\*\*This is a Double-blind review, please do not include authors information in this version \*\* Received Date: \*date Accepted Date: \*date Published Date: \*date **HIGHLIGHTS** - Fuzzy Evaluation Method was used to evaluate Qu’ran Recitation Competition. - The evaluation is based on four factors, “Tajweed”, “Tarannum”, “Fasohah” and Vocal. - The sample of this study is from Qur’an Recitation Competition in Klang, Malaysia ABSTRACT *A Qur’an Reciting Competition evaluates a participant's performance based on several factors, including Tajweed, Tarannum, Fasohah, and Vocal Quality. Participants in the Qur’an Reciting Competition are usually assigned a point value of 100, with each point representing a linguistic word or label such as “Exceptional,” “Excellent,” “Fairly Good,” and so on. Evaluating participant performance seems difficult because it involves human decision-making, which is imprecise, ambiguous, and unpredictable. This study employed the fuzzy evaluation method to assess participants’ performance at a Qur’an Reciting Competition in Klang. In this manner, the membership function graph was used to determine the membership value of each satisfaction level. The satisfaction level of each participant’s mark was then computed. At the end, the fuzzy markings with linguistic values would be obtained. The proposed method provides an alternative approach that is reasonable and intelligent in assessing competitors’ performance. This method is practical because it can increase the satisfaction of participants and assist the panels in making more reliable decisions during the competition.* *Keywords: Fuzzy evaluation method, membership function, satisfaction level, Qur’an recitation competition* # INTRODUCTION The meaning of the Qur’an to the life of a Muslim cannot be overemphasized. It is enough to mention that it is a guidebook which is responsible for the considerable success of the early Muslims (Gusau, 2012). In each sentence, passage and *surah*, the Qur’an contains precious literary elements which are like jewels; this is one of its miracles. The Holy Qur’an includes all the requirements for guiding and educating people in social, individual, moral, legal, life and afterlife aspects. The Qur’an is a masterpiece of its own expectations with a distinctive style. From a literary point of view, it has an inimitable and astonishing style. It also refers to all individuals which makes it desirable for any reader or listener as it has a clear and intelligible language (Nayef & Wahab, 2018). In the following Qur’anic quotation, Allah (SWT) commands: (Q73:3). Muslims were involved not only in the memorization of the Quran from the time of Muhammad the Prophet (SAW), but also in its poetic beauty with expressiveness and unique interpretative characteristics. This is the result of intensive reciting study known as ‘Tajweed’ and ‘Tarteel’ with continuous practice, which is reciting (Gusau, 2012). In order to instill the love of the Qur’an among Muslims, many parties have organized Qur’an reciting competitions at different levels. Qur’an reciting competition has a spiritual, educational, economic and social impact on the lives of the Muslim (Ummah, 2015). The organization has received an interesting response not only from Malaysia, but from all over the world. The first Malaysian Prime Minister, Tunku Abdul Rahman, founded the International Quran Reciting Competition, which has been held annually in Malaysia since 1961 (Yusof & Tawel, 2013). Each competition has its own different elements or criteria which are used to evaluate the participants. For the Quran reciting competition, there are 4 criteria that will be evaluated that will always be used throughout the reciting competition: Vocals, *Tajweed, Fasohah* and *Tarannum* (Malaysia, 2016). In the *Tajweed* section, the rules of *nun* and *mim Sakinah*, *tanwin, ra* and *lam* and other *Tajweed* rules are considered (Hassan & Zailaini, 2013). Reading the Quran is not like reading other normal books. In order to read so reliably correctly, it requires a series of conventions and special rules that are free from errors. In addition to long vowels and other morphological rules, these conventions include sound-pronouncing on the right track, and rules on when to stop and where to continue (Al-Jazi, 2017). In a Qur’an Reciting Competitiom, there are always points that concentrate only on four elements, such as Vocals, *Tajweed, Fasohah* and *Tarannum*, when the participants, Qari and Qariah recite the Holy Qur’an during the competition. Participants perform their own performances in front of a panel of judges.This method of evaluation is recognized and has been applied by most of the Qur’an Reciting Competition Committees. However, based on the findings that have been made, this assessment approach does not seem like the best way to measure the participants’ Qur’an reciting abilities since it contains elements of fuzziness. It is so difficult to measure subjective aspects as they tend to be ambiguous. This problem arises because individual panelists will have distinct attitudes, experiences and tolerance during the assessment process. The scores obtained will differ and thus, the average score which may include a decimal value will be taken. Since the 100-point evaluation approach is commonly used in terms of the nominal importance of the linguistic value, it will be difficult to define the linguistic values of the points. Most of the time, members of the panel will feel very dissatisfied with the results once all the participants have scored. In addition, participants will always consider the tournament to be unfair. Therefore, a more reliable method is needed to measure the performance of the participants other than the method that is often used to gauge reciting abilities. In this study, the fuzzy evaluation method is applied to measure the performance of participants in the Qur’an reciting competition. The fuzzy method is used because it is more convenient to be applied compared to other artificial intelligence methods (V. Zaporozhko et al., 2020). The basic principle of the fuzzy evaluation method is to define the evaluation variables, the normal factor evaluation grades, membership and the weights (Wang et al., 2013). Fuzzy measurements are efficient and easy to apply over a specific set of tasks (Pape et al., 2013). The applications of fuzzy logic have been enhanced by today’s technological growth. Fuzzy Logic is working its way forward in the decision-making and assessment areas of manufacturing (Patil et al., 2012). The analysis using a fuzzy method approach with the membership values provides reliable results compared to the analysis using the mean and percentage of statistics (Yusoff et al., 2013). # RESEARCH METHODOLOGY This section shows how the fuzzy evaluation method is used to assess the performance of participants in the Qur’an reciting competition. The following methodology is utilized in this evaluating procedure. *Step 1: Normalizing the marks* The marks obtained by each of the students are converted to normalized values. A normalized value refers to a value in the range of \[0, 1\]. Table 1 tabulates the sample marks and the normalized values obtained by a student from two schools for all the criteria. > (1) where (NV) = normalized value for each criterion, (TM) = total marks and (MO) = marks obtained Table 1: Samples of marks and normalized values
No. School Name Criteria Total Marks Obtained Normalized Value
1 SMK JALAN KEBUN

