Explanation of **Table 11**:

G = Glutinousness. (+) means it is more sticky or contains water. () means it is slightly watery or sticky.

L = Length. (+) means more oblong. (=) means more rounded.

A = Aroma. (+) means more fragrant. () means less fragrant.

R = Taste. (+) means sweeter. () means more acidic.

= means having these criteria within normal limits.


#### **Table 10.**

*Brief description of rice criteria.*


#### **Table 11.**

*Processed compliance tables with types, criteria, and brand of rice.*

*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health… DOI: http://dx.doi.org/10.5772/intechopen.88180*

## **4. Results**

**Table 10** shows the criteria for rice used:

*Ontological Analyses in Science,Technology and Informatics*

**Number Criteria Description** 1 Glutinousness Rice stickiness level

Explanation of **Table 11**:

slightly watery or sticky.

*Brief description of rice criteria.*

4 Black sticky rice porridge

Appendix.

**Table 10.**

**Table 11.**

**56**

The following is a table of matches between the types, criteria, and brands of

G = Glutinousness. (+) means it is more sticky or contains water. () means it is

4 Length The length and shape of rice (somewhat oval or rather round)

**Number Processed Types of rice Criteria Brand of rice**

2 Bakcang White rice, glutinous rice + √ Myrice, Penguin 3 Porridge White rice + √ Flower Stamp

6 Egg-crust — — Swallow

9 Rice (All) √√√√ (All)

brown rice

10 Roasted rice White rice, parboiled rice,

*Processed compliance tables with types, criteria, and brand of rice.*

5 Gyudon Mixed rice, Japan + √ Louhan, Goldrice Green

7 Lemper White glutinous rice + — Swallow, King rice,

8 Lontong/ketupat White rice + √ Myrice, King rice

11 Fried rice White rice, red rice — √ Panda, BMW, Penguin 12 Gudeg rice White rice + √ Three Guava, Capit 13 Corn rice Basmati rice + + √ Three Guava, Capit 14 Yellow rice White rice — — √ Panda, BMW, Penguin 15 Liwet rice White rice + √ Three Guava, Capit 16 Team rice White rice + √ Three Guava, Capit 17 Uduk rice White rice — √ Panda, BMW, Penguin 18 Sushi Japanese rice, mixed rice + √ Louhan, Goldrice Green

Black glutinous rice √ Parakeet, Bakul

Goldrice Green

+ √ Goldrice Red, VIP

**1 GLAR**

rice with processed rice; the original table sent by the guest speaker is in the

L = Length. (+) means more oblong. (=) means more rounded. A = Aroma. (+) means more fragrant. () means less fragrant.

2 Taste The resulting taste (more acidic or sweet)

R = Taste. (+) means sweeter. () means more acidic. = means having these criteria within normal limits.

3 Aroma The fragrance level of rice

**Figure 1** describes a flowchart design used in the process of making this system or application. First, the collected data will be analyzed and will be used as a reference in making classes and subclasses on ontology, and the process of creating classes and subclasses will involve the use of Protégé tools. Then the next step is to determine the property. At this stage, we will determine the property object and data property, which will be needed as attributes and relations of each data. The first property object is created based on the class and subclass that were created before, and each class will have its data property.

After that create a data property; this time the creation will be affected by property objects, and this data property will be useful to name the class and the data to be included in this ontology because each data will have its name used for identifying it. Then classify all data entered into ontology. Each incoming data must have at least one relationship with other data so that it can be used based on the relationship they have. All data will be given a relationship with each other; after that the data is ready and stored in the form of an OWL file, which will be used later in the application. Next is creating a SPARQL query that will be used to retrieve data from the OWL file. To be able to make the query, PREFIX must first be specified, which is the name of the place of the data [11]. Furthermore, the WHERE is determined, to give a limit on the data to be taken, by determining the conditions or conditions that must be met to retrieve the data. Inside the WHERE, there is a FILTER, which is useful for classifying data retrieval as needed. The next step is to do the making of an application, starting from the design of how the application will look up to the functions in the application. Besides that, it also makes a connection between the OWL file containing the ontology data and the application.

