Concentration level prediction classification based on iq using backpropagation ann by IJRTER

Concentration Level Prediction Classification Based On IQ Using Backpropagation ANN T. Henny Febriana Harumy 1, Indri Sulistianingsih2 Faculty of Computer Science Universitas Pembangunan Panca Budi Jl. Jend. Gatot Subroto Km. 4,5 Sei Sikambing, 20122, Medan, Sumatera Utara, Indonesia

Abstract â&#x20AC;&#x201D; Intelligence quotient is the figure which describes a person's intelligence level were compared with each other in a population. A classification method with back propagation algorithm can perform classification on the level of concentration of the human brain. This classification is based on IQ components. It is a particular section to record brain activity. This analysis uses five samples used were taken at random to obtain three types of results, low, normal and high. After a process, it generated the output performance of 0.0344 and 0.9854 as the first rank with the highest IQ level. The high IQ level has the concentration of an 88% accuracy rate. Keywords â&#x20AC;&#x201D; Backpropagation, IQ, ANN, Classification I. INTRODUCTION For several years, people who are looking for a non-muscular channel between the brain and the outside world so that they can control the device by thinking [1][4]. With the production of the sophisticated bio instruments to record and amplify signals as well as the personal computer easy and powerful. This dream comes true, and Brain Computer Interface (BCI) has been developed. BCI is a type of communication system that translates the brain activity into commands, allowing users to control computer applications specifically or other devices just by her way of thinking. Electrodes acquired the signals from the brain on the scalp and processed to extract specific features which reflect the intentions of the user. These features are then translated into commands that operate application or device. It must be developed and maintained a good correlation between the intentions of the user and features a signal used by BCI. BCI should select and extract the features that the user can control and translate these features into the command device correctly and efficiently. For that, the brain activity to be monitored. In this case, there are various techniques used to achieve it. Some BCI is based on pattern recognition classification approach. In the second method the user must learn to organize themselves to respond EEG yum. For example, by changing the amplitude rhythm. The different components of the EEG signals have been widely shown to have a measurable correlation with the activity of the brain involved in the particular mental tasks. The signal can be decoded in real-time to commands that operate a computer screen or other device. Successful operation requires that encode the user commands in these signals obtained an order from the signal. Thus, users and BCI systems need to adapt to one another both in the beginning and kept so as to ensure stable performance. For this application, BCI can challenge other classical communication devices; they must be reliable, fast, and provide efficient solutions. BCI currently has the maximum information transfer rate of 10-29 bits/min. This limited capacity can be useful for people who are severely disabled to prevent them using conventional augmentative communication methods. However, many possible applications of BCI technology may require a degree of classification accuracy and a higher transfer information. In this paper, the authors propose a method of classification @IJRTER-2016, All Rights Reserved

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 03, Issue 1; January - 2017 [ISSN: 2455-1457]

for time series that combines EEG signals Backpropagation Neural Network (BPNN), which has developed well in the field of voice recognition. To check the performance (i.e., accuracy and speed of transfer), improvement of EEG classification method proposed comparative trial conducted by using Bayesian Linear Discriminant Analysis (BLDA). II. THEORIES A. Intelligence Quotient IQ stands for Intelligence Quotient, which means the size of intellectual ability, analytical, logical, and the ratio of a person. Thus, IQ is related to the speaking skills, awareness of the things around her and mastery of mathematics. IQ is a term used to describe the nature of the mind which includes some capabilities, such as the ability to reason, plan, solve problems, think abstractly, comprehend ideas, using language and learning [7][8]. Intelligence can be measured using psychometric tools are commonly referred to as IQ tests. It is a mental age of human beings based on the comparison of chronological age. Intelligence is related to cognitive abilities possessed by individuals. There are several IQ classifications, such as: - Idiot IQ (0-29). An idiot is a person behind the lowest who unable to talk or just say a few words. Usually can not take care of himself as bathing, dressing, eating and so, others should take him care. Idiots stay in bed for the rest of his life. The average growth of intelligence together with normal children two years old. Often it did not survive, because in addition to lower intelligence, also body less resistant to disease -

Imbecile IQ (30-40). Children Group imbecile level higher than the idiot child. He can learn to speak, can take care of himself with rigorous controls. In imbecile can be given light exercises, but in life always depend on others, can not be independent. His intelligence is the same as normal children aged 3 to 7 year after year. The imbecile child can not be educated in ordinary schools.