Tajweed

Tarannum

Fasohah

Vocal

40

25

20

15

31

8

11

9

0.78

0.32

0.55

0.60
10 SMK
MERU

Tajweed

Tarannum

Fasohah

Vocal

40

25

20

15

31

10

8

10

0.78

0.40

0.40

0.67
*Step 2: Developing the graph of the fuzzy membership function*. The membership function graph is created in order to perform the fuzzification process. The input value is mapped to the membership graph function to obtain the fuzzy membership value for that specific input value. The level of satisfaction would be proportional to the value of each membership. Table 2 depicts the twelve levels of satisfaction proposed for this study (Daud et al., 2011). The range of marks for each level of satisfaction is indicated by the degrees of satisfaction. The mapping function for the corresponding satisfaction standard defines the highest level of satisfaction as T (Xi) ranging from 0 to 1. > Table 2: Standard satisfaction levels and the corresponding degrees of satisfaction

Satisfaction Levels (Xi)

Degrees of Satisfaction

Maximum Degrees of Satisfaction T(Xi)

Exceptional (ET)

80%-100% (0.8-1.0)

[T(X1)] = 1.0

Excellent (EX)

75%-79% (0.75-0.79)

[T(X2)] = 0.79

Very Good (VG)

70%-74% (0.7-0.74)

[T(X3)] = 0.74

Fairly Good (FG)

65%-69%(0.65-0.69)

[T(X4)] = 0.69

Marginally Good (MG)

60%-64% (0.6-0.64)

[T(X5)] = 0.64

Competent (CT)

55%-59% (0.55-0.59)

[T(X6)] = 0.59

Fairly Competent (FC)

50%-54% (0.5-0.54)

[T(X7)] = 0.54

Marginally Competent (MC)

45%-49% (0.45-0.49)

[T(X8)] = 0.49

Bad (BD)

40%-44% (0.4-0.44)

[T(X9)] = 0.44

Fairly Bad (FB)

35%-39% (0.35-0.39)

[T(X10)] = 0.39

Marginally Bad (MB)

30%-34% (0.3-0.34)

[T(X11)] = 0.34

Very Bad (VB)

0-29% (0-0.29)

[T(X12)] = 0.29
*Step 3: Calculating the degree of satisfaction* In this step, the degree of satisfaction which is denoted by *D*(*Ci*) is evaluated by: Degree of satisfaction *D*(*Ci*) = (2) where y = degree of membership value and *T(X)* = the maximum degree of satisfaction *Step 4: Evaluating the Final mark* For the final step, the final scores or marks are calculated using the following formula: > (3) where w is the sum of marks that reflects the number of criteria. Table 3: Fuzzy grade sheet

No

 Criteria

Fuzzy Membership Value

Degree of Satisfaction

Final

Mark
VB

MB

FB

BD

MC

FC

CT

MG

FG

VG

EX

ET

1

C1

 y1

y2

y3

y4

y5

y6

y7

y8

y9

y10

y11

y12

D(C1)

