A Oil Palm Harvest Grouping Using K-Medoids Algorithm
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Published:
Sep 30, 2021
Keywords:
Oil Palm , Janjang, Data Mining , K-Medoids , RapidMiner
Section
Computer Science
Statistics Article
Article View : 164 Times
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Abstract
Oil palm (Elaies Guinnnsiss Jacq) is one of the important industrial crops producing cooking oil, industrial oil, and fuel. Indonesia is the largest palm oil producer in the world. The rest of the processing of oil palm fruit is called janjang. Janjang also serves to be used as compost. The data that is processed in this research is the harvest data at PT. Surya Intisariraya Mandau. Data mining is the process of looking for patterns or information in selected data using certain techniques or methods. The processing steps are grouped using the K-Medoids method and then the data will be processed using RapidMiner tools. Where this grouping is done to minimize the amount of similarity of data and appropriate so that it becomes more valid data. This study aims to simplify the grouping of harvest data based on high, medium and low clusters.
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How to Cite
Angraini, D. D. ., Safii, M. ., & Anggraini, F. . (2021). A Oil Palm Harvest Grouping Using K-Medoids Algorithm. International Journal of Basic and Applied Science, 10(2), 60–68. https://doi.org/10.35335/ijobas.v10i2.56
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J. A. Widians, M. Taruk, Y. Fauziah, and H. J. Setyadi, “Decision Support System on Potential Land Palm Oil Cultivation using Promethee with Geographical Visualization,” J. Phys. Conf. Ser., vol. 1341, no. 4, pp. 0–9, 2019, doi: 10.1088/1742-6596/1341/4/042011.
A. Sequiño and J. Avenido, “The World’s Leader in the Palm Oil Industry: Indonesia,” IAMURE Int. J. Ecol. Conserv., vol. 13, Jan. 2015, doi: 10.7718/ijec.v13i1.1074.
D. Khatiwada, C. Palmén, and S. Silveira, “Evaluating the palm oil demand in Indonesia: production trends, yields, and emerging issues,” Biofuels, vol. 12, no. 2, pp. 135–147, 2021, doi: 10.1080/17597269.2018.1461520.
N. Khan, M. A. Kamaruddin, U. U. Sheikh, and Y. Yusup, “Oil Palm and Machine Learning : Reviewing One Decade of,” pp. 1–26, 2021.
M. S. M. Kassim, W. I. W. Ismail, A. R. Ramli, and S. K. Bejo, “Image Clustering Technique in Oil Palm Fresh Fruit Bunch (FFB) Growth Modeling,” Agric. Agric. Sci. Procedia, vol. 2, pp. 337–344, 2014, doi: 10.1016/j.aaspro.2014.11.047.
V. Issue, N. S. Astuti, I. Karim, and M. Arafat, “Anjoro : International Journal of Agriculture and Business Contribution of Oil Palm ( Elaeis guineensis J .) Plantations to Farmers ’ Income in West Sulawesi,” vol. 1, no. 2, pp. 45–51, 2020, doi: 10.31605/anjoro.v1i2.892.
Y. I. Febiola, I. Cholissodin, and C. Dewi, “Peramalan Hasil Panen Kelapa Sawit Menggunakan Metode Multifactors High Order Fuzzy Time Series yang Dioptimasi dengan K-Means Clustering ( Studi Kasus : PT . Sandabi Indah Lestari Kota Bengkulu ),” vol. 3, no. 12, 2019, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/6841.
E. F. Himmah, M. Widyaningsih, and M. Maysaroh, “Identifikasi Kematangan Buah Kelapa Sawit Berdasarkan Warna RGB Dan HSV Menggunakan Metode K-Means Clustering,” J. Sains dan Inform., vol. 6, no. 2, pp. 193–202, 2020, doi: 10.34128/jsi.v6i2.242.
E. Fauzi et al., “Cluster Analysis of Determining the Location of Oil Palm Replanting in Mukomuko District,” AGRITROPICA J. Agric. Sci., vol. 3, no. 1, pp. 38–45, 2020, doi: 10.31186/j.agritropica.3.1.38-45.
D. F. Pasaribu, I. S. Damanik, E. Irawan, Suhada, and H. S. Tambunan, “Memanfaatkan Algoritma K-Means Dalam Memetakan Potensi Hasil Produksi Kelapa Sawit PTPN IV Marihat,” BIOS J. Teknol. Inf. dan Rekayasa Komput., vol. 2, no. 1, pp. 11–20, 2021, doi: 10.37148/bios.v2i1.17.
V. Marriboyina and L. C. Reddy, “A Review on Data mining from Past to the Future,” Int. J. Comput. Appl., vol. 15, Feb. 2011, doi: 10.5120/1961-2623.
I. B. A. Peling, I. N. Arnawan, I. P. A. Arthawan, and I. G. N. Janardana, “Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm,” Int. J. Eng. Emerg. Technol., vol. 2, no. 1, p. 53, 2017, doi: 10.24843/ijeet.2017.v02.i01.p11.
A. Palmer, R. Jiménez, and E. Gervilla, “Data Mining: Machine Learning and Statistical Techniques,” 2011, pp. 373–396.
D. F. Pramesti, 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.
V. A. P. Sangga, “Perbandingan Algoritma K-Means dan Algoritma K-Medoids dalam Pengelompokan Komoditas Peternakan di Provinsi Jawa Tengah Tahun 2015,” Tugas Akhir Jur. Stat. Fak. Mat. dan Ilmu Pengetah. Alam Univ. Islam Inndonesia Yogyakarta, vol. 53, no. 9, pp. 1689–1699, 2018.
A. D. Andini and T. Arifin, “Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung,” J. RESPONSIF Ris. Sains …, vol. 2, no. 2, pp. 128–138, 2020, [Online]. Available: http://ejurnal.ars.ac.id/index.php/jti/article/view/247.
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,” J. Terap. Sains Teknol., vol. 1, no. 3, pp. 54–69, 2019.
A. Y. Rofiqi, “CLUSTERING BERITA OLAHRAGA BERBAHASA INDONESIA MENGGUNAKAN METODE K-MEDOID,” vol. 6, no. 1, 2017.
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. 301–306, 2020.
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.
I. Werdiningsih, B. Nuqoba, and Muhammadun, Data Mining Menggunakan Android, Weka, Dan Spss.pdf. Jawa Timur: Airlangga University Press, 2020.
M. North, Data Mining for the Masses. 2012.
B. G. Sudarsono, M. I. Leo, A. Santoso, and F. Hendrawan, “Analisis Data Mining Data Netflix Menggunakan Aplikasi Rapid Miner,” JBASE - J. Bus. Audit Inf. Syst., vol. 4, no. 1, pp. 13–21, 2021, doi: 10.30813/jbase.v4i1.2729.
Ardiyansyah, P. A. Rahayuningsih, and R. Maulana, “Analisis Perbandingan Algoritma Klasifikasi Data Mining Untuk Dataset Blogger Dengan Rapid Miner,” J. Khatulistiwa Inform., vol. VI, no. 1, pp. 20–28, 2018.
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