Vol. 5, No. 8, August 2024

E-ISSN: 2723-6692

P-ISSN: 2723-6595

http://jiss.publikasiindonesia.id/

Jurnal Indonesia Sosial Sains, Vol. 5, No. 8, August 2024 1911

KEYWORDS

ABSTRACT

K-means algorithm;

Scholarship; Qur'an

Education Park

The Qur'an Education Park (TPA) plays an important role in

improving children's education in the surrounding environment by

providing facilities for learning to read and write the Quran,

reading short prayers, and other positive activities. However, the

criteria for scholarship recipients at TPA Anak Sholeh are still

unclear, even though the provision of this scholarship aims to

motivate students to be more active in learning and excelling. This

study aims to determine more specific criteria for scholarship

recipients, such as attendance, achievements, home conditions, and

parents' income. Another objective is to validate and improve the

accuracy of the K-Means Clustering model used in grouping the

data of prospective scholarship recipients into three categories:

accepted, considered, and rejected. The research method used is

the K-Means Clustering algorithm. Algorithm performance

evaluation is carried out through metrics such as accuracy,

precision, recall, f-measure, and cross-validation. Testing was

carried out on TPA student data as many as 80 student data. The

results of the grouping using 3 clusters were obtained as many as

46 students received C1 scores, C2 scores were rejected by 12

students and C3 scores were considered for 22 students. The

determination of the centroid distance has an effect on the value of

the cluster results when conducting the scholarship attribute

collateralization.

Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

1. Introduction

TPA (Alqur'an Education Park), one of the educational facilities close to residential areas and

affordable in cost, is in great demand by the community, so many leave their children in landfills.

TPA has an important role in children's education. One of the TPA programs is non-formal

education, which includes reading and writing the Qur'an among children (Nurjayanti et al., 2020).

The Qur'an Education Park (TPA) is a non-formal educational institution that has the leading

position of directing the skills of reading and writing the Qur'an, also functions to increase the

Implementation of K-Means Algorithm for Clustering

Scholarship Candidates at TPA Anak Sholeh

Nurwati, Yudi Santoso

Universitas Budi Luhur, Jakarta, Indonesia

Email: nurwati@budiluhur.ac.id, yudi.santoso@budiluhur.ac.id

Correspondence: nurwati@budiluhur.ac.id*

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Jurnal Indonesia Sosial Sains, Vol. 5, No. 8, August 2024 1912

growth of children's souls and form children's religious character (Guchi & Sunarti, 2021; Wandri et

al., 2023).

As a landfill that was established 8 (eight) years ago, TPA Anak Sholeh has 7 (seven) teachers

and students, totalling approximately 70 (seventy) children with its building as a result of the self-

help of guardians, students, and donors from sharing parties. TPA accepts students from various

walks of life. For 8 (eight) years of the learning process at TPA, many potential students have

excelled in Qur'an reading, da'wah achievements, Qur'an memorization achievements and poetry

humming (marawis). The Sholeh Children's Landfill already has a foundation that oversees the

activities and supports the costs of the learning process. However, the terms and criteria for

prospective scholarship recipients are still unclear: who and what is included in the recipient

criteria. To motivate students at TPA, scholarships are planned to be provided to students who

quickly accept lessons and have the potential to achieve achievements.

The scholarship provides financial assistance to individuals who aim to sustain the education

pursued (Campbell & Neff, 2020; Manihuruk et al., 2020). The purpose of providing scholarships to TPA

Anak Sholeh students is to appreciate the achievements that have been achieved and motivate

students to study more actively at TPA. The scholarship assistance provided is in the form of full

education fees while being a TPA student.

This study uses the K-Means algorithm to determine the criteria for grouping prospective

scholarship recipients. The K-Means method partitions data into groups so that data with the same

characteristics are entered into the same group and data with different characteristics are grouped

into other groups. Data with a restrictive value equation in one group and data with differences in

another group allows for the grouping of different data with a small degree of variation. The main

principle of this technique is to compile K partitions/centroids/mean from a data set. This data

grouping aims to minimize the objective functions set in the grouping process, which generally

seeks to minimize variation within a group and maximize variation between groups (Hoban et al.,

2020; Sulistiyawati & Supriyanto, 2021).

