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Implementation of the K-Nearst Neighbor (k-NN) Algorithm in Classification of Angora and Country Cats Andriana, Wiwin; Wisnumurti, Reza; Lestari, Yuni; Indriyono, Bonifacius Vicky; Wibowo, Dibyo Adi; Udayanti, Erika Devi
Journal of Applied Intelligent System Vol. 8 No. 1 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i1.7129

Abstract

There are so many types of mixed cats from various cat breeds, so many people find it difficult to identify and classify them. Therefore, we need a method that can classify the type of cat breeds. In this study the authors used the K-Nearest Neighbor (k-NN) algorithm to make it easier to recognize and classify cat breeds based on certain characteristics. The author took samples of 2 types of cat races, namely the Anggora race and the Kampung race. The implementation stage is to determine the euclidean distance and sort it and then determine the value of K to find the nearest neighbor. In testing, the authors used 50 training data and 50 test data with 6 attributes used, namely body shape, nose width, nose height, food type, hair type and hair length. The results of the classification of cat breeds using the k-NN method obtained an accuracy rate of 94% and an error rate of 6%.
Analisis Pola Belanja Menggunakan Algoritma Apriori Pada Usaha Thrift “Toko Bakul Gombal” Andriana, Wiwin; Lestari, Yuni; Wisnumurti, Reza; Indriyono, Bonifacius Vicky; Pamungkas, Natalinda
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 7 No. 1 (2023): PROSIDING NSEMINAR NASIONAL INOVASI TEKNOLOGI TAHUN 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v7i1.3444

Abstract

Selama ini pemanfaatan data transaksi penjualan pada bisnis thrift yaitu Toko Bakul Gombal hanya disimpan sebagai arsip. Penulis memanfaatkan dan mengolah data transaksi tersebut menjadi informasi yang berguna untuk peningkatan penjualan dan inovasi produk baru. Dengan menganalisa data transaksi diharapkan mendapatkan suatu pola penjualan. Dalam penelitian ini penulis menggunakan Algoritma Apriori, dengan membentuk kandidat kombinasi item yang mungkin terjadi, selanjutnya dilakukan pengujian apakah kombinasi tersebut memenuhi parameter support dan confidence minimum. Dari 50 sampel data transaksi yang diambil, dengan 8 item yaitu yaitu kaos, kemeja, celana kain, topi, jaket kain, jaket jeans, sepatu. Dengan mengambil minimum support 8% dan confidence 70%. Menghasilkan perkalian support dan confidence tertinggi sebesar 38% yaitu if buy kemeja then buy kaos.