Expert System for Measuring the Level of Spinach Freshness using

Certainty Factor Method

Marchello William

, Jason Kristanto

, Jansen Sampurna

, Edrick

and Melvin Hendronoto

Informatics Student, Multimedia Nusantara University, Tangerang, Indonesia

Keywords:

Spinach, Certainty Factor, Prototype, Expert System, Freshness Level.

Abstract:

Expert system for measuring the level of spinach freshness is made because spinach is one of the vegetables

that has a good nutritions and often consumed by people, especially in Indonesia. Spinach that will be con-

sumed must be check ﬁrst the freshness level. However, not everyone know how to ﬁnd out the level of spinach

freshness Therefore, an expert system is needed to measure the level of spinach freshness. Expert system for

measuring the level of spinach freshness is also rarely found, especially in Indonesia. The measurement of

level of spinach freshness can be seen from several factors or symptoms, such as soft texture, leaf color be-

tween light green and green, and no white spots on the leaves. These factors or symptoms determine the level

of spinach freshness or spinach quality whether the spinach is good or normal. Expert system created in this

study uses the Certainty Factor method because this method has been widely used in several other studies and

gives good results. This research still uses prototype system, but it cant give good results, namely the system

made to show the quality calculation of the level of spinach freshness in accordance with manual calculations.

Test result on expert get 90% accuracy.

1 INTRODUCTION

This research will discuss the level of spinach fresh-

ness using Certainty Factor method with the help of

an expert system. Artiﬁcial intelligence are every-

where, perform in so many sector such as security

using biometric, health etc (Alexander et al., 2018).

Expert system is part of artiﬁcial intelligence that

combines knowledge from the research of an expert

who has knowledge of a ﬁeld to help make a deci-

sion(Islam and Mishra, ). It has been implemented in

many ﬁelds including health (Kusnadi, 2013), educa-

tion (Futra, 2014) and others.

Spinach contains vitamin K, iron, ﬂavonoids,

carotene, vitamin C, and other good nutrients. These

nutrients are good for the body and can prevent sev-

eral diseases such as cancer, presbyopia, cataract, and

other diseases. This has been proven from an inter-

national research journal entitled Nutritional Value of

Spinacia Oleraecea Spinach which states that spinach

is good for the body (Singh et al., 2016). In addition,

these beneﬁts make people become interested in con-

suming spinach, including Indonesian.

Indonesia has abundant natural resources, includ-

ing spinach. Based on data from Statistics Indone-

sia regarding spinach production in Indonesia, the in-

crease level of spinach production in Indonesia is in-

creasing almost every year (Statistik, 2019). The in-

crease in spinach production each year is between

10.000 and 20.000 per ton. This shows that spinach is

in demand by Indonesian people. Therefore, the qual-

ity of spinach also needs to be considered. Before

spinach is consumed, it is better to check the fresh-

ness level ﬁrst so that the nutrients in spinach can be

beneﬁcial to the body. The level of spinach fresh-

ness is generally seen from the physical condition.

However, not everyone knows how to check the level

of spinach freshness. Therefore, an expert system is

needed to help people in giving advice on choosing

fresh spinach.

Spinach that is good to be consume is fresh

spinach and useful substances in spinach can still be

utilized. Checking the level of spinach freshness is

generally seen from the physical condition of spinach.

However, not everyone knows how to check the level

of spinach freshness. Therefore, an expert system

is needed to help people in giving advice on choos-

ing fresh spinach. Currenlty in Indonesia there are

not many system for measuring the level of veg-

etable freshness, especially spinach. Based on this,

an expert system for measuring the level of spinach

freshness needs to be made. Expert system that

William, M., Kristanto, J., Sampurna, J., Edrick, E. and Hendronoto, M.

Expert System for Measuring the Level of Spinach Freshness using Certainty Factor Method.

DOI: 10.5220/0009909703190324

In Proceedings of the International Conferences on Information System and Technology (CONRIST 2019), pages 319-324

ISBN: 978-989-758-453-4

319

will be created is to use the certainty factor method.

This method was chosen because there are already

many research journals that use this method, such

as Sistem Pakar Mendiagnosa Penyakit Tanaman Kol

Menggunakan Metode Certainty Factor (Rayuwati,

2013), Sistem Pakar Diagnosa Penyakit Tomat den-

gan Metode Certainty Factor (Rohmah, 2017), Sistem

Pakar Mendiagnosa Penyakit Kolesterol pada Remaja

dengan Metode Certainty Factor Berbasis Web (Si-

hotang, 2017), and other researches. Based on these

researches, certainty factor method can provide good

suggestions to users.