This algorithm groups prospective scholarship recipients into three categories: accepted,

rejected, and considered. The K-Means Clustering Algorithm helps cluster the proposals of

prospective scholarship recipients into categories that are worthy of being proposed and not

worthy of receiving scholarships according to the criteria that have been determined. The K-means

algorithm is used because there has been much research on grouping things using Clustering

(Ahmed et al., 2020; Rahmah & Antares, 2022).

Previous research on the K-means clustering algorithm was used to recommend scholarship

recipients (Khomarudin et al., 2021). The results of the recommendation are in the form of

placement of scholarship applicant data to each cluster group produced. The data used in this study

are Final School Exam Scores (UAS), Report Card Scores, House Status, Home Condition, and

Parents' Income. With a cluster (k=3) with 30 (thirty) scholarship recipients. Through attribute

selection, K-means calculates each data into a predetermined cluster. Of the results of the

calculations that have been processed, as many as 16% are accepted, 61% are considered, and 23%

are rejected (Salam et al., 2020).

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Jurnal Indonesia Sosial Sains, Vol. 5, No. 8, August 2024 1913

2. Materials and Methods

The research stages explain as follows:

1. Collect dataset data,

At this stage, collect the required data sets according to the research problem. The data set

was obtained from the initial student registration documents registering to become students at the

Anak Sholeh's landfill. A multiclass dataset is a unique dataset that has more than two labels. The

performance of a method can be measured through testing curation, precision, recall and f-measure

values, and by applying cross-validation to performance testing, the robustness of the performance

can be seen (Azis et al., 2020).

2. Data processing by specifying,

After compiling the data set in stage 1. The next stage is the selection of the data set by

determining what attributes are used. Data Mining preprocessing can improve the quality of

processed data through the stages of data cleaning, data integration, data selection, and data

transformation. This is done so that the processed data is of higher quality, meaning that the data is

objective, representative, has a small sampling error, is updated and relevant (Alwendi; et al., 2023).

3. Get a new set of data,

The 3rd (three) stage is the result of the 2nd (two) stage, which is to obtain a new set of data

with selected attributes.

4. Algorithm K-means (modelling)

The algorithm for performing K-Means clustering is as follows: (1) Pick K centroid points at

random. (2) Group the data so that K clusters are formed, with the centroid point of each cluster

being the centroid point that was selected previously. (3) Update the centroid point value. (4)

Repeat steps 2 and 3 until the value of the centroid point no longer changes. Data can be grouped

into clusters by calculating the closest distance from data to a centroid point. Minkowski's distance

calculation can be used to calculate the distance between 2 pieces of data (Zuhal, 2022). To carry

out the data processing process at each point of the centre of the cluster, namely with the Euclidean

distance theory formulated (Harahap & Rambe, 2021), as follows:

K-means formula (Sulistiyawati & Supriyanto, 2021)

vij is the centroid or the ith cluster mean for the jth variable

This is the amount of data that is a member of the ith cluster

i,k is the index of the cluster

j is the index of the variable

xkj is the value of the kth data in the cluster for the jth variable.

5. Evaluation

Evaluation is carried out to determine whether all data has gone through the k-means

algorithm. The evaluation was conducted to get the maximum results when testing the data set.

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Data Validation and Testing

1. Data Validation: In data testing, cross-validation is used as the primary method to ensure that

the developed model has consistent performance. This method divides the dataset into subsets,

trains the model on a specific subset, and tests other subsets. This helps minimise model bias

and ensures that the results can be generalized to new data.

2. Data Testing: Data is tested using several evaluation metrics, such as precision, recall, and f-

measure. This metric measures how well the model correctly groups data based on predefined

criteria. In addition, sensitivity analysis was also carried out to measure how sensitive the

model is to small changes in the dataset.