F(S1)

C2

 :

:

:

:

:

:

:

:

:

:

 :

:

 D(C2)

C3

 :

:

:

:

:

:

:

:

:

:

 :

:

 D(C3)

C4

 :

:

:

:

:

:

:

:

:

:

 :

:

 D(C4)
**FINDINGS AND DISCUSSIONS** To illustrate this method of evaluation, examples of marks for a student are taken from Table 1. Figure 1 shows the membership function graph that is produced to carry out the fuzzification procedure in Step 2. > ![](62eb22680dd6c_media/media/image4.png) > > Figure 1: Membership functions for satisfaction level of Qur’an Reciting Competition Figure 1 represents the satisfaction levels of Excellent and Exceptional, which reflect the degree of membership of 0.2 and 0.8, respectively for the normalized value of 0.78 from the first criterion. The degree of satisfaction with criterion 1 is calculated as follows using equation 2: > (4) Finally, the participant's final mark for all criteria is calculated using equation 3: > (5) Based on the final mark, the participant from SMK Jalan Kebun is given a fuzzy linguistic term of Very Good at 1.0. Furthermore, the final grade can be calculated as 69.40 (multiplied by 100%), which corresponds to the linguistic phrase “Very Good.” The details of the fuzzy marks produced by this evaluation method are shown in Table 4. Table 4: The samples of fuzzy marks of SMK Jalan Kebun in 2013

