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Vol.2 No.2 July 2021 

Buana Information Tchnology and Computer Sciences (BIT and CS) 

 

31 | Vol.2 No.2, July 2021 

 

Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS)  

In Predicting New Students 

(Case Study : UBP Karawang) 

Tatang Rohana 1  

Study Program 

Technical Information 

Faculty Of Engineering And Computer Science, Buana 

Perjuangan University 

Email: tatang.rphana@ubpkarawang.ac.id 

Bayu Priyatna 2 

Study Program 

Information System 

Faculty Of Engineering And Computer Science, Buana 

Perjuangan University 

Email: bayu.priyatna@gmail.com 

 

 

 ‹β› 
Abstract—The process of admitting new students is an annual 

routine activity that occurs in a university. This activity is the 

starting point of the process of searching for prospective new 

students who meet the criteria expected by the college. One of the 

colleges that holds new student admissions every year is Buana 

Perjuangan University, Karawang. There have been several studies 

that have been conducted on predictions of new students by other 

researchers, but the results have not been very satisfying, 

especially problems with the level of accuracy and error. Research 

on ANFIS studies to predict new students as a solution to the 

problem of accuracy. This study uses two ANFIS models, namely 

Backpropagation and Hybrid techniques. The application of the 

Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the 

predictions of new students at Buana Perjuangan University, 

Karawang was successful. Based on the results of training, the 

Backpropagation technique has an error rate of 0.0394 and the 

Hybrid technique has an error rate of 0.0662. Based on the 

predictive accuracy value that has been done, the Backpropagation 

technique has an accuracy of 4.8 for the value of Mean Absolute 

Deviation (MAD) and 0.156364623 for the value of Mean 

Absolute Percentage Error (MAPE). Meanwhile, based on the 

Mean Absolute Deviation (MAD) value, the Backpropagation 

technique has a value of 0.5 and 0.09516671 for the Mean 

Absolute Percentage Error (MAPE) value. So it can be concluded 

that the Hybrid technique has a better level of accuracy than the 

Backpropation technique in predicting the number of new students 

at the University of Buana Perjuangan Karawang 

Keywords— ANFIS, Backpropagation, Hybrid, Prediction 

Abstract—Proses penerimaan mahasiswa baru merupakan 

kegiatan rutin tahunan yang terjadi di sebuah universitas. 

Kegiatan ini merupakan titik awal dari proses pencarian calon 

mahasiswa baru yang memenuhi kriteria yang diharapkan oleh 

perguruan tinggi. Salah satu perguruan tinggi yang 

menyelenggarakan penerimaan mahasiswa baru setiap tahunnya 

adalah Universitas Buana Perjuangan Karawang. Ada beberapa 

penelitian yang telah dilakukan terhadap prediksi mahasiswa baru 

oleh peneliti lain, namun hasilnya belum terlalu memuaskan, 

terutama masalah tingkat akurasi dan kesalahan. Penelitian 

tentang studi ANFIS untuk memprediksi siswa baru sebagai solusi 

dari masalah akurasi. Penelitian ini menggunakan dua model 

ANFIS, yaitu teknik Backpropagation dan Hybrid. Penerapan 

model Adaptive Neuro-Fuzzy Inference System (ANFIS) pada 

prediksi mahasiswa baru Universitas Buana Perjuangan 

Karawang berhasil. Berdasarkan hasil pelatihan, teknik 

Backpropagation memiliki tingkat kesalahan 0,0394 dan teknik 

Hybrid memiliki tingkat kesalahan 0,0662. Berdasarkan nilai 

akurasi prediksi yang telah dilakukan, teknik Backpropagation 

memiliki akurasi sebesar 4,8 untuk nilai Mean Absolute Deviation 

(MAD) dan 0,156364623 untuk nilai Mean Absolute Percentage 

Error (MAPE). Sedangkan berdasarkan nilai Mean Absolute 

Deviation (MAD), teknik Backpropagation memiliki nilai 0,5 dan 

0,09516671 untuk nilai Mean Absolute Percentage Error (MAPE). 

