K-Means is a data mining algorithm that can be used to grouping or clustering data. This research using k-means for clustering the data of motorcycle based on consumer needs. The dataset used in this research is Honda and Yamaha motorcycle which taken from the dialers in Dewantara District, Aceh. The data tested by grouping 300 data of motorcycle with different attributes into 3 clusters, which are cheap, normal, and expensive. The distribution of the data we separate it using 45 data in 15 times of test. Each test used 3 different data randomly selected on each test. To calculate the distance of each motorcycle data that have been inputted to each centroid, we used the Euclidean Distance formula. Data in this cluster system can be used as a recommendation for users in selecting the motorcycle that they interest the most. The results of the performance on each test finished in 15 times shown that the average value of Precision by 76%, Recall by 76% and the accuracy by 81%.
Data Mining merupakan proses ekstraksi data menjadi informasi yang memungkinkan para pengguna untuk mengakses secara cepat data dengan jumlah yang besar, dengan teknik yang tepat proses data mining akan memberikan hasil yang optimal [1]. Setiap data pada data mining terdiri dari kelas tertentu bersama dengan variabel dan faktor-faktor penentu kelas variabel tersebut. Dengan data mining, peneliti dapat menentukan suatu kelas dari variabel data yang dimiliki[2]. Salah satu tujuan yang banyak dihasilkan dalam data mining adalah klasifikasi[3]. Menurut Abdillah (2018), klasifikasi merupakan penggolongan atau pengelompokan fungsi yang menjelaskan atau membedakan konsep atau kelas data, dengan tujuan untuk memperkirakan kelas dari suatu objek yang labelnya belum diketahui atau pembagian sesuatu menurut kelas-kelas nya. Metode-metode klasifikasi data INFORMASI ARTIKEL
With k-medoids algorithm, it often takes many iterations to cluster a large dataset, that is, the k-medoids algorithm cannot achieve the optimal performance. Based on cluster validity, this paper tries to optimize the clustering performance of k-medoids algorithm, using the purity algorithm. Specifically, the medoids value was determined by the purity value, and cluster validity was measured with the Davies Bouldin Index (DBI) on the Iris Dataset and the Death/Birth Rate Dataset. The results show that the cluster validity of the proposed purity k-medoids algorithm was better than the conventional k-medoids algorithm. The conventional k-medoids converged in an average of 8.7 iterations on the Death Birth Rate Dataset and 13.2 on the Iris Dataset. By contrast, the purity k-medoids algorithm only needed 2 iterations on either dataset. Therefore, the purity k-medoids algorithm can effectively minimize the number of iterations in the clustering of large datasets.
Proses clustering dengan algoritma K-Means pada dataset yang memiliki banyak atribut akan mempengaruhi besarnya jumlah iterasi. Pada penelitian ini, metode Information Gain digunakan untuk mereduksi atribut dataset. Dataset yang telah direduksi atribut akan dilanjutkan proses clustering dengan K-Means. Dataset yang dianalisis pada penelitian ini adalah data Hepatitis C Virus yang diperoleh dari UCI Machine Learning Repository, dengan 29 atribut dan 1385 jumlah data. Hasil penelitian ini menunjukkan bahwa rata-rata jumlah iterasi yang diperoleh dari 10 kali pengujian dengan menggunakan K-Means konvensional diperoleh rata-rata sebesar 32 iterasi, sedangkan K-Means dengan reduksi atribut diperoleh rata-rata sebesar 27.7 iterasi. Nilai validitas cluster dihitung menggunakan Davies-Bouldin Index (DBI). Nilai DBI pada K-Means konvensional sebesar 2.1972, sedangkan DBI pada K-Means yang telah direduksi 1 atribut sampai 5 atribut diperoleh nilai rata-rata DBI masing-masing sebesar 2.0290, 1.8771, 1.8641, 1.8389, dan 1.8117.
A monitoring system is needed to monitoring vehicle’s daily activities at Bank Indonesia Lhokseumawe. The management of vehicle’s daily activities in the Representative Office of Bank Indonesia Lhokseumawe is still using manual handwritting’s records on some books, which is making it difficult to retrieve the required data quickly and accurately. To assist in carrying out the objection given, the authors designed an integrated information system which capable to monitoring vehicle’s daily activities. The Information system based on web and gps for android.
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