Full-text

Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽

10.3390/s21155188 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5188

Author(s):

Mitsugu Hasegawa ◽

Daiki Kurihara ◽

Yasuhiro Egami ◽

Hirotaka Sakaue ◽

Aleksandar Jemcov

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Augmentation ◽

Percentage Error ◽

Pressure Sensitivity ◽

Pressure Sensitive Paint ◽

Pressure Sensitive ◽

Artificial Neural ◽

Paint Thickness ◽

Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

Download Full-text

Literature Survey for Applications of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch031 ◽

2022 ◽

pp. 669-682

Author(s):

Pooja Deepakbhai Pancholi ◽

Sonal Jayantilal Patel

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Weather Forecasting ◽

Handwriting Recognition ◽

Complete Solution ◽

Learning System ◽

E Learning ◽

Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Download Full-text

Univariate Artificial Neural Network in Forecasting Demand of Low Cost House in Petaling Jaya

Jurnal Teknologi ◽

10.11113/jt.v40.406 ◽

2012 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Norhisham Bakhary ◽

Khairulzan Yahya ◽

Chin Nam Ng

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Low Cost ◽

Housing Demand ◽

Percentage Error ◽

Potential Applications ◽

Low Cost Housing ◽

Artificial Neural ◽

Artificial Neural Network Ann

Kebelakangan ini ramai penyelidik mendapati ‘Artificial Neural Network’ (ANN) untuk digunakan dalam berbagai bidang kejuruteraan awam. Banyak aplikasi ANN dalam proses peramalan menghasilkan kejayaan. Kajian ini memfokuskan kepada penggunaan siri masa ‘Univariate Neural Network’ untuk meramalkan permintaan rumah kos rendah di daerah Petaling Jaya, Selangor. Dalam kajian ini, beberapa kes bagi sesi latihan dan ramalan telah dibuat untuk mendapatkan model terbaik bagi meramalkan permintaan rumah. Nilai RMSE yang paling rendah yang diperolehi bagi tahap validasi adalah 0.560 dan nilai MAPE yang diperolehi adalah 8.880%. Hasil kajian ini menunjukkan kaedah ini memberikan keputusan yang boleh diterima dalam peramalan permintaan rumah berdasarkan data masa lalu. Kata kunci: Univariate Neural Network, permintaan rumah kos rendah, RMSE, MAPE Recently researchers have found the potential applications of Artificial Neural Network (ANN) in various fields in civil engineering. Many attempts to apply ANN as a forecasting tool has been successful. This paper highlighted the application of Time Series Univariate Neural Network in forecasting the demand of low cost house in Petaling Jaya district, Selangor, using historical data ranging from February 1996 to Appril 2000. Several cases of training and testing were conducted to obtain the best neural network model. The lowest Root Mean Square Error (RMSE) obtained for validation step is 0.560 and Mean Absolute Percentage Error (MAPE) is 8.880%. These results show that ANN is able to provide reliable result in term of forecasting the housing demand based on previous housing demand record. Key words: Time Series Univariate Neural Network, low cost housing demand, RMSE, MAPE

Download Full-text

A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i4.pp609-615 ◽

2020 ◽

Vol 9 (4) ◽

pp. 609

Author(s):

Abdulrahman Jassam Mohammed ◽

Muhanad Hameed Arif ◽

Ali Adil Ali

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Intrusion Detection ◽

Multilayer Perceptron ◽

Detection System ◽

Denial Of Service ◽

Detection Accuracy ◽

Neural Network Approach ◽

Artificial Neural ◽

Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

Download Full-text

An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

Open Journal of Applied Biosensor ◽

10.4236/ojab.2014.34004 ◽

2014 ◽

Vol 03 (04) ◽

pp. 29-39 ◽

Cited By ~ 18

Author(s):

Bhargava Teja Nukala ◽

Naohiro Shibuya ◽

Amanda Rodriguez ◽

Jerry Tsay ◽

Jerry Lopez ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Gait Analysis ◽

Detection System ◽

Fall Detection ◽

Support Vector ◽

Artificial Neural ◽

Artificial Neural Network Ann

Download Full-text

The Use of an Artificial Neural Network for Predicting the Gloss of Thermally Densified Wood Veneers

BALTIC FORESTRY ◽

10.46490/bf422 ◽

2021 ◽

Vol 27 (2) ◽

Author(s):

Şükrü Özşahin ◽

Hilal Singer

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Model Performance ◽

Coefficient Of Determination ◽

Densified Wood ◽

Percentage Error ◽

Angle Of Incidence ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this study, an artificial neural network (ANN) model was developed to predict the gloss of thermally densified wood veneers. A custom application created with MATLAB codes was employed for the development of the multilayer feed-forward ANN model. The wood species, temperature, pressure, measurement direction, and angle of incidence were considered as the model inputs, while the gloss was the output of the ANN model. Model performance was evaluated by using the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R²). It was observed that the ANN model yielded very satisfactory results with acceptable deviations. The MAPE, RMSE, and R2 values of the testing period of the ANN model were found as 8.556%, 1.245, and 0.9814, respectively. Consequently, this study could be useful for the wood industry to predict the gloss with less number of tiring experimental activities.

Download Full-text

Prediction of rubber vulcanization using an artificial neural network

Hemijska industrija ◽

10.2298/hemind210511026l ◽

2021 ◽

Vol 75 (5) ◽

pp. 277-283

Author(s):

Jelena Lubura ◽

Predrag Kojic ◽

Jelena Pavlicevic ◽

Bojana Ikonic ◽

Radovan Omorjan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Large Scale ◽

Mean Squared Error ◽

Percentage Error ◽

Dense Layer ◽

Ann Model ◽

Different Temperatures ◽

Artificial Neural ◽

Artificial Neural Network Ann

Determination of rubber rheological properties is indispensable in order to conduct efficient vulcanization process in rubber industry. The main goal of this study was development of an advanced artificial neural network (ANN) for quick and accurate vulcanization data prediction of commercially available rubber gum for tire production. The ANN was developed by using the platform for large-scale machine learning TensorFlow with the Sequential Keras-Dense layer model, in a Python framework. The ANN was trained and validated on previously determined experimental data of torque on time at five different temperatures, in the range from 140 to 180 oC, with a step of 10 oC. The activation functions, ReLU, Sigmoid and Softplus, were used to minimize error, where the ANN model with Softplus showed the most accurate predictions. Numbers of neurons and layers were varied, where the ANN with two layers and 20 neurons in each layer showed the most valid results. The proposed ANN was trained at temperatures of 140, 160 and 180 oC and used to predict the torque dependence on time for two test temperatures (150 and 170 oC). The obtained solutions were confirmed as accurate predictions, showing the mean absolute percentage error (MAPE) and mean squared error (MSE) values were less than 1.99 % and 0.032 dN2 m2, respectively.

Download Full-text