University-Level of eLearning in ASEAN


Information System Databases for Neuropsychology Tests: Case Study in Boston Naming Test 

SISFORMA: Journal of Information Systems (e-Journal) Vol. 6   | No. 1 |Th.2019 page 28 

ISSN 2442-7888 (online) DOI 10.24167/Sisforma 

Information System Databases for Neuropsychology Tests: Case 

Study in Boston Naming Test 

Shinta Estri Wahyuningrum1, Augustina Sulastri2, Ridwan Sanjaya3 

Faculty of Computer Science, Soegijapranata Catholic University Indonesia1,3, Faculty of 

Psychology, Soegijapranata Catholic University Indonesia2 

shinta@unika.ac,id1, ag.sulastri@unika.ac.id2, ridwan@unika.ac.id3 

 

 
Abstract— In the field of psychology, 

determining the psychological condition of 

a person’s can be done using various types 

of tests. Neuropsychology test is a battery 

test that means every person should be 

taken 11 test in a moment. Each test has a 

different objective, as an example, The 

Boston Naming test is used to measure a 

person's ability in the language domain. 

The data stored for each data in the Boston 

Naming Test (BNT) is around 130 fields. 

Each test has different specific data. This 

makes the data grow rapidly and requires 

a database design that can accommodate 

this need. 

There are many approaches can be done to 

store the database such a relational 

database and NoSQL database. When the 

data are stored using relational methods 

and amount of data are large, there can be 

a lack of time in both processing and 

tracking. This article proposes a system to 

store the result of the neuropsychological 

test using the NoSQL database approach 

with sample data in subtest BNT. 

Keywords— Database, NoSQL, 

Neuropsychology, Statistical, BNT 

 

 I. INTRODUCTION  

Nowadays there are many technologies that 

can be used in solving problems in various 

fields. One of them is web base application. 

Web based applications that are widely 

developed today can be used to process large 

amounts of data. When the applications are 

meant to be used only to handle certain 

amounts of data, a relational model database 

design is feasible to be used, but when the 

data to be processed is very large, complex 

and requires a dynamic schema change, the 

database that should be used is NoSQL. 

Applications like e- commerce, CMS and 

business applications are more optimal when 

developed with database schemes using 

NoSQL [1][3]. Other examples of 

applications using complex and a lot of data 

are student academic application and library 

application. It is complex because besides 

being used to store data, student and libraries 

applications are also accessed by many users. 

However, using NoSQL the database 

response time on a web application only takes 

less than one minute [2]. In Addition, NoSQL 

database also supports large-scale statistical 

analysis. In addition, NoSQL is used in EI 

(Em- bedded Intelligent) to process data 

generated from sensor readings [5].   

Neuropsychology is a field of psychology 

that focuses on neuro-human problems. For 

Neuropsychological it is used name to 

determined the brain disorder. There are tree 

domain like language, attention, executive 

function, and memory. Each domain consists 

of one or more test. like a Boston Naming 

Test, Token Test and Verbal Fluency Test are 

in Language Domain. There is a benchmark 

used to determine a person’s normal condition 

i.e. a normative score obtained from the mean 

and standard deviation of healthy people [9].  

The computerized system for administering 

neuropsychological test data is currently 

being developed only in Nether- lands [10]. In 

Indonesia, a system is being developed to able 

to accommodate the needs of processing and 

storing data resulted from neuropsychological 

tests. Some constraints are encountered 

during the data processing. These constraints 

are: 1) various different tests taken by each 

individual. 2) All test results from each 

individual are documented in the database. 3) 

Each test has a variety of fields, even in a test 

mailto:shinta@unika.ac,id
mailto:ag.sulastri@unika.ac.id2


Information System Databases for Neuropsychology Tests: Case Study in Boston Naming Test 

SISFORMA: Journal of Information Systems (e-Journal) Vol. 6   | No. 1 |Th.2019 page 29 

ISSN 2442-7888 (online) DOI 10.24167/Sisforma 

more than 30 fields must be saved. 4) 

Flexibility in adding types of tests to the 

database. 5) Flexibility of generating 

normative score.  

NoSQL database has a less nature schema. 

This allows the user to add fields to the 

database without changing the structure. 

