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All Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) MEANS (Media Informasi Analisa dan Sistem)
Lestari Sinaga
STIKOM Tunas Bangsa Pematangsiantar
Computer Science & IT Electrical & Electronics Engineering Energy Engineering

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Implementasi Data Mining Menggunakan Algoritma Apriori Pada Penjualan Sepeda Motor Jenis Honda (Studi Kasus : Showroom Honda Arista Pematangsiantar) Lestari Sinaga; Abdullah Ahmad; Muhammad Safii
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (964.229 KB) | DOI: 10.54367/means.v5i1.518

Abstract

Showroom Honda Arista merupakan salah satu showroom atau Dealer yang menyediakan jual beli kendaraan sepeda motor Honda dengan berbagai type dan harga. Data mining ialah serangkaian proses untuk menggali nilai tambah dari suatu kumpulan data berupa pengetahuan yang selama ini tidak diketahui secara manual. Salah satu metode yang data mining yang digunakan dalam penelitian ini adalah Metode Apriori. Metode Apriori merupakan salah satu teknik data mining yang berfungsi untuk menemukan aturan asosiatif antara suatu kombinasi item. Barang yang dianalisis yaitu barang yang dipesan bersamaan dengan barang lainnya atau dengan kata lain pemesanan lebih dari satu barang namun tetap melibatkan data pemesanan keseluruhan. Adapun item-item yang masuk kedalam data penjualan ialah Revo, Supra, Beat, Vario, Sonic, PCX, CB, Scoopy, Megapro, Verza, CBR. Hasil dari association yang berupa informasi mengenai Honda merk apa saja yang dibeli secara bersamaan oleh konsumen, dapat digunakan sebagai bahan pertimbangan untuk menetapkan Strategi Pemasaran dan pada data transaksi. Kata kunci: Showroom Honda, Data Mining, Apriori
PENERAPAN DATA MINING PADA JUMLAH PELANGGAN PERUSAHAAN AIR BERSIH MENURUT PROVINSI MENGGUNAKAN METODE K-MEANS CLUSTERING Lestari Sinaga; Abdullah Ahmad; Muhammad Safii
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 2 No. 2 (2019): Jurnal RESISTOR Edisi Oktober 2019
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v2i2.418

Abstract

Water is one of the primary needs for humans so that everyone has the right to get clean water for their daily needs. Along with increasing population, the need for water will increase. So with that the PDAM must sell clean / decent water to its customers, clean water becomes the focus of attention and has the greatest power compared to other problems. Because water is a basic necessity, most of the companies impose rates that can be reached by the community and prices are adjusted to the growth in demand. The purpose of this research is to get a grouping of the number of customers of clean water companies in all provinces using the K-Means Algorithm, K-Means is a method for grouping data into a cluster by calculating the closest distance from a data to a centroid point. Clusters used are high level clusters (C1), medium level clusters (C2), and for low level clusters (C3). Centroid data obtained is for high-level clusters (C1) which are as many as 7710154, for medium-level clusters as much as 929586, and for low-level clusters as much as 112462. Based on the calculated data obtained high-level results, namely the province of Indonesia, for the medium level namely province North Sumatra, DKI Jakarta, West Java, Central Java and East Java, and other provinces are low levels. So that this result can be a support for the company in order to increase water needs.
Co-Authors Abdullah Ahmad Abdullah Ahmad Muhammad Safii Muhammad Safii