3. Result and Discussion

Stages of collecting data sets

The research data source was obtained from TPA Anak Sholeh's registration documents. The data used

are daily student attendance scores, home status, achievements, parental income from 80 students,

and each student's assignment score.

Data processing by specifying

The data collected is as follows:

Tabel 1Tampilan atribut

Kode

Attribute Name

T03

Value attendance

T04

Home status

T05

Have achievements

T06

Parents' income

T07

Assign grades

Table 1 of the attribute display describes the attributes that will be used in calculating K-

means clustering, namely with the code T03 for the student attendance value at the TPA. Code T04

for the status of the occupied student's house. Code T05 to have achievements. Code T06 for the

student's parent's income. Code T07 for Grades of student assignments while attending lessons at

TPA (the duration of study at TPA is approximately 2 hours per day from Monday to Friday).

The value of each code is shown in Table 2.

Table 2 Attribute Values

No.

Code

Attribute name

Name Value

attribute

Value

1

T04

Home status

Own

0

2

T04

Home status

Contracting

1

3

T05

Have achievements

Achievement

1

4

T05

Have achievements

No Achievement

0

5

T06

Parents' income

<=2.000.000

1

6

T06

Parents' income

>2.000.000

0

Table 2 regarding attribute values. The attribute value view contains 0 and 1 for each attribute.

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Get a new set of data

This stage calculates the weight of each attribute's value. In the previous stage, it was

determined that there are three clusters: accepted, rejected, and considered. The next step is to

analyze the data and then process it with the K-means clustering algorithm. Table 3 of the overall

result display of student data is shown below.

Table 3 Display of Overall Results of Student Data

No.

Student

Name

Attendance

Value

Home Status

Have achievements

Parents'

income

Assign

grades

1

Bagas

100

Contracting

2

1.900.000

100

2

Fauzan

100

Contracting

3

1.000.000

100

3

Syauqi

100

Own

1

4.000.000

100

4

Aisyah

100

Own

1

5.000.000

100

5

Shanum

97

Contracting

1

1.800.000

100

6

Nadia

100

Contracting

2

1.950.000

100

7

Arraya

100

Own

2

5.000.000

100

8

Darrel

89

Contracting

1

1.200.000

100

9

Juan

100

Own

1

3.000.000

100

10

Cantika

98

Contracting

2

1.980.000

100

…

….

…

80

Arjuna

99

Contracting

3

1.890.000

100

Table 3 shows the overall results of this student data that will be processed and grouped into 3

clusters.

Algoritma K-means (modelling)

For the modeling process using the K-means algorithm, a centroid value is generated from the

data obtained with the provision that the desired clustering is 3 (Manihuruk et al., 2020). The

analysis results table is shown in table 4 of the analysis data display below.

Table 4 Display of Analysis Results Data

No.

Student

Name

Attendance

Value

Home Status

Have achievements

Parents'

income

Assign

grades

1

Bagas

100

1

100

2

Fauzan

100

1

100

3

Syauqi

100

0

100

4

Aisyah

100

0

100

5

Shanum

97

1

0

1

100

6

Nadia

100

1

100

7

Arraya

100

0

1

0

100

8

Darrel

89

1

0

1

100

9

Juan

100

0

100

10

Cantika

98

1

100

…

….

…

80

Arjuna

99

1

100

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Table 4 shows the results of the analysis of all the collected data. The initial cluster point is

determined by taking the largest value for the accepted cluster (C1), the smallest value for the

rejected cluster (C2), and the average value for the considered cluster (C3). Table 5 shows the table

of the initial cluster points (Initial centroid values) below.

Table 5 Early centroid values

Cluster

Value

attendance

Home

status

Have

achievements

Parents'

income

Assign

grades

C1

100

1

100

C2

89

0

80

C3

98,4375

0,7875

0,6625

0,725

95,125

Table 5 shows the initial centroid values of K-means processing. Then, using the initial

centroid value, it is obtained into 3 clusters. This cluster process is processed by taking the closest

distance from each processed data. From the student data of 80 students, grouping was obtained in

iterations 1 to 3 clusters. The calculation of the cluster centre distance is shown in Table 6 below.