Ontology graphics, or commonly referred to as OntoGraf, is one of the features found in Protégé tools. This feature was introduced to the user, starting from version 4.3 [12]. The Protégé software adapts the Java programming language which can be customized according to user needs [13]. Usually, ontology research

**Figure 1.** *Flowchart.*

and acquired knowledge using Protégé software. The function of OntoGraf itself is to provide an interface so that users can manage navigation from relationships between classes, properties, and individuals contained in OWL files [14]. By using this menu, the user can see the display of relations between class, property, and individuals in the OWL file. There is an OntoGraf feature that provides interactive support for navigating the OWL ontology relationship. Various layouts are supported to regulate the structure of the ontology [15]. The types of data relationships supported include subclass, individual, domain or range object properties, and equivalence.

**Figure 2** is an ontograph wherein the Protégé application; there are seven depressive disorders and eight subclasses of depressive disorder. Other specified depressive disorder and unspecified disorder is being one part because they have the same characteristics. The picture shows the depressed mood subclass has two individuals, namely, not experiencing and experiencing. The subclass of psychological diagnosis has two individuals, namely, none and possible; subclass how long describes how long the user has experienced problems such as depressed mood or more irritable, that is, more than 12 months, more than 2 weeks, do not know, more than 1 month, not long ago, and more than 2 years.

**Figure 3** is the first-level display of the ontograph. The conclusion is that rice has members, namely, the type of rice that exists. Then as a subclass of rice, there are descriptions and dishes. The description has a brand and variable, while the dishes contain rice dishes. Next level 2 of the ontograph is a member of each subclass. Rice dishes consist of names of processed foods, and brands have members, namely, existing rice brands, while variables have members, namely, the criteria for rice used.

The OWL file is the output generated from the design that has been done before, namely, the design of classes, subclasses, property objects, and datatype properties. The following are the results of the design. The class in the class design is depressive order. **Figure 4** shows that the depressive order class has subclasses, which are how long, depressed food, psychological diagnosis, food problems, problems with tempers, menstruation, drugs, and medicinal medicine, while **Figure 5** shows the contents of the class and subclass of OWL in the second scenario.

The input of the first scenario:

*Ontograph of the second scenario.*

**Figure 3.**

**59**

1.How long the user has experienced problems such as depressed mood or more irritable has six individuals, that is, more than 12 months, more than 2 weeks, do not know, more than 1 month, not long ago, and more than 2 years.

2.Depressed mood has two individuals: not experiencing and experiencing.

*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health…*

*DOI: http://dx.doi.org/10.5772/intechopen.88180*

**Figure 2.** *Ontograph of the first scenario.*

*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health… DOI: http://dx.doi.org/10.5772/intechopen.88180*

**Figure 3.** *Ontograph of the second scenario.*

The input of the first scenario:


and acquired knowledge using Protégé software. The function of OntoGraf itself is to provide an interface so that users can manage navigation from relationships between classes, properties, and individuals contained in OWL files [14]. By using this menu, the user can see the display of relations between class, property, and individuals in the OWL file. There is an OntoGraf feature that provides interactive

supported to regulate the structure of the ontology [15]. The types of data relationships supported include subclass, individual, domain or range object properties, and

**Figure 2** is an ontograph wherein the Protégé application; there are seven depressive disorders and eight subclasses of depressive disorder. Other specified depressive disorder and unspecified disorder is being one part because they have the same characteristics. The picture shows the depressed mood subclass has two individuals, namely, not experiencing and experiencing. The subclass of psychological diagnosis has two individuals, namely, none and possible; subclass how long describes how long the user has experienced problems such as depressed mood or more irritable, that is, more than 12 months, more than 2 weeks, do not know, more

**Figure 3** is the first-level display of the ontograph. The conclusion is that rice has members, namely, the type of rice that exists. Then as a subclass of rice, there are descriptions and dishes. The description has a brand and variable, while the dishes contain rice dishes. Next level 2 of the ontograph is a member of each subclass. Rice dishes consist of names of processed foods, and brands have members, namely, existing rice brands, while variables have members, namely, the criteria for

The OWL file is the output generated from the design that has been done before, namely, the design of classes, subclasses, property objects, and datatype properties. The following are the results of the design. The class in the class design is depressive order. **Figure 4** shows that the depressive order class has subclasses, which are how long, depressed food, psychological diagnosis, food problems, problems with tempers, menstruation, drugs, and medicinal medicine, while **Figure 5** shows the con-

support for navigating the OWL ontology relationship. Various layouts are

than 1 month, not long ago, and more than 2 years.