Moron or debilitated IQ / Mentally retarded (50-69) group is to a certain extent they can learn to read, write, and make simple calculations, can be given a certain routine work that does not require planning and troubleshooting. Many children debilitated received education in schools is outstanding.

Low-level IQ or mental retardation. Stupid group IQ dull/borderline (70-79) This group is located above and below the retarded group normal group (as a limit). In lengths marsh with a few obstacles, the individual can carry out the junior high school, but it is hard to be able to finish the last classes in junior high school

Low IQ Levels are still in the normal category (Dull Normal). Normal low (below average), IQ 80-89. This group includes a group of normal, average or moderate but at the lower Tertiary, they were a bit slow in learning, they can finish high school first level but somewhat difficult to be able to accomplish the tasks at senior secondary level

IQ level of normal or average. Normal being, IQ 90-109. This group is a group of normal or average, the percentage they represent the largest group in the population.

High IQ level of the normal category (Bright Normal). Normal high (above average) IQ 110-119 This group is a group of normal individuals but are at a high level.

Intelligent (superior), IQ 120-129. This group was very successful in the work of school/academic. They are often found in regular classes. Class leaders usually come from this group.

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 03, Issue 1; January - 2017 [ISSN: 2455-1457]

Genius IQ. Very clever (very superior/gifted) 130-139 IQ Kids very superior more proficient in reading, have a very good knowledge about the numbers, extensive vocabulary, and quick to understand the meaning of the abstract. In general, health factors, agility, and strength is more prominent than normal children.

More Genius IQ> 140 This group of extraordinary ability. They have the ability to solve problems and find something new even though he did not go to school. This group is in all races and nations, in all economic levels of both men and women. Examples of geniuses are Edison and Einstein.

B. Backpropagation Backpropagation has some units in one or more hidden layers [2][3]. The picture below is Backpropagation architecture with n inputs (x1, x2, x3, ....... xn) plus a bias, a hidden layer consisting of units plus a bias j, and k output unit.

Figure 1 Three layers backpropagation architecture with

The symbols used is not absolute, it could be replaced by other symbols as long as the intended logic function remains the same. In simple terms it can be said that if the output give incorrect results, then the weights so that corrected the error can be reduced and further network response is expected to be closer to the correct value. There are three phases in the settlement using backpropagation [5], such as: 1. Forward Propagation. During the forward propagation, the input signal (= xi) propagated to the hidden layer using the specified activation function. The output of each hidden layer units (= zj) which are then propagated forward again to the hidden layer on top of it using the activation function specified. Moreover, so on to produce the network output (= yk). Next, the network output (= yk) compared with the target to be achieved (= tk). Difference tk-yk is the error that occurred. If this error is smaller than the specified tolerance limits, the iteration is stopped. If the error is still greater than the tolerance limit, the weight of each line in the network will be modified to reduce errors that occur. 2. Backward Propagation. Based on the error tk-yk, calculated factor δk (k = 1, 2, ..., m) that is used to distribute the error in the unit yk to all hidden units are connected directly with yk. δk is also used to change the weight of the line connecting directly with unit output. In the same way, count δj in every unit in the hidden layer as a base weight change all the lines coming from the unit hidden layers underneath. Moreover, so on up to a factor δ in hidden units that are directly related to the input unit is calculated.

