ANALISIS PRODUK TERLARIS MENGGUNAKAN METODE K-MEANS CLUSTERING PADA “PT.SUKANDA DJAYA
DOI:
https://doi.org/10.31000/jika.v5i1.3236Abstrak
Kepentingan Klasterisasi produk menjadi salah satu penentu pengembangan produk sebuah perusahaan dalam aktifitas penjualan. Penelitian lanjut terhadap variable produk terlaris menjadi point penting yang perlu dikembangkan sebaik mungkin. PT Sukanda Djaya. Diamond Cold Storage hari ini. Diketahui adanya kekurangan dalam masalah loading barang yang paling laris untuk di bawa oleh bagian salesman, dan menjadi rekomendasi Karena belum pernah melakukan pengukuran produk terlaris, untuk mengetahui produk mana yang paling laris yang ada di daerah tertentu untuk memudahkan salesman membawa produk yang paling laris agar tidak terjadi penumpukan yang kurang komsumtif. Tujuan dari penelitian ini adalah membantu salesman membawa produk yang laris agar tidak terjadi kesia-siaan dalam membawa produk yang kurang komsumtif. Untuk metode yang digunakan adalah metode Algoritma K means Clustering, Clustering merupakan salah satu teknik dari salah satu fungsionalitas data mining, Algoritma Clustering merupakan Algoritma pengelompokan jumlah data sejumlah data menjadi kelompok-kelompok data tertentu (cluster). Sehingga dengan adanya pengelompokan data ini pihak perusahaan dapat mengetahui barang paling laris, laris dan tidak laris. Sehingga barang yang ada digudang tidak menumpuk. Dari penitian ini output yang dihasilkan yaitu, barang paling laris sebanyak 10, kurang laris sebanyak 4. Dengan adanya pengolahan data yang dilakukan diharapkan dapat memberikan solusi kepada pihak perusahaan agar dapat mengetahui mana barang yang paling laris dan mana barang yang tidak laris.Referensi
Metisen. B. M. and Sari H. L. (2015) Analisis clustering menggunakan metode K-Means dalam pengelompokan penjualan produk pada swalayan fadhila. Jurnal Media Infotama, vol. 11, no. 2, pp. 110–118
Tamba. S. P, Kesuma F. T., and Feryanto. (2019) Penerapan data mining untuk menentukan penjualan sparepart toyota dengan metode K-Means Clustering. Jurnal Sistem Informasi Ilmu Komputer. Orima (JUSIKOM Prima) vol. 2, no. 2, pp. 67–72
Annur. H. (2019) Penerapan Data Mining Menentukan Strategi Penjualan Variasi Mobil Menggunakan Metode K-Means Clustering J. Inform. Upgris, vol. 5, no. 1, 2019, doi: 10.26877/jiu.v5i1.3091
Vhallah. I, Sumijan. S, and Santony. J. (2018) Pengelompokan Mahasiswa Potensial Drop Out Menggunakan Metode Clustering K-Means. J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 2, no. 2, pp. 572–577, 2018, doi: 10.29207/resti.v2i2.308
Haryati. S, Sudarsono. A., and Suryana. E. (2015) 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. 130–138.
Rianti. E (2017) “Data Mining Dalam Menentukan Penjualan Laris Menggunakan Metode Clustering,†KomTekInfo, vol. 4, no. 2, pp. 267–283.http://lppm.upiyptk.ac.id/komtekinfo/index.php/KOMTEKINFO/article/view/128
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with International Journal of Advances in Intelligent Informatics agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.Â
- 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.
Licensing for Data Publication
International Journal of Advances in Intelligent Informatics use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/ (default)
Creative Commons CC-Zero Waiver, http://creativecommons.org/publicdomain/zero/1.0/
Open Data Commons Public Domain Dedication and Licence, http://www.opendatacommons.org/licenses/pddl/1-0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