MongoDB supports data storing using the 

Document database method that uses the 

concept of storing all data in an object 

databases[1]. Saved database documents can 

use XML, JSON, BSON document. In 

addition, MongoDB also supports data model 

flexibility and elastic scaling when they are 

developed into a distributed database. By 

using NoSQL the data storing in the database 

is more flexible. We can use schema-oriented, 

semi-structured and schemeless data [7]. 

 II. METHOD 

Propose system consists of two parts: to 

generating normative scores based on 

dynamic datasets and to determining patient 

profile. 

 

 
  

Figure 1. Center of data 

 

A. Generating normative scores based on 
dynamic datasets. 

 

Figure 1, explain how to manage the center 

of data. The server will receive the data from 

all different clients. Before the data transfer to 

the data center, system synchronized the raw 

data. The process is needed to cleaning the 

data, or to normalize the data. Normalize 

intend to make the data valid. 

The data center contains data on 

neuropsychology results. Each participant 

gets a score for 11 subtests. The data of 

participant and score data for each sub-test 

will be recorded. All of data will be stored in 

the data center. 

Data center is built to process all data into 

normative score information. This normative 

score will be standard for neuropsychologists 

to determine a person's performance. In 

addition to the data center is needed to 

facilitate the updating of data. Updating the 

data will used to get the updated of the 

normative score calculation. Normative data 

is obtained by clustering data. Normative 

scores are calculated for each sub-test. 

Normative data is calculated based on the 

mean and standard deviation of each sub-test. 

Normative scores are obtained by filtering 

based on several criteria from the data of 

participants who did the test [9]. Filtering is 

based on several things such as age, gender, 

city data collection, and education. In Figure 

1, the results of data filtering are shown in the 

dataset image.  

 

B. Determining patient profile 
 

 For a neuropsychology, after obtaining a 

normative score, they can do a comparison of 

the results of a person's score with the 

normative score. The system built makes it 

easy to access data.  

Patient analysis based on normative score. 

The output of the neuropsychology test series 

is to produce patients’ profiles [10]. This 

output is obtained by comparing individual 

scores with a normative score that matches the 

patient’s profile.  

The neuropsychologist will input patient 

score results into the system show in figure 2. 

Then based on patient demographic data that 

is detected automatically by the system, it will 

be compared with the results of the 

corresponding normative score dataset. The 

system will only resulted at providing data 

results that can be a support for experts in 

determining decisions. Because the ones that 



Information System Databases for Neuropsychology Tests: Case Study in Boston Naming Test 

SISFORMA: Journal of Information Systems (e-Journal) Vol. 6   | No. 1 |Th.2019 page 30 

ISSN 2442-7888 (online) DOI 10.24167/Sisforma   

determine patient profiles are the experts, so 

the system will involve users, in this case 

neuropsychology experts, to determine the 

profile of the patient. If the neuropsychologist 

determine based on the patient’s data and 

conclude that the patient is healthy, then the 

neuropsychology can submit the data to be 

inputted to the datacenter. The increase of 

data in the datacenter will be updating the 

normative score.  

 
Figure 2. Access to the database 

 

C. Database Design 
 

Figure 3, shows the database design. Each 

test consist of a variety of different data fields, 

each test will be stored in a table with user id 

as a foreign key. Each field generated by the 

test should be stored in the database, to 

facilitate the data search process. Using the 

relational database design.  There are 27 

tables in the database design. Each table 

contains different field. The database is 

design using relational models.  

 

 
 

Figure 3. Table design  

 

 III. RESULTS AND DISCUSSION 

 

The subtest used as sample data is the Boston 

Naming Test. Amount of BNT data as sample 

is 407 data. The database model using 407 

data. Models of databases that use are 

MySQL model and NoSQL model. 

 

A. Database model with MySQL 
 

The structure table consist of two table: 

Demographic table and BNT score table. 

Demographic tables contain detailed 

information from individuals including 

patient names, data of birth, place of 

residence, and history of illness. while the 

BNT score table contains the score data for 

each question in the BNT test.  

The score table consists of about 300 fields 

for each rows. The contain od this table is data 

score for each question from BNT. Each 

question has 5 fields of score. In BNT there 

are 60 questions, when added with total of 

each fields, there will be approximately 310 

fields for one rows data. 