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 03, Issue 1; January - 2017 [ISSN: 2455-1457]

3. Weight Change. After all the factors Î´ is calculated, the weights of all the lines simultaneously modified. Changes in weight of a line based on factors Î´ neurons in layers above it. For example, changes in the weight of the line leading to the output layer based on the basis Î´k in unit output. It is continuously repeated until the termination condition is achieved. The termination used is the number of iterations or error. It will be terminated if the number of iterations performed already exceeded the maximum number of iterations specified, or if the error that occurs is smaller than the allowable tolerance limit. Error at the output of the network is the difference between the actual output to the desired output [6][9]. The difference is usually determined using the equation Sum Square Error (SSE). It is calculated as follows: - Calculate the neural network output to the first input. - Compute the difference between the output value of the neural network and the desired target value for each output. - Multiply each output and then count entirely. đ?&#x2018;&#x2020;đ?&#x2018;&#x2020;đ??¸ = â&#x2C6;&#x2018; â&#x2C6;&#x2018;(đ?&#x2018;¤đ?&#x2018;&#x2014;đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;Ľđ?&#x2018;&#x2014;đ?&#x2018;?)2 đ?&#x2018;?

đ?&#x2018;&#x2014;

Where : SSE = Sum Square Error wjp = value of the output of the neural network xjp = value target desired for each output Root Mean Square Error (RMS Error) is calculated as follows: - Calculate SSE. - The result is divided by multiplying the number of data on the training and the number of outputs, then rooted.

đ?&#x2018;&#x2026;đ?&#x2018;&#x20AC;đ?&#x2018;&#x2020; đ??¸đ?&#x2018;&#x;đ?&#x2018;&#x;đ?&#x2018;&#x153;đ?&#x2018;&#x; = â&#x2C6;&#x161; Where : RMS = wjp = xjp = np = no =

â&#x2C6;&#x2018;đ?&#x2018;? â&#x2C6;&#x2018;đ?&#x2018;&#x2014; (đ?&#x2018;¤đ?&#x2018;&#x2014;đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018;Ľđ?&#x2018;&#x2014;đ?&#x2018;?)2 đ?&#x2018; đ?&#x2018;? â&#x2C6;&#x2019; đ?&#x2018; đ?&#x2018;&#x153;

Root Mean Square Error value of the output of the neural network value target desired for each output number of the entire pattern number of outputs

III. PROPOSED WORK Process analysis and data collection analysis system will have problems in data collection so prepared alternative other options if the system did not achieve what was expected. The collection of data for the five samples taken randomly from men/women aged 15-30 years in a healthy condition. The collection of this data with direct observation by interviewing respondents. Data to be collected later is 20 and the data will be divided 10 for training and 10 for testing data is then 15 and 5 and 12 and 8 data. This is done to look at the accuracy of the system to recognize input patterns of data. The design of the input data has been obtained and further to do system design modeling. Artifcial Neural Network can be used to determine and recognize patterns in predicting the level of accuracy of the @IJRTER-2016, All Rights Reserved

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 03, Issue 1; January - 2017 [ISSN: 2455-1457]

Human IQ. In this case, the parameters to be used by four variables that affect the level of human concentration. These variables are cognitive, affective, psychomotor, and language as seen in Table 1. The input data is taken directly from the respondents. Table 1 Variables

Variable Remark X1 Cognitive X2 Affective X3 Pyschomotor X4 Language Data input and targets are variables which are replaced with values obtained based on the data that has been obtained from the respondents. Input can be trained so that the table data is converted in the form of a matrix A 4 x 20, B 4 x15, and C4 x10. Results of the desired output in the form of prediction of human IQ qualification level and measure the IQ level which has a higher concentration of the five types of Borderline IQ is up to Very Superior. Table 2 Aspect and Sub Aspect

Aspect Cognitive

Sub Aspect Apply the knowledge acquired

Question Item 1,2,3,4,5, 6,7,8,9,10 Affective The response is a desire to react to the material taught 1,2,3,4,5, 6,7,8,9,10 Pyschomotor Their precise movement of the limbs or by the instructions of 1,2,3,4,5, teachers 6,7,8,9,10 Language The activities are coordinated speak properly 1,2,3,4,5, 6,7,8,9