Sehingga dapat disimpulkan bahwa teknik Hybrid memiliki tingkat 

akurasi yang lebih baik dibandingkan dengan teknik 

Backpropation dalam memprediksi jumlah mahasiswa baru di 

Universitas Buana Perjuangan Karawang. 

Kata Kunci— ANFIS, Backpropagation, Hybrid, Prediksi 

 

I. INTRODUCTION 

The process of admitting new students is an annual routine 
activity that occurs in a university. This activity is the 
starting point of the process of searching for prospective 
new students who meet the criteria expected by the college. 
One of the colleges that hold new student admissions every 
year is Buana Perjuangan University Karawang. 

Buana Perjuangan Karawang University is one of the 
universities in the Karawang area which is developing very 
rapidly. This is proven by the high interest of new students 
who register and can be accepted at Buana Perjuangan 
University, Karawang. This is of course a challenge and a 
good opportunity for the university. On the other hand, the 
stability and availability of campus facilities and 
infrastructure are things that need to be considered by 
university administrators. The university certainly has to be 
able to calculate how many new students are accepted at 
Buana Perjuangan University, this is important for the 
organizers as a material for decision making, especially 
those related to campus development, infrastructure, and 
resources that support the teaching and learning process in 
the campus environment. 

In this study, the authors used the ANFIS model to predict 
the number of new students at the University of Buana 
Perjuangan Karawang. Many studies have been conducted, 
using the Adaptive Neuro-Fuzzy Inference System (ANFIS) 
model in the prediction system. Among them, the use of 
Artificial Neuro Fuzzy Inference System (ANFIS) in 
Determining the Status of Mount Merapi Activities [5]; The 
Use of Backpropagation Neural Networks for New Student 



 

32 | Vol.2 No.2, July 2021 

 

Admissions in the Computer Engineering Department at 
Sriwijaya State Polytechnic [1]; Development of Artificial 
Neural Network Model to Predict the Number of New 
Students in PTS Surabaya [2]; Adaptive Neuro Fuzzy 
Inference System (ANFIS) method for prediction of road 
service levels [11], and other studies. It is expected that the 
results of this study can provide good accuracy and error 
rates. In this study, the authors raised the title "Adaptive 
Neuro-Fuzzy Inference System (ANFIS) Study in Predicting 
New Student Admissions at Buana Perjuangan University, 
Karawang. 

II.  METHOD 

A. Types of research 

In this research, the type of research used is quantitative. 

The objective of quantitative research is to develop and use 

mathematical models, theories and hypotheses related to 

natural phenomena. Quantitative research is a type of 

research that basically uses a deductive-inductive approach. 

This approach departs from a theoretical framework, the 

ideas of experts, as well as the understanding of researchers 

based on their experience, then it is developed into problems 

and their solutions that are proposed to obtain justification 

(verification) or an assessment in the form of support for 

empirical data in the field. 

B. Data Collection 

Data is a unit of information recorded by media that can be 

distinguished from other data, can be analyzed and is 

relevant to certain programs. Data collection is a systematic 

and standard procedure for obtaining the necessary data. To 

collect research data, the authors used the interview method. 

To obtain data sources, the authors conducted interviews 

with the New Student Admissions Committee, which were 

then validated with the Data Center (Pusdatin) of Buana 

Perjuangan University. 

 

C. Data Analysis 

The data analysis technique used is to divide the data into 

two, namely training data and testing data. Model training 

uses training data, while model testing uses testing data. The 

results of the model trial conclusion will be verified by 

diagnosis on the testing data. Data analysis in this study 

aims to determine how accurate the Adaptive Neuro Fuzzy 

Inferences System (ANFIS) model at Buana Perjuangan 

University, Karawang. In predicting the number of students 

using the singular value decomposition method. The stages 

of ANFIS data analysis can be seen in the following figure. 

 

 

Fig. 1. ANFIS Analysis and Prediction Process  

The data used in this research is secondary data, namely 

data on enrollments from new students obtained from the 

new student admissions committee, and active students for 

each study program obtained from the Center for Data and 

Information (PUSDATIN), University of Buana Perjuangan 

Karawang. 

