ANALISIS DATA PENJUALAN PRODUK PAKAIAN MENGGUNAKAN K-MEANS DENGAN CLUSTER DISTANCE PERFORMANCE

  • Wulan Komala STMIK IKMI Cirebon
  • Rudi Kurniawan STMIK IKMI Cirebon
  • Yudhistira Arie Wijaya STMIK IKMI Cirebon
DOI: https://doi.org/10.31000/jika.v8i1.10383

Abstract

Seiring dengan perkembangan zaman di era modern dimana teknologi informasi(TI) semakin berkembang pesat. Setiap perusahaan dalam bidang perdagangan juga memiliki keinginan untuk mengembangkan usahanya dengan maksimal agar tidak tenggelam dalam persaingan bisnis yang berjalan sangat ketat. Permasalahan pada penelitian ini belum diketahui informasi yang diperoleh dari data transaksi penjualan. Oleh karena itu, perlu dilakukan beberapa strategi untuk meningkatkan penjualan. Tujuan penelitian ini untuk mengetahui pengaruh measure type agar mendapatkan nilai K optimal  berdasarkan Davies Bouldin Index. Dalam penelitian ini menggunakan algoritma K-Means dan metode analisa data yang digunakan yaitu Cross Industry Standard Process for Data Mining (CRISP-DM). Adapun data yang digunakan dalam penelitian ini sebanyak 172 record. Hasil dari penelitian ini adalah melakukan perbandingan nilai DBI dengan measure type yang digunakan. Karena nilai K=4 merupakan nilai terkecil dibandingkan K lainnya, maka dapat disimpulkan bahwa K=4 dengan nilai DBI sebesar 0,156 dengan  menggunakan measure type jenis Bregman Divergences merupakan hasil cluster terbaik. Dari hasil penelitian yang dilakukan oleh Peneliti, toko LanShop dapat mengetahui stok produk pakaian yang laris dan kurang laris sehingga dapat mengatur jenis produk mana yang harus ditingkatkan dan dikurangi guna mengoptimalkan persediaan jenis produk pakaian.

 

Kata kunci ; Penjualan, K-Means, CRISP-DM, Cluster Distance Performance, Davies Bouldin Index

References

Al-Fahmi, Barron Mahardhika, Endra Rahmawati, and Tri Sagirani. 2023. “Penerapan K-Means Clustering Pada Pariwisata Kabupaten Bojonegoro Untuk Mendukung Keputusan Strategi Pemasaran.†Jurnal Nasional Teknologi Dan Sistem Informasi 9(2):141–49. doi: 10.25077/teknosi.v9i2.2023.141-149.

Amin, Fadli, Dini Sri Anggraeni, and Qurrotul Aini. 2022. “Penerapan Metode K-Means Dalam Penjualan Produk Souq.Com.†Applied Information System and Management (AISM) 5(1):7–14. doi: 10.15408/aism.v5i1.22534.

Anam, Khaerul, Dadang Sudrajat, and Dian Ade Kurnia. 2022. “Analisis Segmentasi Pelanggan Menggunakan Metode K-Means Clustering.†Jurnal ICT : Information Communication & Technology 21(2):273–78.

Andi Cuhwanto, Yahya Novi, and Dewi Agushinta R. 2021. “Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means.†Petir 15(1):48–56. doi: 10.33322/petir.v15i1.1358.

Dharma Putra, Yogiswara, Made Sudarma, and Ida Bagus Alit Swamardika. 2021. “Clustering History Data Penjualan Menggunakan Algoritma K-Means.†Majalah Ilmiah Teknologi Elektro 20(2):195. doi: 10.24843/mite.2021.v20i02.p03.

Firdaus, A., Sopandi, A., Herdiansah, A., & Fauziyah, S. (2023). Pengembangan Sistem Informasi Data Balance Planning Berbasis Web Studi Kasus PT. Panarub Industry. JIKA (Jurnal Informatika), 7(3), 329–335. https://doi.org/10.31000/jika.v7i3.8604

Indriyani, Fintri, and Eni Irfiani. 2019. “Clustering Data Penjualan Pada Toko Perlengkapan Outdoor Menggunakan Metode K-Means.†JUITA : Jurnal Informatika 7(2):109. doi: 10.30595/juita.v7i2.5529.