IV. EVALUATION This section is the result of the implementation of the parameters tested. Once the matrix is determined, then at this stage of the determination of output measurement IQ score is increased or decreased and the respondent who has the best rate and the highest concentration. Minimum error data specified for the prediction is if the minimum range between 0.0000 to 0.0010, it will increase the value of concentration. While if the minimum error value ranging from 0.0011 to 0.1000, it does not increase the value of concentration. Table 3 Result of 4-2-1 Model

No 1 2 3 4 5

X1 0,1004 0,1005 0,1005 0,1005 0,1006

Training Input Data X2 X3 X4 X5 0,1005 0,1000 0,5667 0,2714 0,1005 0,1000 0,6333 0,2959 0,1006 0,1000 0,7000 0,3204 0,1005 0,1000 0,6333 0,2959 0,1007 0,1000 0,7667 0,3448

Jst 4-2-1 Target Output Error 0,3213 0,3318 -0,0105 0,353 0,3419 0,0111 0,3846 0,3834 0,0012 0,353 0,3419 0,0111 0,4162 0,4294 -0,0132

International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 03, Issue 1; January - 2017 [ISSN: 2455-1457]

Table 4 Results of Data Analysis Training & Testing (Model 4.2-1)

V. CONCLUSION The level of concentration depends on the IQ owned. The higher the IQ, the higher the level of concentration owned. Backpropagation algorithm can rate the concentration degree of the person based on four main factors. These factors will be processed and produce values that will be processed by the back propagation algorithm. These results can be used as a reference for taking remedial action IQ. Comparative Study Of Backpropagation Algorithms In Neural Network Based Identification Of Power System REFERENCES [1] A. P. U. Siahaan, "Fuzzification of College Adviser Proficiency Based on Specific Knowledge," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 6, no. 7, pp. 164-168, 2016. [2] S. Tiwari, R. Naresh and R. Jha, "Comparative Study of Backpropagation Algorithms in Neural Network Based Identification of Power System," International Journal of Computer Science & Information Technology, vol. 5, no. 4, pp. 93-107, 16 3 2013. [3] A. Ganatra, Y. P. Kosta, G. Panchal and C. Gajjar, "Initial Classification Through Back Propagation in a Neural Network Following Optimization Through GA to Evaluate the Fitness of an Algorithm," International Journal of Computer Science & Information Technology, vol. 3, no. 1, pp. 98-116, 2011. [4] M. D. L. Siahaan and A. P. U. Siahaan, "Fingerprint Pattern Recoqnition Using LVQ," IOS Journal of Computer Engineering, vol. 16, no. 6, pp. 85-92, 2016. [5] Enireddy.Vamsidhar, K.V.S.R.P.Varma, P. S. Rao and R. Satapati, "Prediction of Rainfall Using Backpropagation Neural Network Model," International Journal on Computer Science and Engineering, vol. 2, no. 4, pp. 1119-1121, 2010. [6] N. A. Hamid, N. M. Nawi, R. Ghazali and M. N. M. Salleh, "Accelerating Learning Performance of Back Propagation Algorithm by Using Adaptive Gain Together with Adaptive Momentum and Adaptive Learning Rate on Classification Problems," International Journal of Software Engineering and Its Applications, vol. 5, no. 4, pp. 32-44, 2011. [7] H. Imlahi and I. Kissani, Intelligence quotient and its environmental factors in children, vol. 6, Marocco: Al Akhawayn University, 2015, pp. 697-704. [8] P. A. Tias, S. Istamar, A. Atmoko and A. D. Corebima, "The contribution of intelligence quotient (IQ) on biology academic achievement of senior high school students in Medan, Indonesia," International Journal of Educational Policy Research and Review, vol. 2, no. 10, pp. 141-147, 2015. [9] A. Gupta and M. Shreevastava, "Medical Diagnosis using Backpropagation Algorithm," International Journal of Emerging Technology and Advanced Engineering, vol. 1, no. 1, pp. 55-58, 2011.