Clustering Application for UKT Determination Using Pillar K-Means Clustering Algorithm and Flask Web Framework

Ahmad Luky Ramdani, Hafiz Budi Firmansyah

Abstract


Clustering is one of technique in data mining which has purpose to group data into a cluster. At the end, a cluster will have different data compared with others. This paper discussed about the implementation of clustering technique in determining UKT (Uang Kuliah Tinggal) / Tuition Fee in Indonesia. UKT is a tuition fee where its amount is determined by considering students purchasing power. Most of University in Indonesia often use manual technique in order to classify UKT’s group for each student. Using web-based application, this paper proposed a new approach to automatise UKT’s grouping which leads to give an reasonable recommendation in determining the UKT’s group. Pillar K-Means algorithm had been implemented to conduct data clustering. This algorithm used pillar algorithm to initiate centroid value in K-means algorithm. By deploying students data at Institut Teknologi Sumatera Lampung as case study, the result illustrated that Pillar K-Means and silhouette coefficient value might be adopted in determining UKT’s group

References


Republik Indonesia, Undang-Undang No.12. 2012

Arai K, Barakbah AR. Hierarchical K-means: an algorithm for centroids initialization for K-means. Reports of the Faculty of Science and Engineering, 2007, 36.1: 25-31.

Barakbah AR, Kiyoki Y. A Pillar algorithm for k-means optimization by distance maximization for initial centroid designation. Computational Intelligence and Data Mining, CIDM'09. IEEE Symposium on. IEEE. 2009; 61-6

Barakbah AR, Kiyoki Y. A new approach for image segmentation using Pillar-Kmeans algorithm. World Academy of Science, Engineering and Technology. 2009; 59: 23-28.

Wahyudin I, Taufik D, and Wisnu AK. Cluster analysis for SME risk analysis documents based on Pillar K-Means. TELKOMNIKA (Telecommunication Computing Electronics and Control). 2016; 14(2): 674-683.

Arthur D, Sergei V. k-means++: The advantages of care- ful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027–1035. Society for Industrial and Applied Mathematics, 2007.

Pedregosa F. Scikit-learn: Machine learning in Python. Journal of machine learning research. 2011; 12: 2825-2830.

Kaufman L, Rousseeuw PJ. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New Jersey: John Wiley and Sons

Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics. 1987; 20: 53-65.

http://www.numpy.org/

Scikit-learn. sklearn.metrics.silhouette_score [internet]. [diacu 2018 Mei 10]. Tersedia dari: http://scikit-learn.org/stable/modules/generated/ sklearn.metrics.silhouette_score.html

https://docs.python.org/3/library/pickle.html

Flask Web Framework. Flask (A Python Microframework)[internet]. [diacu 2018 Mei 10]. http://flask.pocoo.org


Refbacks

  • There are currently no refbacks.


Office and Secretariat

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone./ Hp.: +62 852-7535-9942/ +62 852-6370-8907

IJAIDM Stats