Irawan, Yuda. 2019. “Implementation of Data Mining for Determining Majors Using K-Means Algorithm in Students of Sma Negeri 1 Pangkalan Kerinci.†Journal of Applied Engineering and Technological Science 1(1):17–29. doi: 10.37385/jaets.v1i1.18.

Mangku Negara, Iis Setyawan, Purwono Purwono, and Imam Ahmad Ashari. 2021. “Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 Dengan Algoritma K-Means.†JOINTECS (Journal of Information Technology and Computer Science) 6(3):153. doi: 10.31328/jointecs.v6i3.2693.

Muhima, Rani Rotul, Muchamad Kurniawan, Septiyawan Rosetya Wardhana, Anton Yudhana, Sunardi, and Mitra Adhimukti. 2023. “An Improved Clustering Based on K-Means for Hotspots Data.†Indonesian Journal of Electrical Engineering and Computer Science 31(2):1107–17. doi: 10.11591/ijeecs.v31.i2.pp1109-1117.

Nurdiyansyah, Firman, and Ismail Akbar. 2021. “Implementasi Algoritma K-Means Untuk Menentukan Persediaan Barang Pada Poultry Shop.†Jurnal Teknologi Dan Manajemen Informatika 7(2):86–94. doi: 10.26905/jtmi.v7i2.6377.

Prakoso, Bakhtiyar Hadi, Ervina Rachmawati, Demiawan Rachmatta Putro Mudiono, Veronika Vestine, and Gandu Eko Julianto Suyoso. 2023. “Klasterisasi Puskesmas Dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga Dan Gizi Masyarakat.†Jurnal Buana Informatika 14(01):60–68. doi: 10.24002/jbi.v14i01.7105.

Putri Trisnawati, and Ade Irma Purnamasari. 2023. “Penerapan Pengelompokkan Produktivitas Hasil Pertanian Menggunakan Algoritma K-Means Pendahuluan Potensi Sektor Pertanian Yang Tak Kalah Besarnya Dengan Setor Migas . Apalagi Sumber Daya Migas Yang Bersifat Tidak Bisa Diperbarui Dan Pasti Akan Habis , Hal.†6(2):249–57

Purnama, Chandra, Wina Witanti, and Puspita Nurul Sabrina. 2022. “Klasterisasi Penjualan Pakaian Untuk Meningkatkan Strategi Penjualan Barang Menggunakan K-Means.†Journal of Information Technology 4(1):35–38. doi: 10.47292/joint.v4i1.79.

Sya’bana, N. A., Herdiansah, A., Faridi, F., & Pujangkoro, T. (2023). Sistem Pendukung Keputusan Pemilihan Makanan Kucing Menggunakan Metode Analitical Hierarchy Process. JIKA (Jurnal Informatika), 7(4), 472–478. https://doi.org/10.31000/jika.v7i4.9600

Triyandana, Genta, Lala Aprianti Putri, and Yuyun Umaidah. 2022. “Penerapan Data Mining Pengelompokan Menu Makanan Dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means.†Journal of Applied Informatics and Computing 6(1):40–46. doi: 10.30871/jaic.v6i1.3824.

Wahyudi, Tri, and Titi Silfia. 2022. “Implementation of Data Mining Using K-Means Clustering Method To Determine Sales Strategy in S&R Baby Store.†Journal of Applied Engineering and Technological Science 4(1):93–103. doi: 10.37385/jaets.v4i1.913

Published
2024-01-27
Issue
Vol. 8 No. 1 (2024): JIKA (Jurnal Informatika)
Section
Articles

Copyright (c) 2024 JIKA (Jurnal Informatika)

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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:

  1. 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. 
  2. 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.
  3. 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:

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.