The grouping of potential students conducts to determine the student's interest and increase the student's academic performance. The K-Means algorithm could do collection or clusterization. This study aims to implement one of the Machine Learning algorithms, K-Means, to classify the potential of interest grouping of Informatics Engineering student's batch 2019 at the Universitas Muhammadiyah Purwokerto. The process of categorization was based on average course values, which are a part of student specializations, namely 1) Intelligent Systems (IS), 2) Software Engineering (SE), 3) Computer Networks (CN), and 4) Multimedia (MM), as well as student's GPA data (semester 1 to semester 4). Moreover, this research involves the Elbow method for determining the number of optimal clusters and Sum of Squared Errors (SSE) as a cluster validation technique. From the Elbow process, Within Cluster Sum of Squares (WCSS) significantly decreases when K is significantly upwards from 2 to 3, and the SSE maximum rate of change is 71.29 %. Therefore, the optimal cluster is 3. With K-Means clustering results, the majority of the students (62 or 41.05 %) are assigned to the Intelligent System group, the second majority (59 or 39.07 %) to the Multimedia group. At the same time, a cluster of Computer Networks was the group with the fewest members.

1.
Badan Akreditasi Nasional Perguruan Tinggi
, “
Peraturan BAN-PT No 05 Tahun 2019 tentang (Lampiran) Naskah Akademik IAPS 4.0
,”
2019
.
2.
C. C.
Aggarwal
,
Data Mining.
Cham
:
Springer International Publishing
,
2015
.
3.
A. S.
Ahmar
,
D.
Napitupulu
,
R.
Rahim
,
R.
Hidayat
,
Y.
Sonatha
, and
M.
Azmi
, “
Using K-Means Clustering to Cluster Provinces in Indonesia
,” in
Journal of Physics: Conference Series
,
2018
, vol.
1028
, no.
1
, doi: .
4.
D. P.
Ismi
,
S.
Panchoo
, and Murinto, “
K-means clustering based filter feature selection on high dimensional data
,”
Int. J. Adv. Intell. Informatics
, vol.
2
, no.
1
, pp.
38
45
,
2016
, doi: .
5.
Provost & Fawcett
,
Data science-what you need to know about analytic-thinking and decision-making.
O'Reilly Media, Inc
,
2013
.
6.
D.
Jared
,
Big data, data mining, and machine learning [internet resource]: value creation for business leaders and practitioners.
Wiley
,
2014
.
7.
M. Z.
Hossain
,
M. N.
Akhtar
,
R. B.
Ahmad
, and
M.
Rahman
, “
A dynamic K-means clustering for data mining
,”
Indones. J. Electr. Eng. Comput. Sci.
, vol.
13
, no.
2
, pp.
521
526
, 2019, doi: .
8.
M. A.
Syakur
,
B. K.
Khotimah
,
E. M. S.
Rochman
, and
B. D.
Satoto
, “
Integration K-Means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster
,” in
IOP Conference Series: Materials Science and Engineering
,
2018
, vol.
336
, no.
1
, doi: .
9.
N. U.
Roiha
,
Y. K.
Suprapto
, and
A. D.
Wibawa
, “
The optimization of the weblog central cluster using the genetic k-means algorithm
,” in
Proceedings - 2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016
,
2017
, pp.
278
284
, doi: .
10.
E.
Umargono
,
J. E.
Suseno
, and V. G. S. K., “
K-Means Clustering Optimization using the Elbow Method and Early Centroid Determination Based-on Mean and Median
,” in
Proceedings of the International Conferences on Information System and Technology (CONRIST 2019)
,
2019
, pp.
234
240
, doi: .
11.
B. A.
Jaafar
,
M. T.
Gaata
, and
M. N.
Jasim
, “
Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms
,”
Indones. J. Electr. Eng. Comput. Sci.
, vol.
19
, no.
3
, pp.
1635
1642
,
2020
, doi: .
12.
T.
Kansal
,
S.
Bahuguna
,
V.
Singh
, and
T.
Choudhury
, “
Customer Segmentation using K-means Clustering
,” in
Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018
,
2018
, pp.
135
139
, doi: .
13.
T.
Omar
,
A.
Alzahrani
, and
M.
Zohdy
, “
Clustering Approach for Analyzing the Student's Efficiency and Performance Based on Data
,”
J. Data Anal. Inf. Process.
, vol.
08
, no.
03
, pp.
171
182
,
2020
, doi: .
14.
D.
Marutho
,
S. Hendra
Handaka
,
E.
Wijaya
, and Muljono, “
The Determination of Cluster Number at k-Mean Using Elbow Method and Purity Evaluation on Headline News
,” in
Proceedings - 2018 International Seminar on Application for Technology of Information and Communication: Creative Technology for Human Life, iSemantic 2018
,
2018
, pp.
533
538
, doi: .
15.
Y.
Tao
,
J.
Deng
, and
X.
Song
, “
Drug audit based on bisecting k-means clustering algorithm
,” in
Proceedings - 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2019
,
2019
, no.
2
, pp.
265
270
, doi: .
16.
S.
Wang
 et al, “
K-Means Clustering With Incomplete Data
,”
IEEE Access
, vol.
7
, pp.
69162
69171
,
2019
, doi: .
17.
J.
Zeng
,
J.
Wang
,
L.
Guo
,
G.
Fan
,
K.
Zhang
, and
G.
Gui
, “
Cell Scene Division and Visualization Based on Autoencoder and K-Means Algorithm
,”
IEEE Access
, vol.
7
, pp.
165217
165225
,
2019
, doi: .
18.
R.
Nainggolan
,
R.
Perangin-Angin
,
E.
Simarmata
, and
A. F.
Tarigan
, “
Improved the Performance of the K-Means Cluster Using the Sum of Squared Error (SSE) optimized by using the Elbow Method
,”
J. Phys. Conf. Ser.
, vol.
1361
, no.
1
,
2019
, doi: .
19.
W.
Purba
,
S.
Tamba
, and
J.
Saragih
, “
The effect of mining data k-means clustering toward students profile model drop out potential
,”
J. Phys. Conf. Ser.
, vol.
1007
, no.
1
,
2018
, doi: .
20.
T.
Thinsungnoen
,
N.
Kaoungku
,
P.
Durongdumronchai
,
K.
Kerdprasop
, and
N.
Kerdprasop
, “
The Clustering Validity with Silhouette and Sum of Squared Errors
,” in
Proceedings of the 3rd International Conference on Industrial Application Engineering 2015
,
2015
, no. November, pp.
44
51
, doi: .
This content is only available via PDF.
You do not currently have access to this content.