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All Journal Indonesian Journal of Data and Science Komisi
Ana Fatahali Ramadhan
Universitas Nusa Putra
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics

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Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services Ana Fatahali Ramadhan; Sudin Saepudin; Carti Irawan; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.316

Abstract

The digital printing industry in Indonesia is experiencing rapid growth thanks to the increasing demand from companies for printing services such as banners, stickers, brochures, and business cards. CV. Copy Paste is one of the companies operating in the digital printing industry that fulfills various printing orders every month. However, the company has difficulty identifying the most popular printing services, which makes it difficult to develop a targeted promotional strategy. In view of this problem, the aim of this study is to group digital printing services according to their popularity using the K-Means Clustering method. This study uses a quantitative approach, collecting sales data from the last 12 months, covering 160 types of services. The steps taken include preliminary data processing, namely attribute selection, data cleaning, and data transformation so that it can be effectively processed using the K-Means algorithm, implemented in the Python programming language. The test results show that digital printing services can be divided into three clusters: 115 less popular services (C1), 31 fairly popular services (C2), and 14 very popular services (C3). The results of this study provide information that can be used as a basis for strategic decisions regarding promotion and service management. In this way, the K-Means Clustering algorithm has proven effective in helping companies group products in a more objective and measurable way based on historical data.  
PERSEDIAAN SPAREPART DENGAN ALGORITMA FPGROWTH DALAM PENJUALAN BENGKEL MOTOR (Studi Kasus Bengkel Cahaya Motor) Ana Fatahali Ramadhan
Jurnal Komputer dan Sistem Informasi Vol. 1 No. 3 (2024): Juni 2024
Publisher : Indie Press Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66865/w043dv25

Abstract

Berdasarkan sebuah kasus Penelitian yang terjadi di Bengkel Cahaya Motor, inventarisasi produk dilakukan pada saat produk habis. Dengan menerapkan algoritma FP-Growth pada data transaksi penjualan Bengkel Cahaya Motor selama bulan Agustus 2023 untuk mengidentifikasi barang yang diminati oleh pelanggan. Penelitian ini menggunakan penerapan algoritma FP-Growth melalui aplikasi rapidminer 9.10 dengan minimum support ≥ 65% dan minimum Confidence ≥ 80%. Hasil penelitian menunjukkan bahwa Gearset memiliki tingkat minat tertinggi (87,10%), diikuti oleh Seal Karet (83,87%), Kampas Rem, Oli, dan Ban (80,65%), Kabel Kopling (74,19%), serta Lahar (67,74%). Hasil ini memberikan wawasan penting untuk keputusan pengadaan barang, dengan rekomendasi penambahan stok Gearset
Co-Authors Carti Irawan Mupaat Saepudin, Sudin