PT Cipta Tekno Mandiri merupakan sebuah sofware house yang menawarkan jasa pengembangan software. Dalam artian perusahaan yang produk utamanya adalah pengembangan dan menyediakan software atau aplikasi. PT Cipta Tekno Mandiri juga menawarkan layanan domain dan hosting, serta jasa kursus programming. Untuk membantu PT Cipta Tekno Mandiri dalam berbagi informasi antar departemen maka diperlukan adanya sebuah sistem Enterprise Resource Planning (ERP) yang berfungsi untuk mengintegrasikan setiap departemen yang ada. Melalui sistem ini setiap departemen dapat saling bertukar informasi. ERP yang dibuat berbasis web, untuk mempercepat proses pembuatan website maka diperlukan sebuah framework. Framework yang digunakan yaitu Yii 2. Yii merupakan salah kerangka kerja yang memiliki performa yang mengagumkan. Framework Yii memberikan kemudahan bagi programmer dalam pembuatan sebuah aplikasi berbasis web. ERP ini dikembangkan menggunakan model waterfall. Model waterfall merupakan metode pengembangan perangkat lunak yang bersifat sequensial atau terurut dimulai dari tahap pengumpulan data, desain, pengkodean, pengujian dan pemeliharaan.
Clustering is a process of grouping a set of objects into multiple clusters, so that the collection of similar objects will be grouped into the same cluster and dissimilar objects will be grouped into other clusters. Fuzzy k-means algorithm is one of clustering algorithm by partitioning data into k clusters employing Euclidean distance as a distance function. This research discusses clustering categorical data using Fuzzy k-Means Kullback-Leibler Divergence. In the determination of the distance between data and center of cluster uses mutual information known as Kullback-Leibler Divergence distance between the joint distribution and the product distribution from two marginal distributions. Extensive theoretical analysis was performed to show the effectiveness of the proposed method. Moreover, the comparison results of the proposed method with Fuzzy Centroid and Fuzzy k-Partition approaches in terms of response time and clustering accuracy were also performed employing several datasets from UCI Machine Learning. The experiment results show that the proposed algorithm provides good results both from clustering quality and accuracy for clustering categorical data as compared to Fuzzy Centroid and Fuzzy k-Partition.
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