*Ontological Analyses in Science,Technology and Informatics*

tents of the class and subclass of OWL in the second scenario.

equivalence.

rice used.

**Figure 2.**

**58**

*Ontograph of the first scenario.*

6.Menstruation has two individuals: no and moderate period. This menstruation is only experienced by women, as an initial indication of premenstrual

*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health…*

8.Medical treatment has three individuals: ever undergoing, undergoing, and

The output of the first scenario is the application which can find information about signs of depressive disorder. Users will choose the type of depressive disorder they want to know the information, and then the system will process. Then the desired data will appear; after getting the desired data, the user can try again to find

The input of the second scenario is class rice has property objects and datatype properties that vary according to the characteristics of each rice. Rice class has members, namely, the type of rice. Each type of rice has different attributes. Then brands have types of rice and rice variables. Each subclass rice dish contains rice. These preparations also have object properties and datatype properties that differ according to the needs of each processed rice. Members or identifiers in the rice dish class are types of rice. Subclass brand is a subclass of the brands of rice sold in Indonesia. Each brand has an object property such as compatibility with the type of rice and the characteristic determinant of rice. Subclass variable contains variables that are used to classify the types of rice, and aroma has attributes such as the type

The output of the second scenario is to build an application to make it easy for users to find the most suitable type of rice so that the desired rice processing is appropriate and to find out the application of the ontology method in making expert

The next step is to convert the results from Protégé to the database. In the first scenario, we will use a CSV file where the results of Protégé are then exported to a CSV format file with entities containing individuals from depressive disorder and values of properties containing object properties. This step is shown in **Figure 6**. Whereas in the second scenario directly using PHP, where the SPARQL query is

used to retrieve data from OWL files that have been created using PHP. In this process function filters one and two function as complex character removers so that the results of the OWL can be read clearly [16–18]. This function is essential, so

the other data, or if there is nothing to look for, the application is complete.

7.Narcotics have two individuals: users and nonusers.

of rice and the brand of rice that has these characteristics.

system applications based on Android.

**Figure 6.**

**61**

*SPARQL query for the first scenario.*

dysphoric disorder.

*DOI: http://dx.doi.org/10.5772/intechopen.88180*

never undergoing.

**Figure 4.** *Design class of the first scenario.*


**Figure 5.** *Design class of the second scenario.*


*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health… DOI: http://dx.doi.org/10.5772/intechopen.88180*


The output of the first scenario is the application which can find information about signs of depressive disorder. Users will choose the type of depressive disorder they want to know the information, and then the system will process. Then the desired data will appear; after getting the desired data, the user can try again to find the other data, or if there is nothing to look for, the application is complete.

The input of the second scenario is class rice has property objects and datatype properties that vary according to the characteristics of each rice. Rice class has members, namely, the type of rice. Each type of rice has different attributes. Then brands have types of rice and rice variables. Each subclass rice dish contains rice. These preparations also have object properties and datatype properties that differ according to the needs of each processed rice. Members or identifiers in the rice dish class are types of rice. Subclass brand is a subclass of the brands of rice sold in Indonesia. Each brand has an object property such as compatibility with the type of rice and the characteristic determinant of rice. Subclass variable contains variables that are used to classify the types of rice, and aroma has attributes such as the type of rice and the brand of rice that has these characteristics.

The output of the second scenario is to build an application to make it easy for users to find the most suitable type of rice so that the desired rice processing is appropriate and to find out the application of the ontology method in making expert system applications based on Android.

The next step is to convert the results from Protégé to the database. In the first scenario, we will use a CSV file where the results of Protégé are then exported to a CSV format file with entities containing individuals from depressive disorder and values of properties containing object properties. This step is shown in **Figure 6**.

Whereas in the second scenario directly using PHP, where the SPARQL query is used to retrieve data from OWL files that have been created using PHP. In this process function filters one and two function as complex character removers so that the results of the OWL can be read clearly [16–18]. This function is essential, so


**Figure 6.** *SPARQL query for the first scenario.*

3.Diagnosis psychology has two individuals: none and may be present.

**Figure 4.**

**Figure 5.**

**60**

*Design class of the second scenario.*

*Design class of the first scenario.*

*Ontological Analyses in Science,Technology and Informatics*

4.Mood problems have three individuals: all the time, rarely, and often.