Figure 4 are the implementation of 

MySQL model from BNT data. There are 2 

table. The LanguageBNT table has long data 

fields. 

 



Information System Databases for Neuropsychology Tests: Case Study in Boston Naming Test 

SISFORMA: Journal of Information Systems (e-Journal) Vol. 6   | No. 1 |Th.2019 page 31 

ISSN 2442-7888 (online) DOI 10.24167/Sisforma 

….…  

 
Figure 4. Database using MySQL 

 

B. Convert from MySQL to MongoDB 
 

The method of migrating data from 

MySQL to the MongoDB scheme [8] can be 

done in 2 ways. As can be seen in table 1, that 

table from MySQL will be converted to a 

collection in MongoDB. The first is to convert 

2 tables from MySQL into 1 collection in 

MongoDB. In this way, BNT score data will 

be embedd to participant demographic data. 

Whereas the second way each table is convert 

to two tables in the colecction MongoDB. The 

number of table is same as in the table design 

with the relational method.  

 
Table 1. Database Schema 

 
 

Conversion from two tables in MySQL to one 

table in NoSQL is shown in figure 5. While 

the databases conversion from one table in 

MySQL to one table in NoSQL can be seen in 

figure 6. 

 
Figure 5. Convert to Embedded Collection 

 

 
 

Figure 6. Convert to Single Collection 

 

C. Collection pada MongoDB 
 

After the data migration process, the data 

result can be seen in the figure 7. Figure 7 is 

the sample of embedded collection.  

 

 
Figure 7. Embedded collection 

 



Information System Databases for Neuropsychology Tests: Case Study in Boston Naming Test 

SISFORMA: Journal of Information Systems (e-Journal) Vol. 6   | No. 1 |Th.2019 page 32 

ISSN 2442-7888 (online) DOI 10.24167/Sisforma   

In the embedded collection, scores of 

individuals are stored in an object. In 

implementation using NoSQL it is possible to 

save different data number of fields in one 

collection. 

 
Figure 8. Single collection 

 

Figure 8, is a display of data stored in a 

single database. 

 

The contain of table 2 is an example of a 

Query in the MongoDB database:  

 
Table 2. Query in BNT NoSQL 

Single 

collect

ion 

andiindonesia.person.find({}) 

andiindonesia.LanguageBNT.

find({}) 

Embedded 

Collection 

andiindonesia.BNT.find({}) 

$match{ 

      kotaambildata: “Jakarta” } 

$group{  

      _id: "$kotaambildata", 

      total : {  $sum :        

      "$dataskor.totalskor" }} 

$group{  

    _id: "$kotaambildata", 

    total : {  $avg :  

   "$dataskor.totalskor" }} 

$group{ 

  _id: "$kotaambildata", 

  stddev: { 

    $stdDevPop: 

"$dataskor.totalskor" 

  }} 

Table 2, is an example of query syntax 

performed on the MongoDB database. The 

example of query use to calculate the mean 

and stddev values. From the query command 

structure, it appears that the command used to 

do the query is simpler. 

In the implementation using a single 

collection, the query process that involves two 

tables can be done using the index based refer 

method. Indexes are automatically obtained 

when adding new data in MongoDB. The 

index can be seen in figure 8 in the red text. 

In addition to the ease of querying, adding 

new data to the collection is more flexible, 

because this will not change the structure of 

the collection. When new fields are added to 

the database, the addition of data and new 

fields can be done immediately when 

inserting new data. The addition of these 

fields takes effect on newly entered data, 

while the old data does not need to be changed 

and is not affected. This is rather difficult to 

do when using a relational database. 

 IV. CONCLUSION 

Using NoSQL database design makes the 

database flexible in storing different data for 

each individual. NoSQL can also be used with 

a large amount of data. This means the 

document storage system will make it faster 

and easier when tracking test data from each 

individual. In addition, if later in the 

development of the field of neuropsychology 

a new type of assessment created, it can be 

easily added. The advantages of designing 

NoSQL in BNT system are ease, speed and 

flexibility in determining datasets to get 

normative score.  

 

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Information System Databases for Neuropsychology Tests: Case Study in Boston Naming Test 

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ISSN 2442-7888 (online) DOI 10.24167/Sisforma 

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