The system that will be created will receive input

from the user to select factors or symptoms that exist

in spinach that will be checked for the freshness. Af-

ter that, the user ﬁlls in the certainty level (CF User)

on each factor or symptom. The system will calcu-

late the CF factor to get the freshness percentage of

spinach. Based on the testing result expert system

was successfully made with 90% accuracy so it can

be useful to help people or users who have difﬁculty

in choosing fresh spinach. Therefore, people or users

can choose spinach that is good for the body and get

maximum beneﬁts from the spinach.

2 LITERATURE REVIEW

2.1 Level of Spinach Freshness

The level of spinach freshness can be seen from the

color of spinach leaves which are still integrated with

the stem. Spinach leaves that are suitable for con-

sumption are spinach leaves that are green or dark

green while spinach that is not suitable for consump-

tion is spinach with yellow leaves and also on the sur-

face of a good spinach leaf there are no white spots

(fungus) (Zhang and Zhang, 2014).

2.2 Certainty Factor Method

The following are literature review of researches us-

ing Certainty Factor method.

1. Sistem Pakar Mendiagnosa Penyakit Tanaman

Kol Menggunakan Metode Certainty Factor by

Danil Iskandar in 2017. This research diagnosed

cabbage plant diseases that can provide advice to

users and farmers and provide solutions that are

appropriate to the symptoms experienced by cab-

bage.

2. Sistem Pakar Diagnosa Penyakit Tomat dengan

Metode Certainty Factor by Siti Nur Romah in

2017. This research produced a system that can

provide disease indication results based on the

selected symptoms and the level of conﬁdence.

Validity testing is done by using system calcula-

tion and manual calculation by ﬁnding highest CF

value that is equal to conﬁdence value 93.77%. So

the system can be declared as valid.

3. Sistem Pakar Mendiagnosa Penyakit Kolesterol

pada Remaja dengan Metode Certainty Factor

Berbasis Web by Hengki Tamando Sihotang in

2014. This research simplify and provide a calcu-

lation of the completion of how certain the users

are aware of teenage cholesterol disease.

2.3 Results Validation

System testing is done by comparison result of man-

ual testing with those calculated through the system.

This research will be testing the highest CF value.

The following are literature review of research test-

ing.

1. Sistem Pakar Diagnosa Penyakit Tomat dengan

Metode Certainty Factor by Siti Nur Romah in

2017. This research examines the highest CF

value with conﬁdence value of 93.77%.

There are also rules that determine the level of

spinach freshness that is inﬂuenced by the color of

leaf, the amount of mold, and the texture of the leaf.

The test result are in the form of a comparison be-

tween manually calculated Certainty Factor and sys-

tem calculated using system.

Certainty Factor can be calculated using the fol-

lowing formula.

CF[H, E] = MB[H, E]–MD[H, E] (1)

Variables:

CF[H,E] : Certainty factor hipotesis which is

inﬂuenced by evidence e known with certainty.

MB[H,E]: Measure of belief against hypothesis H, if

given evidence E (between 0 and 1).

MD : Measure of disbelief

P : Probability

E : Evidence

The basic formula is used if there is no CF value

for each spinach fact. The Certainty Factor used to

measure the quality of spinach is as follows.

1. Certainty Factor for rules with a single

premise/factor

CF f actor = CF[user] ∗CF[expert] (2)

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2. If there are more than factors, then it will be de-

termined by the following equation.

CFcombination = CFold +CF f actor

∗ (1 −CFold) (3)

3. Whereas to calculate the percentage of quality

freshness can use the following equation.

CF percentage = CFcombination ∗ 100 (4)

3 SYSTEM DEVELOPMENT

METHOD

The method used to develop this system is pro-

totype method due to one of the advantages of pro-

totype method because this method able to take con-

crete needs and can be developed into largescale sys-

tem but only the simpliﬁed version was made (bam-

bang hermawan, 2015). System only accepts input in

the form of conﬁdence percentage in a spinach quality

category. The steps of this method are as follows:

Figure 1: Prototype Method

1. Requirements gathering

Identify all the requirements and system out-

line that will be made. Requirements are ex-

plored with stakeholders to deﬁne software re-

quirements.

2. Build the prototype

After gathering requirements, temporary design

will be made for the user. The temporary design

consist of input, process, and output that can be

seen by the user.

3. Evaluate the prototype

Evaluate the prototype to ﬁnd out if it has fulﬁlled

all the necessary requirements. If not, then the

developer must gather requirements more deeper

and prototype must be designed and built or de-

veloped so that it suits the overall requirements.

4. Code the system

Prototype that has been approved will be code into

certain programming language.

5. Test the system

System has become a software and must be tested

using either White Box, Black Box, and other test-

ing.

