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**HIGHLIGHTS**
- Fuzzy Evaluation Method was used to evaluate Quran Recitation Competition.
- The evaluation is based on four factors, “Tajweed”, “Tarannum”, “Fasohah” and Vocal.
- The sample of this study is from Quran Recitation Competition in Klang, Malaysia
ABSTRACT
*The Quran Recitation Competition evaluates a participant's performance based on several factors, including Tajweed, Tarannum, Fasohah, and Vocal. Participants in the Quran Recitation Competition are usually assigned a point value of 100, with each point representing a linguistic word such as “Exceptional,” “Excellent,” “Fairly Good,” and so on. Evaluating participant performance is seems difficult because it involves human decision-making, which is imprecise, ambiguous, and unpredictable. This study employs the fuzzy evaluation method to assess participant performance at a Quran Recitation 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 would then be computed. At the end, the fuzzy markings with linguistic value would be obtained. The proposed method provides an alternative approach to assessing performance with a reasonable and intelligent evaluation. This method is practical to apply because it can increase the satisfaction of participants and assisting the panels making decisions during the competition.*
*Keywords: Fuzzy evaluation method, membership function, satisfaction level, Quran 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 Quran contains literary something precious that is the same as jewels, and this is one of its miracle signs. The Holy Quran includes all the requirements for guiding and educating people in the social, individual, moral, legal, life and afterlife. The Quran is a masterpiece of its own expectations and a distinctive style. From a literary point of view, it has an inimitable and astonishing style. It also refers to all individuals and makes it desirable for any reader or listener to have a clear and intelligible language (Nayef & Wahab, 2018). In the following Quranic 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 express their love for the Quran among Muslims, many parties have organized Quran recitation competitions at different levels. Quran 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 Recital 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 error. 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 the Quran Recitation Tournament, there were always points that concentrate only on four elements, such as Vocals, tajweed, Fasohah and Tarannum, when the participants, Qari and Qariah recited the Holy Quran during the tournament. Participants perform their own performances in front of a key person. An important person responsible for evaluating the performance of the participant is commonly referred to as judges or panels. These forms of evaluation are recognized and have been applied by most of the Quran Recitation Tournament Committees. However, this assessment approach does not include the best way to measure the participants on the basis of the findings that have been made, since it contains elements of fuzziness. It is so difficult to measure subjective aspects when they are ambiguous. This problem arises because a number of panels will have distinct attitudes, experience and tolerance during the assessment process. The scores obtained which 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, the panels will feel very dissatisfied with the results, once all the participants have scored. Participants will always consider the tournament to be unfair. A more appropriate method is needed to measure the performance of the participants other than the method that is often used to obtain better readings.
In this study, the fuzzy evaluation method is applied to measure the performance of participant in the Quran recital competition. 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 ease 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**
In this section, Fuzzy evaluation method is used to assess the performance of participants in the Quran recitation tournament. The following methodology has been followed in this evaluating procedure.
*Step 1: Normalized the marks*
The marks obtained by each of the student have to be converted to the normalized values. Normalized value is referred to a value in a range of \[0, 1\]. Table 1 tabulates the example marks and the normalized values obtained by a student for all the criteria.
**(1)**
**where (NV) = normalized value for each criterion, (TM) = total marks and (MO) = marks obtained**
| No. |
School Name |
Criteria |
Total Marks |
Obtained |
Normalized Value |
|
SMK JALAN KEBUN |
Tajweed |
40 |
31 |
0.78 |
| 1 |
|
Tarannum |
25 |
8 |
0.32 |
|
|
Fasohah |
20 |
11 |
0.55 |
|
|
Vocal |
15 |
9 |
0.60 |
|
SMK
MERU |
Tajweed |
40 |
31 |
0.78 |
| 10 |
|
Tarannum |
25 |
10 |
0.40 |
|
|
Fasohah |
20 |
8 |
0.40 |
|
|
Vocal |
15 |
10 |
0.67 |
Table 1: An example of mark and normalized value
*Step 2: Developed 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) in ranges from 0 to 1.
Table 2: Standard satisfaction level and the corresponding degree 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: Calculate the degree of satisfaction*
In this step, the degree of satisfaction which 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: Evaluate the Final mark*
For the final step, the final scores or marks shall be 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**
As an illustration, the example of marks for a student is taken from table 1. Figure 1 shows the membership function graph that is produced to carry out the fuzzification procedure in step 2.
Figure 1: Membership functions for satisfaction level of Quran Recitation 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 criteria. The degree of satisfaction with criteria 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 method 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 method 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 method 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 transformed 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 fuzzy approach’s linguistic terms are more detailed because it includes 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 non-fuzzy method’s typical final mark. In other words, using satisfaction levels, this approach can be used to compare the performances of participants who have the same final linguistic terms. Table 5 shows 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 criteria, other three criteria have a different normalized values in table 1. In the second and final criteria, participant 10 has a higher normalized value than participant 1. Thus both participants have almost a different degree of satisfaction in each criterion with the support of the fuzzy grade sheet. Table 4 shows the final mark for participants 1 is 0.6940 while participant 10 scores 0.6945. Although the different 0.0005 is extremely minor, it has a significant impact on the rank position.
**CONCLUSION**
Evaluation or analysis of a competitor's performance is important for enhancing 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 performance of competitors in the Quran Recitation Competitions held in Klang in 2013 and 2015 was evaluated using a fuzzy evaluation method. The assessment procedure is helpful with the membership function graph and the fuzzy grade sheet for any type of criterion (Tajweed, Tarannum, Fasohah and Vocal). 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 could be used as an alternative method for assessing the competition, which could be more relevant and practical. We believed that the fuzzy evaluation method is able to improve the assessment of the competition’s regular procedures, resulting in a better outcome that can distinguish competitors’ rankings.
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