5.Easy to get angry has two individuals: yes, it is easier, and no, it is not easier.

there is no error in retrieving data from OWL. Also, this function is useful for calling data from the OWL while matching data that has been previously made according to the right results. The match function is given results for the function to

be displayed on the application. Moreover, thus it can be concluded that the query used to retrieve data is SELECT DISTINCT \* to retrieve all data using the conditions specified in the WHERE where there are conditions that must be met to choose

*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health…*

The following are some of the pseudocodes contained in the design:

After doing the development and the prototype is declared complete, the implementation is done. Implementation is done when publishing the application in Play Store. After that is monitoring the users who are interested in using this application. The application created can be seen on the Google Play Store with the name MentalHelp application for the first scenario and RicheApp for the second

**Figure 8** describes the Disorder search menu, wherein the user can choose the answer that is perceived by the user and then look for what kind of depressive disorder might be suitable, but the user may not find the answer sought. This image is part of the search menu where the results will appear in the selection which matches the existing data and can show the display for depressive info where there is a choice

of each type of depressive disorder containing information about each type.

the right results [19, 20]. This step is shown in **Figure 7**.

**Begin**

**then**

**end if**

scenario.

**Figure 9.**

**63**

*Result of the second scenario.*

**foreach** dishes

rice = rice\_variance

**if** criteria dish\_rice = criteria rice

*DOI: http://dx.doi.org/10.5772/intechopen.88180*

**Figure 7.** *SPARQL query for the second scenario.*


**Figure 8.** *Result of the first scenario.*

*An Ontology-Based Approach to Diagnosis and Classification for an Expert System in Health… DOI: http://dx.doi.org/10.5772/intechopen.88180*

be displayed on the application. Moreover, thus it can be concluded that the query used to retrieve data is SELECT DISTINCT \* to retrieve all data using the conditions specified in the WHERE where there are conditions that must be met to choose the right results [19, 20]. This step is shown in **Figure 7**.

The following are some of the pseudocodes contained in the design:

**Begin foreach** dishes **if** criteria dish\_rice = criteria rice **then** rice = rice\_variance **end if**

there is no error in retrieving data from OWL. Also, this function is useful for calling data from the OWL while matching data that has been previously made according to the right results. The match function is given results for the function to

*Ontological Analyses in Science,Technology and Informatics*

**Figure 7.**

**Figure 8.**

**62**

*Result of the first scenario.*

*SPARQL query for the second scenario.*

After doing the development and the prototype is declared complete, the implementation is done. Implementation is done when publishing the application in Play Store. After that is monitoring the users who are interested in using this application. The application created can be seen on the Google Play Store with the name MentalHelp application for the first scenario and RicheApp for the second scenario.

**Figure 8** describes the Disorder search menu, wherein the user can choose the answer that is perceived by the user and then look for what kind of depressive disorder might be suitable, but the user may not find the answer sought. This image is part of the search menu where the results will appear in the selection which matches the existing data and can show the display for depressive info where there is a choice of each type of depressive disorder containing information about each type.

**Figure 9.** *Result of the second scenario.*

**Figure 9** is the main menu display of the application when the application starts. The main menu includes Rice Ontology, Rice Info, Rice Dishes, and Rice Seller. The Rice Selection feature makes it easy to find types of rice processed foods that are common in Indonesia. After the type of processing is selected, the user can click the "Calculate" button. After clicking the "Calculation" button, it will display the type of rice and the brand that sells the rice in Indonesia. There is a menu to return to the selection of processed types, to return to the main menu, or to exit the application.

Based on the results, the information presented in the application is complete and valid following the results of interviews from experts. In the first scenario, several developments exist after the system is created which can make it easier for people to know information about the depressive disorder. Previously to find out the type of depressive disorder, users cannot know quickly, while after this application, users can know that depressive disorder has several types. In the process of estimating the type of depressive disorder, previously it was difficult for the user to estimate whether he had a specific depressive disorder, while after this application, the user could estimate the depressive disorder that he had or whether he did not have it at all.

In the second scenario, previously in the rice selection process, the user used perceptions without specific guidelines and benchmarks, while after the application of the expert system, there could be a proper and correct reference for selecting each rice selection. In the delivery of information, the user previously conveyed information about the suitability of rice with processing based on perceptions and little knowledge of others, while after this application, information about the suitability of rice can be obtained quickly and surely whenever and wherever.
