PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK MENGANALISA TRANSAKSI PENJUALAN JASA CETAK PADA UNIT PRINT ON DEMAND (POD) PERCETAKAN GRAMEDIA

Goldie Gunadi(1*),

(1) STMIK WIDURI
(*) Corresponding Author

Abstract


Data mining is a method used to obtain valuable information contained in data banks. The information obtained can be used as input for determining the business strategy for the head of the company's agency. One of the most widely used data mining techniques is clustering using the K-Means method. Print on Demand (PoD) is one of the printing business units of PT. Gramedia which specifically provides printing services for various types of products, including: books, magazines, calendars, posters, promo materials such as product catalogs and brochures, as well as small-sized products such as business cards, tickets, coupons or vouchers and stickers. Currently every sales transaction data is stored in a SQL Server database, but until now the data processing is still done manually for reporting needs for company management. The purpose of this research is to perform K-Means Clustering analysis of the transaction data of sales of print services using the RapidMiner application to classify routine customer data based on the number of transactions made for each type of print product. The results of the application of the K-Means Clustering method resulted in 8 groups of customer data where the largest group consisted of 92.89% of the number of customers. The results of the analysis can be used by the company's management to determine various business strategies to increase the company's competitiveness.

Keywords


Clustering, Data Mining, K-Means Clustering, Printing, RapidMiner

Full Text:

PDF

References


Afifah, Lutfia. 2021. “Apa Yang Harus Dilakukan Dalam Proses Data Cleaning?” Https://Ilmudatapy.Com/.

Fatmawati, Kiki and Agus Perdana Windarto. 2018. “Data Mining: Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue (Dbd) Berdasarkan Provinsi.” CESS (Journal of Computer Engineering System and Science) 3(2):173–78.

Ginantra, Ni Luh Wiwik Sri Rahayu, Fatimah Nur ARifah, and Anggi Hadi Wijaya. 2021. Data Mining Dan Penerapan Algoritma. 1st ed. Yayasan Kita Menulis.

Kusumo, Ario Suryo. 2021. Pemrograman SQL Server 2019. Elex Media Komputindo.

Linda Tanti. 2021. “Metode Data Mining Clustering.” Binus.Ac.Id/.

Loukas, Serafeim. 2020. “Everything You Need to Know about Min-Max Normalization: A Python Tutorial.” Towards Data Science.

Melati, I. Gst Ayu Sri, Linawati, and I. A. Dwi Giriantari. 2018. “Knowledge Discovery Data Akademik Untuk Prediksi Pengunduran Diri Calon Mahasiswa.” Majalah Ilmiah Teknologi Elektro 17(3):325–31.

Pambudi, Wahyu Tities and Arita Witanti. 2021. “Penerapan Algoritma K-Means Clustering Untuk Menganalisis Penjualan Pada Toko Ayu Collection Barbasis Web.” Jurnal Informatika Universitas Pamulang 6(3):645–50.

Rohmah, Ai, Falentino Sembiring, and Adhitia Erfina. 2021. “Implementasi Algoritma K-Means Clustering Analysis Untuk Menentukan Hambatan Pembelajaran Daring (Studi Kasus: Smk Yaspim Gegerbitung).” SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika) 1(1):290–98.

Valadkhani, Mohammad. 2016. “Knowledge Discovery in Data (KDD) Process.” Linkedin.Com.

Wanto, Anjar, Muhammad Noor Hasan Siregar, and Agus Perdana Windarto. 2020. Data Mining : Algoritma Dan Implementasi. 1st ed. Yayasan Kita Menulis.

Yufeng. 2022. “Three Performance Evaluation Metrics of Clustering When Ground Truth Labels Are Not Available.” Towards Data Science.




DOI: https://doi.org/10.37365/jti.v8i2.148

Refbacks

  • There are currently no refbacks.


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