D. Framework 

The problem of admitting new students at a university is a 

routine problem that occurs every new academic year. So it 

needs good handlers in its implementation. Information on 

the number of new students is important data for all campus 

members. Both leadership, student affairs, academics, 

infrastructure and others. This is important because the 

campus must prepare everything due to the teaching and 

learning process. The prediction system for new students is 

certainly very helpful for the campus in preparing for needs 

- a need that must be met by all members of the community 

in a university. The framework of this prediction system 

research includes: 

1. The number of new students from the 2015/2016 

academic year to 2019/2020. 

2. The data is obtained from the New Student Admissions 

Committee and is also equipped from the UBP Data 

Center. 

3. Data preprocessing (initial processing), data cleaning 

to eliminate data errors and data transformation.  

4. The process of training and testing data 

5. Make predictions for new students 

6. Evaluate the level of accuracy with the MAD and 

MAPE models 



 

33 | Vol.2 No.2, July 2021 

 

 

Fig. 2. Framework 

E. Research subject 

The data source used in this study is data obtained from the 

Buana Perjuangan Karawang University New Student 

Admissions Committee which is then validated with the 

Pusdatin Section. New student data used as the source of 

data in this study were taken from new students from the 

2015/2016 academic year to the 2019/2020 academic year. 

The new student data can be seen in detail in table 1. 

 
TABLE 1 NEW STUDENTS OF UBP KARAWANG 2015 TO 2019 

 

Year PKn PGSD Law Psik Ak Man SI IF Far TI 

2015/2026 52 152 146 145 155 240 67 187 97 198 

2016/2017 54 165 131 150 179 356 56 178 109 243 

2017/2018 61 154 142 164 225 536 77 207 143 352 

2018/2019 40 139 156 162 190 490 75 231 140 356 

2019/2020 42 148 176 250 208 531 74 209 142 324 

Source : Pusdatin 

 

The data will then be processed with a preprocessing 

process (pre-process) by means of data cleaning and 

normalization, before being used as data analysis in this 

study. The preprocessing results will then be processed 

using the Adaptive Neuro Fuzzy Inference System (ANFIS) 

method as a method for carrying out the prediction process. 

III. RESULTS AND DISCUSSION 

A. Preprocessing Data 

The first step taken in this research data, is to pre-process 

the data on the number of new students accepted at the 

University of Buana Perjuangan for the period of the 

2015/2016 academic year to 2019/2020. This needs to be 

done because the data used in the process is not always ideal 

for processing. Sometimes in this data there are various 

problems that can interfere with the results of the process 

itself, such as missing values, redundant data, outliers, or 

data formats that are not in accordance with the system. The 

pre-processing of this data includes several stages, including 

data cleaning and data normalization.  

1) Data Cleaning 

Data cleaning is performed to eliminate inefficient and 

error-containing data. In this process, data is cleaned using 

the Rapidminer application. 

 

Fig.3. data cleaning process 

 

Fig.4. Results of Data Cleaning 

From the data cleaning process above, it shows that there 

are no errors or missing in the data used in this study. 

2) Data Normalization 

Before input data is entered into the network, the data is 

transformed into interval data (normalization). These data 

are normalized so that they are in the range [0,1]. 

Normalization uses the Min - Max formula (Han, Kamber, 

and Pei, 2012). 

 

     (1) 

X  = data input 

Xmin = data X minimum 

Xmax = data X maksimum 

Bmax = upper limit of the interval 

Bmin = lower limit of the interval 

The purpose of normalization is to equalize the range of 
values for each data so that each data has a proportional role 
in each process. To facilitate the conversion process, the 
name of the Study Program is changed to the X variable. 
Namely X1, X2, .... X10. 

