Penerapan Algoritma K-Means Dalam Mengclustering Kualitas Bibit Kelapa Sawit Di PPKS Marihat
(1) STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
(2) STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
(3) STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
(*) Corresponding Author
Abstract
Grouping the quality of oil palm seeds needs to be done to find out the groups of types of oil palm seeds that are of good and bad quality so that the process of planting oil palm can be done well too. This study discusses classifying the quality of oil palm seeds by using the K-Means Clustering algorithm as a case resolution solution. The source of data in this study was obtained from the Marihat Oil Palm Research Center (PPKS) with 23 data processing data. The analysis in this study uses 2 (two) cluster levels, namely the good cluster (C1) and the bad cluster (C2). The results obtained are the good clusters are at the age (5-25 months), and the bad clusters are at the age (3-4 months). It is hoped that the results of the research can be input, suggestions, and efforts for PPKS Marihat to improve the quality of oil palm seeds so that they can support a more optimal strategy for producing oil palm seeds. Grouping the quality of oil palm seeds needs to be done to find out the groups of types of oil palm seeds that are of good and bad quality so that the process of planting oil palm can be done well too. This study discusses classifying the quality of oil palm seeds by using the K-Means Clustering algorithm as a case resolution solution. The source of data in this study was obtained from the Marihat Oil Palm Research Center (PPKS) with 23 data processing data. The analysis in this study uses 2 (two) cluster levels, namely the good cluster (C1) and the bad cluster (C2). The results obtained are the good clusters are at the age (5-25 months), and the bad clusters are at the age (3-4 months). It is hoped that the results of the research can be input, suggestions, and efforts for PPKS Marihat to improve the quality of oil palm seeds so that they can support a more optimal strategy for producing oil palm seeds.
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L. Felicia, Penerapan Metode Clustering Dengan K-Means Untuk Memetakan Potensi Tanaman Padi Di Kota Semarang, Pp. 15, 2014.
R. Rosmini, A. Fadlil, And S. Sunardi, Implementasi Metode K-Means Dalam Pemetaan Kelompok Mahasiswa Melalui Data Aktivitas Kuliah, It J. Res. Dev., Vol. 3, No. 1, P. 22, 2018, Doi: 10.25299/Itjrd.2018.Vol3(1).1773.
A. P. Windarto, J. Naam, Y. Yuhandri, A. Wanto, And M. Mesran, Bagian 2: Model Arsitektur Neural Network Dengan Kombinasi K-Medoids Dan Backpropagation Pada Kasus Pandemi Covid-19 Di Indonesia, J. Media Inform. Budidarma, Vol. 4, No. 4, Pp. 11751180, 2020, Doi: 10.30865/Mib.V4i4.2505.
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), J. Media Inform. Budidarma, Vol. 4, No. 3, P. 855, 2020, Doi: 10.30865/Mib.V4i3.2312.
Mardalius, Pemanfaatan Rapid Miner Studio 8.2 Untuk Pengelompokan Data Penjualan Aksesoris Menggunakan Algoritma K-Means, Vol. Iv, No. 2, Pp. 401411, 2018.
I. Gunawan, G. Anggraeni, E. S. Rini, And Y. Mustofa, Klasterisasi Provinsi Di Indonesia Berbasis Perkembangan Kasus Covid-19 Menggunakan Metode K-Medoids, Semin. Nas. Mat. Dan Pendidik. Mat., Pp. 301306, 2020.
F. Nasari And C. J. M. Sianturi, Penerapan Algoritma K-Means Clustering Untuk Pengelompokkan Penyebaran Diare Di Kabupaten Langkat, Cogito Smart J., Vol. 2, No. 2, P. 108, 2018, Doi: 10.31154/Cogito.V2i2.19.108-119.
F. Nurzaman, Penerapan Algoritma K-Means Dalam Pengelompokan Lokasi Rumah Sakit Provider Pada Asuransi Kesehatan, Pp. 6167, 2018.
W. A. Triyanto, Algoritma K-Medoids Untuk Penentuan Strategi Pemasaran Produk, J. Simetris, Vol. 6, No. 1, Pp. 183188, 2015.
L. Iswari And U. I. Indonesia, Pemanfaatan Algoritma K-Means Untuk Pemetaan Hasil, Vol. 21, No. December, 2016, Doi: 10.20885/Teknoin.Vol21.Iss1.Art7.
Haviluddin, F. Agus, M. Azhari, And A. S. Ahmar, Artificial Neural Network Optimized Approach For Improving Spatial Cluster Quality Of Land Value Zone, Int. J. Eng. Technol., Vol. 7, No. 2, Pp. 8083, 2018, Doi: 10.14419/Ijet.V7i2.2.12738.
P. Alkhairi, I. S. Damanik, And A. P. Windarto, Penerapan Jaringan Saraf Tiruan Untuk Mengukur Korelasi Beban Kerja Dosen Terhadap Peningkatan Jumlah Publikasi, Pros. Semin. Nas. Ris. Inf. Sci., Vol. 1, No. September, P. 581, 2019, Doi: 10.30645/Senaris.V1i0.65.
S. Haryati, A. Sudarsono, And E. Suryana, Implementasi Data Mining Untuk Memprediksi Masa Studi Mahasiswa Menggunakan Algoritma C4.5 (Studi Kasus: Universitas Dehasen Bengkulu), J. Media Infotama, Vol. 11, No. 2, Pp. 130138, 2015.
M. L. Sibuea And A. Safta, Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustring, Jurteksi, Vol. 4, No. 1, Pp. 8592, 2017, Doi: 10.33330/Jurteksi.V4i1.28.
S. Takalapeta, Penerapan Data Mining Untuk Menganalisis Kepuasan Konsumen Menggunakan Metode Algoritma C4.5, J I M P - J. Inform. Merdeka Pasuruan, Vol. 3, No. 3, Pp. 3438, 2018, Doi: 10.37438/Jimp.V3i3.186.
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