KLASTERISASI PENDUDUK LANJUT USIA SUMATERA SELATAN MENGGUNAKAN ALGORITMA K-MODES

Fithri Selva Jumeilah(1), Dicky Pratama(2),


(1) Prodi Sistem Informasi STMIK Global Informatika Palembang
(2) Prodi Sistem Informasi STMIK Global Informatika Palembang
Corresponding Author

Abstract


Currently, Indonesia is included in a country with a population of old structures because of its advanced population of more than 7% of the total population and 2% comes from southern Sumatra. The large number of elderly citizens required a special government policy to formulate policies and special programs the population can use to alleviate the community. To help local government of South Sumatera government to determine the policy and program hence needed clustering elderly population by using K-mode algorithm existing in R-Studio. This study uses population census data of South Sumatera in 2010 obtained from Bapan Pusat Statistik with 47,358 data sample. From the results of this study made 4 clusters: K1 16244 people, K2 6061 people, K3 18681 people, and K4 6372 people. K1 is an elderly group of mostly men who live in the village and still work in agriculture and plantations. K2 is a cluster of women who still work and live in the village. The third K3 cluster is an elderly unemployed group that mostly lives in the city and 25% lives alone. The last K4 is a cluster of women who do not work anymore, live in the village and 73% illiterate. With the cluster the government can determine what is most appropriate for each cluster.

Keywords


Clustering, K-modes, Population Census, Elderly, South Sumatera.

References


Badan Pusat Statistik Suamtera Selatan, (2011). Statistik Penduduk Lanjut Usia Sumatera Selatan 2010. Palembang: Badan Pusat Statistik.

Badan Pusat Statistik Suamtera Selatan, (2016). Statistik Penduduk Lanjut Usia Sumatera Selatan 2015. Palembang: Badan Pusat Statistik.

Melpa, B., dan Latipa, H., (2015). Analisis Clustering Menggunakan Metode K-means dalam Pengelompokan Penjualan Produk pada Swalayan Fadhila. Jurnal Media Infotama, Vol.11, No.2, pp.110-118.

Asroni, and Andrian, R., (2015). Penerapan Methode K-means untuk Clustering Mahasiswa Berdasarkan Nilai Akademik dengan Weka Interface Studi Kasus Jurusan Teknik Informatika UMM Magelang. Jurnal Ilmiah Semesta Teknika, Vol. 18, No.1, pp.76-82.

Rajagopal, Sankar. (2011). Customer Data Clustering Using Data Mining Technique. International Journal of Database Management Systems (IJDMS), Vol.3, No.4, pp. 1-11.

Fitrianah, D. et al., (2016). A Data Mining based Approach for Determining the Potential Fishing Zones. International Journal of Information and Education Technology, Vol. 6, pp. 187-191.

Aranganayagi, S., and Thangavel, K., (2009). Improved K-Modes for Categorical Clustering Using Weighted Dissimilarity Measure. International Journal of Computer, Electrical, Automation, Control and Information Engineering, Vol.3, No.3, pp. 729-735.

Han, J., and Kamber, M., (2006). Data Mining: Concepts and Techniques 2nd. United States of America: Elsevier.

Xiang, Z., and Zahidul, M., (2014). Hartigan’s Method for K-modes Clustering and Its Advantages. Proceedings of the Australasian Data Mining Conference, Vol.158.

Huang, Z., (1997) A Fast Clustering Algorithm to cluster Very Large Categorical Datasets in Data Mining”, In Proc. SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery.

Zhou, Zhang, and Liu, (2017). A Global-Relationship Dissimilarity Measure for the k-Modes Clustering Algorithm. Computational Intelligence and Neuroscience, Vol. 2017.


Full Text: PDF

Article Metrics

Abstract View : 619 times
PDF Download : 323 times

DOI: 10.56327/jurnaltam.v8i2.535

Refbacks

  • There are currently no refbacks.