Pengelompokan Data Janjang Panen Kelapa Sawit Menggunakan Algoritma K-Medoids Pada PT SIR MANDAU
Grouping of Palm Oil Harvesting Dates Using the K-Medoids Algorithm at PT SIR MANDAU
-
Lila Agustini
*
STIKOM Tunas Bangsa, Pematangsiantar,
Indonesia
- Sumarno STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
- Ika Okta Kirana STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Abstract
Oil palm plantations are almost spread throughout Indonesia. Oil palm is also a tropical plant belonging to the palm family and comes from Africa. Oil palm is a plant with high economic value because it is an oil-producing plant, likewise to Indonesia, one of the producers of palm oil. This research uses data mining techniques in processing or grouping data with the k-medoids clustering method. The k-medoids method is a clustering method that functions to break the dataset into several groups. The advantage of this method is that it can overcome the weakness of the k-means method, which is sensitive to outliers. Another advantage of this method is that the results of the clustering process do not depend on the order in which the dataset is entered. The k-medoids clustering method can be applied to data on oil palm harvest yields based on high, medium, and low yields so that harvest groupings can be known based on these data. It is hoped that this research can provide information to employees about the grouping of oil palm harvesting data, which impacts oil palm yields in the future.
References
G. Abdillah et al., “Penerapan Data Mining Pemakaian Air Pelanggan Untuk Menentukan Klasifikasi Potensi Pemakaian Air Pelanggan Baru Di Pdam Tirta Raharja Menggunakan Algoritma K-Means,” Sentika 2016, vol. 2016, no. Sentika, pp. 18–19, 2016.
Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Inform., vol. 2, no. 2, pp. 213–219, 2017, doi: 10.22202/ei.2016.v2i2.1465.
K. Fatmawati and A. P. Windarto, “Data Mining: Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue (Dbd) Berdasarkan Provinsi,” Comput. Eng. Sci. Syst. J., vol. 3, no. 2, p. 173, 2018, doi: 10.24114/cess.v3i2.9661.
D. F. Pramesti, Lahan, M. Tanzil Furqon, and C. Dewi, “Implementasi Metode K-Medoids Clustering Untuk Pengelompokan Data,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 9, pp. 723–732, 2017, doi: 10.1109/EUMC.2008.4751704.
N. Pulungan, S. Suhada, and D. Suhendro, “Penerapan Algoritma K-Medoids Untuk Mengelompokkan Penduduk 15 Tahun Keatas Menurut Lapangan Pekerjaan Utama,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 329–334, 2019, doi: 10.30865/komik.v3i1.1609.
S. Santoso and R. Nurmalina, “Perencanaan dan Pengembangan Aplikasi Absensi Mahasiswa Menggunakan Smart Card Guna Pengembangan Kampus Cerdas (Studi Kasus Politeknik Negeri Tanah Laut),” J. Integr., vol. 9, no. 1, pp. 84–91, 2017.
M. Silalahi, “Analisis Clustering Menggunakan Algoritma K-Means Terhadap Penjualan Produk Padapt Batamas Niaga Jaya,” Comput. Based Inf. Syst. J., vol. 02, pp. 20–35, 2018.
S. Sindi, W. R. O. Ningse, I. A. Sihombing, F. Ilmi R.H.Zer, and D. Hartama, “Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia,” Jti (Jurnal Teknol. Informasi), vol. 4, no. 1, pp. 166–173, 2020.
S. Sundari, I. S. Damanik, A. P. Windarto, H. S. Tambunan, J. Jalaluddin, and A. Wanto, “Analisis K-Medoids Clustering Dalam Pengelompokkan Data Imunisasi Campak Balita di Indonesia,” Pros. Semin. Nas. Ris. Inf. Sci., vol. 1, no. September, p. 687, 2019, doi: 10.30645/senaris.v1i0.75.
D. Ardiansyah and W. Walim, “Algoritma c4.5 untuk klasifikasi calon peserta lomba cerdas cermat siswa smp dengan menggunakan aplikasi rapid miner,” J. Inkofar, vol. 1, no. 2, pp. 5–12, 2018.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
