Analisis Cluster Ensemble dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kinerja Pembangunan Ekonomi Daerah

Adrian Aqil Yusfar(1*), Muhammad Arif Tiro(2), S. Sudarmin(3),

(1) Program Studi Statistika FMIPA UNM
(2) Program Studi Statistika FMIPA UNM
(3) Program Studi Statistika FMIPA UNM
(*) Corresponding Author




DOI: https://doi.org/10.35580/variansiunm14626

Abstract


Abstract. Cluster analysis or group analysis is an analysis method to classify objects of observation into several groups based on their characteristics. Conventional methods namely Hierarchy and Non-Hierarchy are used in the formation of the initial group. However, the results of the grouping formed had mixed results so that the Cluster Ensemble analysis was then used to obtain a good final grouping. The Cluster Ensemble with the Link-Based Cluster Ensemble approach with the Connected Triple Based Similarity (CTS) method resulted in three final group divisions. The evaluation of the grouping performance used, namely Compactness and Davies-Bouldin, stated that the Cluster Ensemble was better than the hierarchical and non-hierarchical methods. The final group that has been formed is described using the average value for each variable in the district / city in South Sulawesi Province. The first group has the characteristics of regional economic development performance that is better than the second and third groups, but for the third group has the lowest characteristics of regional economic development performance from the first and second groups.

Keywords : Cluster, Cluster Ensemble, Group Performance Evaluation, Performance, Regional Economic


Full Text:

PDF

References


Arsyad, L. (2010). Ekonomi Pembangunan dan Pembangunan Ekonomi. Yogyakarta: UPP STIM YKPN.

Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis Joseph F. Hair Jr. William C. Black Seventh Edition (7th ed.). British Library Cataloguing-in-Publication Data.

Badan Pusat Statistik. (2019). Laporan Perekonomian Indonesia. Jakarta: Badan Pusat Statistik. www.bps.go.id/publication/2019//09/16/laporan-perekonomian-indonesia-2019

Badan Pusat Statistik. (2019). Produk Domestik Regional Bruto Kabupaten/Kota Se Provinsi Sulawesi Selatan Menu. Makassar:

Badan Pusat Statistik Provinsi Sulawesi Selatan. www.sulsel.bps.go.id/publication/ 2019/08/12/produk-domestik-regional-bruto-kabupaten-kota-se-sulawesi-selatan-2014-2018

Badan Pusat Statistik. (2018). Statistik Daerah Provinsi Sulawesi Selatan 2018. Makassar: Badan Pusat Statistik Provinsi Sulawesi Selatan. www.sulsel.bps.go.id/publication/2018/09/26/statistik-daerah-provinsi- sulawesi-selatan-2018

Badan Pusat Statistik. (2019). Statistik Keuangan Pemerintah Daerah Kabupaten/Kota Di Provinsi Sulawesi Selatan 2017/2018. Makassar: Badan Pusat Statistik Provinsi Sulawesi Selatan. www.sulsel.bps.go.id/publication/2019/08/20/statistik-keuangan-pemerintah-daerah-kabupaten-kota-di-provinsi-sulawesi-selatan-2017-2018

Badan Pusat Statistik. (2019). Sulawesi Selatan dalam Angka. Makassar: Badan Pusat Statistik Provinsi Sulawesi Selatan. www.sulsel.bps.go.id/publication/2019/08/16/sulawesi-selatan-dalam-angka-2019

Badan Perencanaan Pembangunan Nasional (BAPPENAS) (2009). Pedoman Evaluasi dan Indikator Kinerja Pembangunan. Jakarta. www.bappenas.go.id

Bejar, J. Unsupervised learning aims the same goal: Consensus clustering or Clustering Ensembel. Consensus Clustering. Diakses pada tanggal 11 Februari 2020. www.cs.upc.edu

Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster Analysis. UK: Wiley.

Hu, X., & Yoo, I. (2004). Cluster Ensemble and Its Apllications in Gene Expression Analysis. 2nd Asia-Pacific Bioninformatics Conference.

Iam-on, N., & Garrett, S. (2010). LinkClue: A MATLAB Package for Link-Based Cluster Ensembles. Journal of Statistical Software

Johnson, R. A., & Winchern, D. W. (2007). Applied Multivariate Statistical Analysis.

Mattjik, A. A., & Sumertajaya, I. M. (2011). Sidik Peubah Ganda (G. N. A. Wibawa & A. F. Hadi, Eds.). Bandung: IPB Press.

Nuraidah, S. (2014). Cluster Ensemble Dalam Penggerombolan Kabupaten/Kota Provinsi Jawa Barat Berdasarkan Indikator Penddikan SMA/SMK/MA. Skripsi: Institut Pertanian Bogor.

Putri, K. R. (2017). Klasifikasi Kabupaten/kota Di Provinsi Jawa Timur Berdasarkan Indikator Kinerja Pembangunan Ekonomi Daerah Dengan Metode Analisis Cluster. Skripsi: Institut Teknologi Sepuluh Nopember

Ristiyanti. (2017). Cluster Ensemble Dalam Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Pelayanan Kesehatan Ibu Hamil. Skripsi: Institut Pertanian Bogor

Strehl, A., & Ghosh, J. (2002). Cluster Ensembles – A Knowledge Reuse Framework for Combining Partitionings.

Sukanto. (2009). Analisis Daya Saing Ekonomi Antar Daerah di Provinsi Sumatera Selatan. Jurnal Ekonomi Pembangunan, 86-102.

Sukirno, S. (2010). Teori Pengantar Makroekonomi Edisi Ketiga. Jakarta: PT. Raja Grasindo Persada.

Supranto. (2004). Analisis Multivariat Arti dan Interpretasi. Jakarta: Rineka Cipta.

Susanto, H. T. (2009). Cluster Analysis. In H. T. Sutanto (Ed.), Cluster Analysis (pp. 978–979). Yogyakarta: Seminar Nasional Matematika dan Pendidikan Matematika.

Tiro, M. A., Sukarna, & Aswi. (2010). Statistika Deskriptif Peubah Banyak. Makassar: Andira Publisher.

Todaro, M. P. (2003). Pembangunan Ekonomi Di Dunia Ketiga. Alih Bahasa: Aminuddin dan Drs. Mursid. Jakarta: Ghalia Indonesia


Article Metrics

Abstract view : 1048 times | PDF view : 422 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Adrian Aqil Yusfar, Muhammad Arif Tiro, Sudarmin

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstracted/Indexed by:

SINTADimensions

 

 

VARIANSI: Journal of Statistics and Its Application on Teaching and Research is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)