Table 6 Cluster centre distance calculation

No

Studen

t Name

Attendanc

e Value

Home

Statu

s

Have

achievements

Parents'

income

Assign

grades

Avera

ge

C1

C2

C3

Closest

Distance

1

Bagas

100

1

100

40,6

0

22,9

5,2

0

2

Fauzan

100

1

100

40,6

0

22,9

5,2

0

3

Syauqi

100

0

100

40

1,7

22,8

5,3

1,7

4

Aisyah

100

0

100

40

1,7

22,8

5,3

1,7

5

Shanum

97

1

0

1

100

39,8

3,2

21,6

5,2

3,2

6

Nadia

100

1

100

40,6

0

22,9

5,2

0

7

Arraya

100

0

1

0

100

40,6

1

22,8

5,3

1

8

Darrel

89

1

0

1

100

38,2

11

20,0

10,6

9

Juan

100

0

100

40

2

22,8

5,3

2

10

Cantika

98

1

100

40,2

2

22,0

4,9

2

…

….

…

..

…

80

Arjuna

99

1

100

40,4

1

22,4

5,0

1

Table 6 displays the calculation of the cluster centre distance, obtaining the values of C1, C2,

C3, and the nearest distance. The next step is to group the results shown in Table 7 below.

Table 7 Grouping Results

No

Name

C1

C2

C3

1

Bagas

1

2

Fauzan

1

3

Syauqi

1

4

Aisyah

1

5

Shanum

1

6

Nadia

1

7

Arraya

1

8

Darrel

1

9

Juan

1

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Jurnal Indonesia Sosial Sains, Vol. 5, No. 8, August 2024 1917

10

Cantika

1

…

….

…

….

…

80

Arjuna

1

Total

46

12

22

In table 7, the grouping results obtained that the C1 score was accepted by 46 students, the C2

score was rejected by 12 students and the C3 score was considered for 22 students. Table 7 is

equipped with the graph shown in figure 2 of the grouping result graph below.

Figure 1 Graph of Clustering Results

The study's results using the K-Means algorithm for data grouping at the Sholeh Children's

Landfill have practical implications, including improving the student registration process and

decision-making related to scholarship acceptance.

With accurate data grouping, TPA can more efficiently identify eligible students to receive

scholarships based on predetermined criteria, such as academic achievement and economic

conditions. This allows TPA to distribute assistance to those who need it and have the potential to

achieve further.

Furthermore, this grouping method can also be applied in a broader context, such as in other

educational institutions that want to implement a more structured and data-driven scholarship

selection system. In addition, the results of this grouping can also be used as a reference in

improving the quality of education at TPA, for example, by providing special attention or additional

learning programs for groups of students who are in the category of "considered" or "rejected," so

that they can improve their achievements.

4. Conclusion

Based on the results of the research obtained, it is concluded that the K-means clustering

method can produce recommendations for scholarship recipients by involving 5 attributes, namely

the attribute of attendance value, the attribute of home status, the attribute of having achievements,

the attribute of parental income, and attribute of assignment value. The determination of the

centroid or central point value affects the value of cluster results such as testing the data of the

Sholeh's children TPA students. There are 3 clusters, namely C1 for the cluster accepted, C2 for the

57%

15%

28%

Clustering Result

C1

C2

C3

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cluster rejected and C3 for the cluster considered. This study used 80 student data. The results of

data grouping obtained that the C1 score was accepted by 46 students, the C2 score was rejected by

12 students and the C3 score was considered for 22 students. The results obtained from the

research can be input to TPA Anak Sholeh in providing scholarships using 3 criteria. Research

Recommendations, Specific Scholarship Criteria: Clarify the criteria for scholarship recipients based

on attributes such as attendance, achievements, home conditions, and parental income, to improve

the accuracy of the grouping. This ensures that scholarships are given to those who need it the most

and have maximum potential. Accumulate the K-Means Model: Advanced validation and adjustment

of the number of centroids can improve clustering results. Evaluate the performance of the

algorithm with various metrics to ensure consistency and accuracy of the grouping data.

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