Pemetaan Tingkat Kemiskinan di Provinsi Jawa Tengah Berdasarkan Kabupaten/Kota dengan Metode K-Medoids

Authors

  • Fitriani Dwi Ratna Sari Politeknik Dharma Patria, Kebumen, Jawa Tengah
  • Sotya Partiwi Ediwijojo Politeknik Dharma Patria, Kebumen, Jawa Tengah

DOI:

https://doi.org/10.30865/mib.v5i4.3278

Keywords:

Clustering, Mapping, Poverty, Central Java, Regency/City, K-Medoids

Abstract

Poverty describes a condition of lack of ownership and low income, or in more detail describes a condition that basic human needs cannot be fulfilled, namely food, shelter, and clothing. In the last ten years, Central Java's poverty reduction performance has had its ups and downs, with rural poverty still dominating. The purpose of this research is to conduct a mapping analysis in the form of clusters on the number of poverty levels in districts or cities in the province of Central Java using artificial intelligence techniques. Given that Central Java is the third most populous province after West Java and East Java. This needs to be done in order to obtain a macro picture of the poverty level over the last few years through regional mapping. The dataset used is sourced from the Central Java Statistics Agency (BPS) website on the subject of the number of poor people (thousands of people) in 2017-2019. The solution given in conducting mapping in the form of clusters is the K-Medoids method which is part of clustering data mining. The number of clusters used are high and low clusters in mapping the number of poverty levels. The mapping analysis process uses the help of RapidMiner software. The results showed that 6 provinces (17%) were in the high cluster and 29 provinces (83%) were in the low cluster. The final centroid values for each cluster are {293.2, 309.2, 343.5} in the high cluster (cluster_1) and {18.6, 19.4, 20.1} in the low cluster (cluster_0). The results of the mapping can be useful information for tackling the poor where the high cluster (cluster_1) is a priority for the government in the province of Central Java, namely Cilacap Regency, Banyumas Regency, Kebumen Regency, Grobogan Regency, Pemalang Regency, Brebes Regency

References

A. Wanto, “Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan Pada Kabupaten/Kota Di Provinsi Riau,†Kumpulan JurnaL Ilmu Komputer (KLIK), vol. 05, no. 01, pp. 61–74, 2018.

A. Wanto and J. T. Hardinata, “Estimations of Indonesian poor people as poverty reduction efforts facing industrial revolution 4.0,†IOP Conference Series: Materials Science and Engineering, vol. 725, no. 1, pp. 1–8, 2020.

A. Wanto and J. T. Hardinata, “Estimasi Penduduk Miskin di Indonesia Sebagai Upaya Pengentasan Kemiskinan dalam Menghadapi Revolusi Industri 4.0,†CESS (Journal of Computer Engineering System and Science), vol. 4, no. 2, pp. 198–207, 2019.

BPS, “Jumlah Penduduk Miskin Menurut Provinsi (Ribu Jiwa), 2019-2020,†Badan Pusat Statistik Indonesia, 2020. [Online]. Available: https://www.bps.go.id/indicator/23/185/1/jumlah-penduduk-miskin-menurut-provinsi.html. [Accessed: 25-Oct-2020].

BPS, “Kemiskinan 2017-2019,†Badan Pusat Statistik Provinsi Jawa Tengah, 2020. [Online]. Available: https://jateng.bps.go.id/indicator/23/34/1/kemiskinan.html. [Accessed: 25-Oct-2020].

A. P. Windarto, U. Indriani, M. R. Raharjo, and L. S. Dewi, “Bagian 1: Kombinasi Metode Klastering dan Klasifikasi (Kasus Pandemi Covid-19 di Indonesia),†Jurnal Media Informatika Budidarma, vol. 4, no. 3, p. 855, 2020.

F. Rahman, I. I. Ridho, M. Muflih, S. Pratama, M. R. Raharjo, and A. P. Windarto, “Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination Country Application of Data Mining Technique using K-Medoids in the case of Export of Crude Petroleum Materials to the Destination C,†in IOP Conference Series: Materials Science and Engineering PAPER, 2020, pp. 1–6.

B. Wira, A. E. Budianto, and A. S. Wiguna, “Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Studi Mahasiwa Baru Tahun 2018 Di Universitas Kanjuruhan Malang,†Rainstek, vol. 1, no. 3, pp. 54–69, 2019.

P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. London: Pearson Education, 2006.

S. Sudirman, A. P. Windarto, and A. Wanto, “Data Mining Tools | RapidMiner : K-Means Method on Clustering of Rice Crops by Province as Efforts to Stabilize Food Crops In Indonesia,†IOP Conference Series: Materials Science and Engineering, vol. 420, no. 012089, pp. 1–8, 2018.

A. M. Hemeida, S. Alkhalaf, A. Mady, E. A. Mahmoud, M. E. Hussein, and A. M. Baha Eldin, “Implementation of nature-inspired optimization algorithms in some data mining tasks,†Ain Shams Engineering Journal, pp. 1–10, 2019.

D. Aprilla, D. A. Baskoro, L. Ambarwati, and I. W. S. Wicaksana, Belajar Data Mining Dengan Rapid Minner. 2013.

D. Nofriansyah and G. W. Nurcahyo, Algoritma Data Mining Dan Pengujian. Yogyakarta: Deepublish, 2015.

U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases,†AI Magazine, vol. 17, no. 3, pp. 37–54, 1996.

D. T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining: Second Edition. New Jersey: John Wiley & Sons, 2005.

J. Han and M. Kamber, Data Mining : Concepts and Techniques Second Edition. San Francisco: Elsevier, 2006.

I. H. Witten, E. Frank, and M. A. Hall, Data Mining : Practical Machine Learning Tools and Techniques Third Edition. Burlington: Elsevier, 2011.

A. Wanto et al., Data Mining : Algoritma dan Implementasi. Yayasan Kita Menulis, 2020.

X. Jin and J. Han, “K-Medoids Clustering,†Encyclopedia of Machine Learning and Data Mining, pp. 697–700, 2011.

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Published

2021-10-26

Issue

Vol. 5 No. 4 (2021): Oktober 2021

Section

Articles

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