TABLE 2 DATA ON THE NUMBER OF NEW STUDENTS 

Year X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 

2015/2026 52 152 146 145 155 240 67 187 97 198 

2016/2017 54 165 131 150 179 356 56 178 109 243 

2017/2018 61 154 142 164 225 536 77 207 143 352 

2018/2019 40 139 156 162 190 490 75 231 140 356 

2019/2020 42 148 176 250 208 531 74 209 142 324 

Become interval data [0, 1] 

 

TABLE 3 CONVERT DATA TO MIN - MAX 

r X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 

1 0,572 0,5 0,333 0 0 0 0,523 0,169 0 0 

2 0,666 1 0 0,047 0,24 0,391 0 0 0,26 0,284 

3 1 0,576 0,244 0,18 1 1 1 0,547 1 0,974 

4 0 0 0,555 0,161 0,5 0,844 0,904 1 0,934 1 

5 0,095 0,346 1 1 0,757 0,983 0,857 0,584 0,978 0,797 

 



 

34 | Vol.2 No.2, July 2021 

 

B. Hypothesis Test 
3) Data Training Process 
Data that has been normalized in the form of Min - Max, 
then used as a source of data for the training process 
(training) and testing (test data) in the ANFIS analysis 
process. For the ANFIS analysis process, the data is divided 
into two parts, namely training data and testing data. New 
student data from 2015 to 2018 is used as training data, 
while new student data for 2019 is used as testing data. 

TABLE 4 DATA TRAINING 

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 

0,572 0,5 0,333 0 0 0 0,523 0,169 0 0 

0,666 1 0 0,047 0,24 0,391 0 0 0,26 0,284 

1 0,576 0,244 0,18 1 1 1 0,547 1 0,974 

0 0 0,555 0,161 0,5 0,844 0,904 1 0,934 1 

TABLE 5 DATA TESTING 

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 

0,095 0,346 1 1 0,757 0,983 0,857 0,584 0,978 0,797 

The training process (training) with the Adaptive Neuro-

Fuzzy Inference System (ANFIS) model was carried out 

using the Matlab R2010 tool. Data analysis for this 

prediction uses two Adaptive Neuro-Fuzzy Inference 

System (ANFIS) models, namely Backpropagation and 

Hybrid techniques. So that the training and testing process is 

also based on these two techniques 

4) Backpropagation Technique Data Training 

The data in table 4 shows that new student data is used as 

training data. The training process consists of 20 epochs 

(iterations) with an error tolerance of 0.001.  

From the results of data training with the Backpropagation 

technique, the error rate obtained is 0.0394. 

 

 

Fig.5. Backpropagation Technique Training Process 

From the Backpropagation technique training above, the 

resulting error rate is 0.0394 with an error tolerance of 0.001 

with 20 iterations. From this error value, the rate of increase 

and decrease in student predictions using the 

Backpropagation technique is 3.94%. 

5) Hybrid Technique Data Training 

In the same way, subsequent training is carried out using 

Hybrid techniques. The training process was carried out 20 

times with an error tolerance of 0.001. The training process 

can be seen in the following image: 

 

Fig. 6. Hybrid Technique Training Process 

Based on the results of training with the Hybrid technique, 

an error rate of 0.0662 was generated. With this error value, 

the predicted value of increase and decrease in new students 

with the Hybrid technique is 6.62%. 

6) Data Testing Process 

The next process is the testing process or data testing. This 

test is an application of the results of training data to 

predictions of new students. The data used in this process is 

the number of new students in 2019, the data can be seen in 

the following table: 

TABLE 6 DATA TESTING 

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 

0,095 0,346 1 1 0,757 0,983 0,857 0,584 0,978 0,797 

42 148 176 250 208 531 74 209 142 324 

From the training process above, the predictive value 

obtained with the Backpropagation technique is 0.0394 

while the Hybrid technique is 0.0662. 

7) Backpropagation Data Testing 

The test results with the parameters obtained from the data 

training process with an error rate of 0.0394 for the 

Backpropagation technique, then the prediction results and 

errors of new students for 2019 are obtained. 