No

 Criteria

Fuzzy Membership Value

Degree of

Satisfaction

Final

Mark
VB

MB

FB

BD

MC

FC

CT

MG

FG

VG

EX

ET

1

C1

 -

-

-

-

-

-

-

-

-

-

0.2

0.8

0.96

 0.6940

C2

 -

0.4

0.6

-

-

-

-

-

-

-

 -

-

 0.37

C3

 -

-

-

-

-

-

0.8

0.2

-

-

 -

-

 0.60

C4

 -

-

-

-

-

-

-

0.8

0.2

-

 -

-

 0.65

10

C1

 -

-

-

-

-

-

-

-

-

-

0.2

0.8

0.96

 0.6945

C2

 -

-

-

0.8

0.2

-

-

-

-

-

 -

-

 0.45

C3

 -

-

-

0.8

0.2

-

-

-

-

-

 -

-

 0.45

C4

 -

-

-

-

-

-

-

-

0.4

0.6

 -

-

 0.72
> Table 5: Results for 13 participants obtained from fuzzy and non-fuzzy methods for Klang 2013 | | | | | | | ------ | ---------------- | ----------------------- | ---------- | ----------------------------------------- | | School | Non-Fuzzy Method | Fuzzy Evaluation Method | | | | | Final Mark | Linguistic Term | Final Mark | Linguistic Term | | 1\. | 59 | Competent | 0.6940 | Very Good at 1.0 | | 2\. | 65 | Fairly Good | 0.7545 | Excellent at 0.2, Exceptional at 0.8 | | 3\. | 60 | Marginally Good | 0.7095 | Very Good at 0.6, Excellent at 0.4 | | 4\. | 61 | Marginally Good | 0.6625 | Fairly Good at 0.6, Very Good at 0.4 | | 5\. | 87 | Exceptional | 1.000 | Exceptional at 1.0 | | 6\. | 62 | Marginally Good | 0.6895 | Very Good at 1.0 | | 7\. | 46 | Marginally Competent | 0.5095 | Fairly Competent at 0.6, Competent at 0.4 | | 8\. | 64 | Marginally Good | 0.7595 | Excellent at 0.6, Exceptional at 0.4 | | 9\. | 49 | Marginally Competent | 0.5395 | Competent at 1.0 | | 10\. | 59 | Competent | 0.6945 | Very Good at 1.0 | | 11\. | 70 | Very Good | 0.7895 | Exceptional at 1.0 | | 12\. | 71 | Very Good | 0.7905 | Exceptional at 1.0 | | 13\. | 46 | Marginally Competent | 0.5095 | Fairly Competent at 0.6, Competent at 0.4 | > Table 6: Results for 11 participants obtained from fuzzy and non-fuzzy methods for Klang(P) 2013 | | | | | | | ------ | ---------------- | ----------------------- | ---------- | ------------------------------------------ | | School | Non-Fuzzy Method | Fuzzy Evaluation Method | | | | | Final Mark | Linguistic Term | Final Mark | Linguistic Term | | 1\. | 78 | Excellent | 0.8425 | Exceptional at 1.0 | | 2\. | 64 | Marginally Good | 0.7305 | Very Good at o.2, Excellent at 0.8 | | 3\. | 86 | Exceptional | 1.000 | Exceptional at 1.0 | | 4\. | 68 | Fairly Good | 0.7965 | Exceptional at 1.0 | | 5\. | 56 | Competent | 0.6115 | Marginally Good at 0.6, Fairly Good at 0.4 | | 6\. | 59 | Competent | 0.6415 | Fairly Good at 1.0 | | 7\. | 75 | Excellent | 0.8360 | Exceptional at 1.0 | | 8\. | 72 | Very Good | 0.8395 | Exceptional at 1.0 | | 9\. | 71 | Very Good | 0.8000 | Exceptional at 1.0 | | 10\. | 60 | Marginally Good | 0.7035 | Very Good at 0.8, Excellent at 0.2 | | 11\. | 72 | Very Good | 0.8325 | Exceptional at 1.0 | > Table 7: Results for 10 participants obtained from fuzzy and non-fuzzy methods for Klang 2015 | | | | | | | ------ | ---------------- | ----------------------- | ---------- | ---------------------------------------------------- | | School | Non-Fuzzy Method | Fuzzy Evaluation Method | | | | | Final Mark | Linguistic Term | Final Mark | Linguistic Term | | 1\. | 87 | Exceptional | 1.000 | Exceptional at 1.0 | | 2\. | 72 | Very Good | 0.8005 | Exceptional at 1.0 | | 3\. | 71 | Very Good | 0.7905 | Exceptional at 1.0 | | 4\. | 51 | Fairly Competent | 0.5615 | Competent at 0.6, Marginally Good at 0.4 | | 5\. | 79 | Excellent | 0.8795 | Exceptional at 1.0 | | 6\. | 81 | Exceptional | 0.8655 | Exceptional at 1.0 | | 7\. | 75 | Excellent | 0.8195 | Exceptional at 1.0 | | 8\. | 44 | Bad | 0.4905 | Fairly Competent at 1.0 | | 9\. | 40 | Bad | 0.4525 | Marginally Competent at 0.8, Fairly Competent at 0.2 | | 10\. | 69 | Fairly Good | 0.7605 | Excellent at 0.6, Exceptional at 0.4 | The computation in the fuzzy evaluation technique is based on fuzzy sets with a range of \[0, 1\]. However, the marks can be converted to a percentage. As shown in the Table above, the fuzzy marks obtained are clearly higher than the non-fuzzy marks. Aside from that, the linguistic terms of the fuzzy approach are more detailed because they include the degrees of satisfaction for each linguistic term. Using this information, we are able to describe each participant’s performance during the competition compared to the typical final mark of the non-fuzzy method. In other words, using the satisfaction levels, this approach can be used to compare the performances of participants who have the same final linguistic terms. For example, Table 5 shows that participant 1 and participant 10 had the same total marks of 59 using the non-fuzzy method. Despite having the same normalized value for the first criterion, the other three criteria had different normalized values in Table 1. In the second and final criteria, participant 10 had a higher normalized value than participant 1. Thus, both participants differed in the degree of satisfaction for most criteria with the support of the fuzzy grade sheet. Table 4 shows that the final mark for participant 1 was 0.6940 while participant 10 scored 0.6945. Although the difference of 0.0005 is extremely minor, it has a significant impact on the rank position. **CONCLUSION** The analysis of a competitor's performance is important in enhancing the evaluation of a competitor's efficiency. The participants' performances are expressed in the form of scores and linguistic terms, which include aspects of ambiguity. In this study, the performances of competitors in the Qur’an Reciting Competitions held in Klang in 2013 and 2015 were evaluated using the fuzzy evaluation method. The assessment procedure was effectively done with the aid of the membership function graph and the fuzzy grade sheet for any type of criteria (*Tajweed, Tarannum, Fasohah* and Vocal Quality). Furthermore, the use of linguistic terms will encourage the participants to work harder since every point will be counted in order to achieve the highest level of performance. Hence, this technique which is deemed to be more reliable and practical could be used as an alternative method for assessing the competitors. We believe that the fuzzy evaluation method is able to improve the reliability of the assessment of the competition’s existing procedures, resulting in a better outcome that can distinguish competitors’ abilities based on ranking. **REFERENCES** Abdulhamid, R. (2015). Impact and Challenges of Qur’anic Recitation Competition in the Federal Capital Teritory (Fct) Abuja. *Journal of Islamic Studies and Culture*, *3*(2), 153–161. [https://doi.org/10.15640/jisc.v3n2a16](https://doi.org/10.15640/jisc.v3n2a16) Al-Jazi, A. (2017). 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