Penerapan Data Mining Untuk Memprediksi Penjualan Bahan Bakar Minyak Menggunakan Algoritma K-Means Clustering (Studi Kasus: PT. Anugerah Alam Semesta)
-
Rahayul Kahfi
*
Budi Darma,
Indonesia
- Muhammad Syahrizal STMIK Budi Darma, Indonesia (*) Corresponding Author
Abstract
Business is always traversed by competing all companies always thinking of ways to make the company always go forward and the company can continue to survive in developing the business if the company will not decline. The company wants to know the number of sales at PT. Anugerah Alam Semesta Raya to obtain the most popular sales data analysis for the sale of fuel types. One of the factors in clustering the sale of fuel oil is to facilitate the sale of fuel oil by means of grouping. One problem in inventory is the difficulty in determining the large amount of inventory that must be provided in meeting the demand. It often happens that a company has too little inventory compared to consumer demand. Reviewing the company directly regarding the process of selling fuel at the company by processing it by clustering the previous sales data to produce a prediction about further sales in order to be better. Unstable sales will result in a company's precenomian can be weakened with the word another company must obtain a technique in terms of managing sales of fuel oil in the future, so that it can produce a prediction with good accuracy in drawing a conclusion
Downloads
References
Johan Oscar Ong, Jurnal Ilmiah Teknik Industri, vol. 12, p. 1, juni 2013.
Sri Mulyati, Prosiding Seminar Ilmiah Nasional Teknologi Komputer, vol. 1, no. Padang, p. 2, oktober 2015.
Asroni, Jurnal Ilmiah Semesta Teknika, vol. 18, Magelang, p. 3, maret 2015.
Elly muningsih, jurnal bianglala informatika, vol. 3, Yogyakarta, p. 4, maret 2015.
Guntur Setiawan, implementasi dalam birokrasi pembangunan, bandung, p. 5, 2002.
Jiawei dan Kamber, Micheline han, data mining, p. 6, 2006.
Budi Santosa, data mining teknik pemanfaatan data untuk keperluan bisnis, Yogyakarta, p. 7, 2007.
Luthfiq Emha Kusrini, algoritma data mining, Yogyakarta, p. 8, 2009.
Dominikus Juju Feri Sulianta, Data mining meramalkan bisnis perusahaan, jakarta, p. 9, 2010.
Juan santana finn lee, data mining:meramalkan bisnis perusahaan, no. jakarta, p. 11, 2010.
Abu Bakar dan Wibowo Arif, akuntansi keuangan dasar2, jakarta, p. 12, 2008.
Oliviera, pendekatan partisi dan clustering, p. 13, 2007.
Bian S.,et.al Everitt, Cluster Analisys, p. 14, 2011.
Brian S.,et.al Everitt, Cluster Analisys 5th Edition, p. 15, 2011.
Everitt et al, agglomerative clustering, p. 16, 2011.
Adi Nugroho, analisa dan perancangan, no. bandung, p. 17.
Haviluddin, Unified modelling language, p. 18, 2011.
D. P. Utomo and B. Purba, "Penerapan Datamining pada Data Gempa Bumi Terhadap Potensi Tsunami di Indonesia," Prosiding Seminar Nasional Riset Information Science (SENARIS), vol. 1, pp. 846-853, 2019.
R. Amelia and D. P. Utomo, "ANALISA POLA PEMESANAN PRODUK MODERN TRADE INDEPENDENT DENGAN MENEREPAKAN ALGORITMA FP. GROWTH (STUDI KASUS: PT. ADAM DANI LESTARI)," KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 3, no. 1, pp. 416-423, 2019.
E. Buulolo, Data Mining Untuk Perguruan Tinggi. 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Data Mining Untuk Memprediksi Penjualan Bahan Bakar Minyak Menggunakan Algoritma K-Means Clustering (Studi Kasus: PT. Anugerah Alam Semesta)
Pages: 189-198
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).