TABLE 7 BACKPROPAGATION TECHNIQUE PREDICTION AND 

ERROR RESULT 

 

No Faculty Data Aktual 
Prediction 

Error 
(Backpropagation) 

1 PKn 42 42 0 

2 PGSD 148 144 4 

3 Law 176 162 14 

4 Psik 250 168 82 

5 Ak 208 197 11 

6 Man 531 509 22 

7 SI 74 78 4 

8 IF 209 240 31 

9 Far  142 146 4 

10 TI 324 370 46 

 
 

Fig 7. Prediction Of Backpropagation Technique 

 

 



 

35 | Vol.2 No.2, July 2021 

 

8) Hybrid Data Testing 

Meanwhile, from the data training process with an error rate 

of 0.0662 for the Hybrid technique, the results of predictions 

and errors for new students for 2019 are as follows: 

TABLE 8 HYBRID TECHNIQUE PREDICTION AND ERROR 

RESULTS 

No Faculty Data Aktual 
Prediction 

Error 
(Hybrid) 

1 PKn 42 43 1 

2 PGSD 148 148 0 

3 Law 176 166 10 

4 Psik 250 173 77 

5 Ak 208 203 5 

6 Man 531 522 9 

7 SI 74 80 6 

8 IF 209 246 37 

9 Far 142 149 7 

10 TI 324 379 55 

 

Fig 8. Prediction Of Hybrid Techniques 

C. Analysis and Discussion 

The model used in this study is the Adaptive Neuro Fuzzy 

Inference System (ANFIS). Meanwhile, the techniques used 

in the Fuzzy Inference System (FIS) are Backpropagation 

and Hybrid Techniques. To measure the level of accuracy of 

the two techniques, the error rate of each technique must be 

sought. (Hanke & Wichern, 2005) said that forecasting 

techniques that use quantitative data often contain data in 

the form of a certain time series. Which is where there are 

errors / errors made by forecasting techniques. Therefore a 

method is needed to measure how much error / error can be 

generated by forecasting methods to be reconsidered before 

making a decision. There are also uses of this method of 

measuring error forecasting are: 

▪ Comparing the accuracy of the 2 (or more) 

forecasting methods used. 

▪ Measuring the reliability and benefits of the 

forecasting method used. 

▪ Finding the optimal forecasting method for the 

organization or company. 

To measure the level of accuracy of the Backpropagation 

and Hybrid techniques, the Mean absolute deviation (MAD) 

and Mean absolute percentage error (MAPE) methods are 

used. A good level of accuracy is if the error rate is smaller 

than the others. 

9) Mean Absolute Deviation (MAD) 

Mean absolute deviation measures the accuracy of the 

prediction (forecast) by making an equal of the magnitude 

of the forecast error, where each prediction has an absolute 

value for each error. 

The formula used to calculate MAD is: 

 

    (2) 

Information : 

Y = actual value in period t  

Y ̈t    = forecast value in period t 

N      = number of data periods 

From the measurement results of the accuracy level of the 

Backpropagation and Hybrid techniques that have been 

carried out using the Mean Absolute Deviation (MAD) 

method, the following values are obtained: 

TABLE 9 MEAN ABSOLUTE DEVIATION  HYBRID 

Aktual Hybrid Y1-ŷt 

42 42 -1 

148 144 0 

176 162 10 
250 168 77 

208 197 5 

531 509 9 
74 78 -6 

209 240 -37 

142 146 -7 
324 370 -55 

 Total Absolute Deviation 5 

 MAD 0,5 

TABLE 10 MEAN ABSOLUTE DEVIATION (MAD) 

BACKPROPAGATION 

Aktual Backpropagation Y1-ŷt 

42 43 0 
148 148 4 

176 166 14 

250 173 82 
208 203 11 

531 522 22 

74 80 -4 

209 246 -31 

142 149 -4 

324 379 -46 
 Jumlah Deviasi Absolut 48 

 MAD 4,8 

From the table above, backpropagation has a Mean Absolute 

Deviation (MAD) value obtained of 4.8, and for the total 

deviation of 48. As for the Hybrid technique, the Mean 

Absolute Deviation (MAD) value obtained is 0.5 and the 

number of absolute deviations is 5. 

10) Mean Absolute Percentage Error (MAPE) 

The mean absolute percentage error is calculated by finding 

the error / absolute error in each period, which is divided by 

the actual observed value for that period, and an average of 

the absolute percentage errors is made. 

