Implementasi Algoritma K-Means Clustering dalam Menentukan Blok Tanaman Sawit Paling Produktif

Irfan Maulana Pulungan, Saifullah Saifullah, M Fauzan, Agus Perdana Windarto

Abstract


PTP.Nusantara-IV (Persero) especially in the Marjandi Gardens located in Panombean Panei, Simalungun Regency is an Indonesian state-owned enterprise engaged in the plantation sector, one of which is Oil Palm. Among the many oil palm plantation blocks, there are productive and unproductive oil palm plant blocks. Unproductive blocks affect the profits of Marjandi Gardens in the production of Palm Oil. Therefore, it is necessary to conduct research to find out the most productive and unproductive blocks of Oil Palm Plants. This study was conducted in Afdeling III Marjandi Gardens with oil palm planting years 2005 and 2006. The method used was the K-Means Clustering method, the blocks were divided into 2 Clusters namely High Clusters for the most productive blocks and Low Clusters for unproductive blocks. Based on the research that has been done, the results obtained are the number of 14 blocks of the most productive oil palm plants, namely (BM, BN, BO, BP, BR, BY, CE, CF, CK, CY, DE, DF, DG) and 26 blocks of plants Oil palm is unproductive, namely (BL, BQ, BS, BW, BX, BZ, CA, CB, CD, CG, CL, CM, CN, CO, CP, CQ, CR, CS, CW, CX, DA, DB, DC, DD). It is expected that the company can develop the most productive oil palm block so that the quality of the oil palm plant blocks is always maintained. As for the unproductive oil palm plant blocks, the company can make repairs to produce a productive block of oil palm in the future.

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References


E. Tarwaca and S. Putra, “Tanggapan Produktivitas Kelapa Sawit ( Elaeis guineensis Jacq . ) terhadap Variasi Iklim The Productivities Responses of Oil Palms ( Elaeis guineensis Jacq .) to Variation of Climate Elements,” vol. 4, no. 4, pp. 21–34, 2015.

R. Setiawan and N. Tes, “PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING UNTUK MENENTUKAN STRATEGI PROMOSI MAHASISWA BARU ( Studi Kasus : Politeknik LP3I Jakarta ),” vol. 3, no. 1, pp. 76–92, 2016.

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.

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.

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.

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.

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.

A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” International Journal of artificial intelligence research, vol. 1, no. 2, pp. 26–33, 2017.

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

N. Sirait, “IMPLEMENTASI K-MEANS CLUSTERING PADA PENGELOMPOKAN MUTU BIJI SAWIT ( Studi Kasus : PT . Multimas Nabati Asahan ),” vol. 16, pp. 368–372, 2017.




DOI: http://dx.doi.org/10.30645/senaris.v1i0.39

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