PERBANDINGAN PENGOLAHAN DATA PREDIKSI PERSEDIAAN GAS LPG 3KG MENGGUNAKAN REGRESI LINIER BERGANDA DAN K-MEANS

Annisa Rismanitanti, Rima Mawarni, Sidik Rahmatullah, Dwi Marisa Efendi, Sulis Nurbaiti

Abstract


he oil and natural gas sector is a sector that is used with great importance for Indonesia's national development. An interesting commodity to watch out for in the oil and gas industry is liquefied petroleum gas (LPG). LPG is a hydrocarbon gas that has been liquefied under pressure to facilitate storage, transportation, and handling and the main ingredients consist of propane/C3, butane/C4 or can be mixed to produce mixed LPG..

At this time PT. BLORA MUSTIKA does not focus on when household needs increase and when not, the meaning of this is that LPG gas data is not used properly and is only recorded, this of course makes PT BLORA MUSTIKA unable to predict demand from sub-distributors and results in frequent an empty supply of LPG gas causing difficulties for the community to obtain 3 Kg LPG gas. This problem can be calculated and compared with the Multiple Linear Regression and K-Means methods.

By using the Multiple Linear Regression and K-Means method, it is hoped that it will make it easier for PT. BLORA MUSTIKA in determining demand predictions from sub-distributors so that there is no shortage of LPG gas supplies and which method can be obtained which is more effective and efficient.


Keywords


Data Mining, Multiple Linear Regression, K-Means

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References


P. Purwadi, P. S. Ramadhan, and N. Safitri, “Penerapan Data Mining Untuk Mengestimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Deli Serdang,” J. SAINTIKOM (Jurnal Sains Manaj. Inform. dan Komputer), vol. 18, no. 1, p. 55, 2019, doi: 10.53513/jis.v18i1.104.

Y. R. Amalia, “Penerapan data mining untuk prediksi penjualan produk elektronik terlaris menggunakan metode k-nearest neighbor,” 2018.

I. L. L. Gaol, S. Sinurat, and E. R. Siagian, “Implementasi Data Mining Dengan Metode Regresi Linear Berganda Untuk Memprediksi Data Persediaan Buku Pada Pt. Yudhistira Ghalia Indonesia Area Sumatera Utara,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 3, no. 1, pp. 130–133, 2019, doi: 10.30865/komik.v3i1.1579.

M. Faid, M. Jasri, and T. Rahmawati, “Perbandingan Kinerja Tool Data Mining Weka dan Rapidminer Dalam Algoritma Klasifikasi,” Teknika, vol. 8, no. 1, pp. 11–16, 2019, doi: 10.34148/teknika.v8i1.95.

D. T. Larose and C. D. Larose, Discovering Knowledge in Data: an Introduction to Data Mining, vol. 100, no. 472. 2005. doi: 10.1198/jasa.2005.s61.

F. M. Alith, “Klasterisasi Proses Seleksi Pemain Menggunakan Algoritma K-Means (Study Kasus : Tim Hockey Kabupaten Kendal),” Jur. Tek. Inform. FIK UDINUS, vol. 1, no. 1, pp. 1–5, 2015.

A. ett all Wanto, Data Mining : Algoritma dan Implementasi. Jakarta: Kita Menulis, 2020. [Online]. Available: https://openlibrary.telkomuniversity.ac.id/pustaka/164835/data-mining-algoritma-dan-implementasi.html

A. R. Isnain, J. Supriyanto, and M. P. Kharisma, “Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 15, no. 2, p. 121, 2021, doi: 10.22146/ijccs.65176.

D. Alita, I. Sari, and A. Rahman Isnain, “Penerapan Naïve Bayes Classifier Untuk Pendukung Keputusan Penerima Beasiswa,” Jdmsi, vol. 2, no. 1, p. 702022, 2021.




DOI: https://doi.org/10.30873/ji.v22i2.3376


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