The formula used to calculate MAPE is: 
 

     (3) 

Information : 

n  = the number of data periods 

Yt  = actual value in period t 

Yt  = the forecast value in period t 

Based on the results of the calculation of Mean absolute 

percentage error (MAPE) for the Backpropagation 

technique, the error value is 1.56365 with a MAPE value of 

0.1563647. With a prediction error value of 15.6%. 



 

36 | Vol.2 No.2, July 2021 

 

TABLE 11 MEAN ABSOLUTE PERCENTAGE ERROR 

BACKPROPAGATION 

Aktual Backpropagation Y1-ŷt 

42 42 0 
148 144 0,02703 

176 162 0,07955 

250 168 0,328 
208 197 0,05288 

531 509 0,04143 

74 78 -0,05405 
209 240 -0,14833 

142 146 -0,02817 

324 370 -0,14198 
 Jumlah Deviasi Absolut 1,56363 

 MAPE 0 

 

As for the Hybrid technique, the error value is 0.95167162 

with a MAPE value of 0.09516671. With this, the prediction 

error value with the hybrid technique is 9.52%. 

 
TABLE 12 MEAN ABSOLUTE PERCENTAGE ERROR HYBRID 

 Aktual Hybrid Y1-ŷt 

42 43 -0,023809 

148 148 0 
176 166 0,056818 

250 173 0,308 

208 203 0,024038 
531 522 0,016949 

74 80 -0,081081 

209 246 -0,177033 
142 149 -0,0492957 

324 379 -0,169753 

 Jumlah Deviasi Absolut 0,95167162 

 MAPE 0,09516671 

 

With the results of this test, it can be concluded that the 

predictions of new students at the University of Buana 

Perjuangan Karawang with the Adaptive Neuro-Fuzzy 

Inference System (ANFIS) model can be used properly. 

This is evidenced by the results of training (training) 

Backpropagation technique has an error rate (Error rate) of 

0.0394, while the Hybrid technique has an error rate of 

0.0662. Then based on the calculation of the value of 

accuracy in predicting, the Backpropagation technique has 

an accuracy of 4.8 for the value of Mean Absolute Deviation 

(MAD) and 0.156364623 for the value of Mean Absolute 

Percentage Error (MAPE). Meanwhile, based on the Mean 

Absolute Deviation (MAD) value, the Backpropagation 

technique has a value of 0.5 and 0.09516671 for the Mean 

Absolute Percentage Error (MAPE) value. When compared 

to the accuracy, the Hybrid technique is more accurate than 

the Backpropagation technique, because it has a smaller 

error accuracy value. 

III. CONCLUSION 

From the results of research and testing that have been 

carried out on the Study of Adaptive Neuro-Fuzzy Inference 

System (ANFIS) in Predicting New Students at Buana 

Perjuangan University, Karawang, it can be concluded as 

follows: 

1. The application of the Adaptive Neuro-Fuzzy Inference 

System (ANFIS) model in the predictions of new 

students at Buana Perjuangan University, Karawang is 

successful. Based on the results of training, the 

Backpropagation technique has an error rate of 0.0394 

and the Hybrid technique has an error rate of 0.0662. 

2. Based on the predictive accuracy value that has been 

done, the Backpropagation technique has an accuracy of 

4.8 for the value of Mean Absolute Deviation (MAD) 

and 0.156364623 for the value of Mean Absolute 

Percentage Error (MAPE). Meanwhile, based on the 

Mean Absolute Deviation (MAD) value, the 

Backpropagation technique has a value of 0.5 and 

0.09516671 for the Mean Absolute Percentage Error 

(MAPE) value. 

3. Based on the accuracy results, the Hybrid technique is 

more accurate than the Backpropagation technique, 

because it has a smaller predictive error accuracy value 

based on the Mean Absolute Deviation (MAD) and 

Mean Absolute Percentage Error (MAPE) values. 

ACKNOWLEDGMENT 

Finally, the authors would like to thank all those who have 
helped and provided criticism and suggestions so that this 
research can be completed on time. 

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