Penerapan Data Mining Dalam Mengelompokkanm Jumlah Usaha Berdasarkan Provinsi Menggunakan K-Means Clustering

Rahel Adelina Hutasoit, M. Safii, Iin Parlina

Abstract


Industrial revolution 4.0 is an era where technology in the IT field is growing very rapidly, it can also be said a new era for entrepreneurs in Indonesia that must be a golden opportunity to improve business performance and opportunities for millennials to enter the business or business world. In this era people are expected to be able to compete especially in the business world. This study discusses the Application of Data Mining in Grouping the Number of Enterprises by Province Using K-Means Clustering. The source of this research data is collected based on the information documents of the number of businesses in Indonesia produced by the National Statistics Agency. The data used in this study are provincial data consisting of 34 provinces. Data will be processed by clustering in 3 clusters, namely cluster of high number of businesses, cluster of medium number of businesses and cluster of low number of businesses. The results obtained from the assessment process are based on the index of the number of businesses with 4 provinces, the number of high businesses, namely North Sumatra, West Java, Central Java, and East Java, 13 Provinces, the number of medium enterprises and 17 other provinces, including low business numbers. This can be used as input to the community, especially in provinces where the number of businesses is low so they can compete in the business world and input for the government to provide facilities and infrastructure to support entrepreneurs in Indonesia.

Full Text:

PDF

References


I. Parlina, A. P. Windarto, A. Wanto, and M. R. Lubis, “Memanfaatkan Algoritma K-Means dalam Menentukan Pegawai yang Layak Mengikuti Asessment Center untuk Clustering Program SDP,” CESS (Journal of Computer Engineering System and Science), vol. 3, no. 1, pp. 87–93, 2018.

A. P. Windarto, P. Studi, S. Informasi, and D. Mining, “Penerapan Data Mining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering,” vol. 16, no. 4, pp. 348–357, 2017.

M. G. Sadewo, A. P. Windarto, and S. R. Andani, “PEMANFAATAN ALGORITMA CLUSHTERING DALAM MENGELOMPOKKAN JUMLAH DESA / KELURAHAN YANG MEMILIKI SARANA KESEHATAN,” vol. I, pp. 124–131, 2017.

P. Bidang, K. Sains, Y. Mardi, J. Gajah, M. No, and S. Barat, “Jurnal Edik Informatika Data Mining : Klasifikasi Menggunakan Algoritma C4 . 5 Data mining merupakan bagian dari tahapan proses Knowledge Discovery in Database ( KDD ) . Jurnal Edik Informatika.”

S. Kasus, U. Dehasen, S. Haryati, A. Sudarsono, and E. Suryana, “IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MASA STUDI MAHASISWA MENGGUNAKAN ALGORITMA C4 . 5,” vol. 11, no. 2, pp. 130–138, 2015.

S. Defiyanti, M. Jajuli, T. Informatika, F. Ilmu, K. Universitas, and S. Karawang, “IMPLEMENTASI ALGORITMA K-MEANS DALAM,” vol. I, no. 2, pp. 62–68, 2015.

B. M. Metisen and H. L. Sari, “ANALISIS CLUSTERING MENGGUNAKAN METODE K-MEANS DALAM PENGELOMPOKKAN PENJUALAN PRODUK PADA SWALAYAN FADHILA,” vol. 11, no. 2, pp. 110–118, 2015.

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. 12089, pp. 1–8, 2018.

M. G. Sadewo, A. P. Windarto, and A. Wanto, “Penerapan Algoritma Clustering dalam Mengelompokkan Banyaknya Desa/Kelurahan Menurut Upaya Antisipasi/ Mitigasi Bencana Alam Menurut Provinsi dengan K-Means,” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 2, no. 1, pp. 311–319, 2018.

R. W. Sari, A. Wanto, and A. P. Windarto, “Implementasi Rapidminer dengan Metode K-Means (Study Kasus : Imunisasi Campak pada Balita Berdasarkan Provinsi),” KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 2, no. 1, pp. 224–230, 2018.

M. Anggara, H. Sujiani, and H. Nasution, “Pemilihan Distance Measure Pada K-Means Clustering Untuk Pengelompokkan Member Di Alvaro Fitness,” vol. 1, no. 1, pp. 1–6, 2016.

M. G. Sadewo, A. P. Windarto, and D. Hartama, “Penerapan Datamining Pada Populasi Daging Ayam Ras Pedaging Di Indonesia Berdasarkan Provinsi Menggunakan K-Means,” InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), vol. 2, no. 1, pp. 60–67, 2017.




DOI: http://dx.doi.org/10.30645/senaris.v1i0.102

Refbacks

  • There are currently no refbacks.


&nbsp