6. Evaluate the system

Sistem will be evaluated and quality of the system

will be checked. If quality of the system is still not

according to user expectations, then system will

be code and test again. When system is ready to

be use, system can measure the level of spinach

freshness. If there is an update, then there will be

requirements gathering again.

System will be made with C/C++ programming

language with CodeBlock editor. System will accept

2 inputs number with ﬂoat data type where the value

of the inputs are probability value that range from -

1 to 1 and each input comes from experts and users.

System has 3 criteria for determining the quality of

spinach, those are the state of spinach leaves, the color

of spinach leaves, and the number of white mold un-

der the spinach leaves. Each criterion receives 2 in-

puts, namely from the experts and the users, so that

the total input is 8 kinds. System will process input

data from users and experts to measure certainty fac-

tor based on the level of certainty, the level of uncer-

tainty, and probability then processed using the for-

mula described in the previous chapter. Output from

the system is the value of a certainty factor that ranges

from -1.0 to 1.0 (certainly not to very certain) to de-

termine whether spinach leaves are suitable for con-

sumption or not.

4 RESEARCH RESULT

4.1 Knowledge Base

Research conducted by interviewing experts who

have experience in understanding the level of spinach

freshness. The following are the categories of symp-

toms and quality in spinach obtained from experts.

Figure 2: Symptoms Category.

Expert System for Measuring the Level of Spinach Freshness using Certainty Factor Method

321

space

Figure 3: Quality Category.

To determine certainty factor information from the

expert, seen from CF combination based on the CF

term table.

4.2 Rule CF

The application of the Certainty Factor method re-

quires several rules in form of variables and weight

values given by experts. The weight value needed for

each symptom can be seen in Figure 4. CF rules that

contain symptoms and weight values from experts for

each disease are shown in Figure 5.

Figure 4: Symptoms Percentage.

Figure 5: Test case calculation.

4.3 Implementation

System will display list of symptom criteria consist-

ing of 3 types which can be seen in Figure 6.

Figure 6: Symptoms criteria

User will input their certainty level about symp-

toms found in spinach with inputs ranging from 0

(certain), 0.2 (not certain), 0.4 (little certain), 0.6

(pretty certain), 0.8 (certain), 1.0 (very certain) as in

Figure 7.

space

Figure 7: Input certainty factor

If the user has ﬁnished inputting all certainty about

symptoms found, then the application will issue a

certainty percentage of spinach quality which can be

seen in Figure 8.

Figure 8: Result certainty percentage of spinach quality

4.4 Application Calculations

Application calculations are used to determine the

quality of spinach freshness. The calculation method

used is Certainty Factor.

The ﬁrst step of using CF method in the calcula-

tion process based on symptoms that have been in-

putted by the user in ﬁgure 3 is to multiply 2 proba-

bility values namely weight given by the user with the

weight value rule given by the expert. The results of

multiplication are shown in Figure 5.

The next step is the combination of the multipli-

cation results of each symptom from the product in

table 4 with each combined iteration show in Figure 9

where the good spinach has 77% percentage value of

spinach quality which can be seen in Figure 8.

Figure 9: Symptoms Percentage.

4.5 System Testing

System testing is done to determine the accuracy be-

tween the calculation of the certainty percentage of

spinach freshness from the system equal to the man-

ual calculation. The system is tested by giving users

to input CF which can be seen in Figure 5. The results

of calculations on the system can be seen from ﬁgure

4 and the results of manual calculation can be seen

in Figure 9. From these calculations were shown to

experts and checked by experts to know the accuracy

level. The level of accuracy is obtained from com-

patibility between the spinach that is checked for the

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322

level of freshness manually by experts and those that

are checked with the system. From the test result it

can be said that the accuracy is 90% which can be

seen in Figure 11.

Figure 10: Symptoms Criteria Testing.

Figure 11: Expert Test Result.

Result from Figure 11 can produce 90% accuracy

because out of 10 test cases, there was one wrong test

case, that was test case 5, where the expected result

is different from result checked in system. Test case

5 included in category not good because CF end is

under 70%. Failure might be caused by expert give

inputs or weight that are not quite right.

From the research conducted there are still weak-

nesses in several aspects. This research might be de-

veloped to give maximum result, such as add more

criteria or expert value that can be checked with other

experts.

5 CONCLUSIONS

Based on research conducted, the expert system with

the Certainty Factor method can be implemented well

for the measurement of the level of spinach fresh-

ness. Calculations made with the system that are

made compared with calculations done manually. The

results of the comparison done are same. So that the

system created can help to see the quality of spinach

from the symptom entered by user. With this system,

it is expected to be useful for people who want to see

the spinach quality or spinach freshness.

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323

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