Analysis of Clustering for Grouping of Productive Industry by K-Medoid Method

Abstract

With the number of existing data, would have difficulty in doing the classification and the classification of the existing data. To resolve the issue, one way to do clustering is with data mining using clustering technique. The purpose of this research is the importance of knowing the pattern of the production of an industry that can provide the decision and the construction of clustering patterns for development and industrial progress. The results of this research can provide recommendations to improve the development of industry, help the owners of industry to develop the industry to an increase in the number of production and product quality, improve the competitiveness of the owner of the industry in developing its products. In this research will use the K-Medoids algorithm for data grouping of the industry so that it will be found the information that can be used for the recommendations of the improvement of marketing. The results of clustering with the number of cluster 3 produces the first group contains 85 members, the second group contains 222 members and the third group numbered 3 members. The third group are classified as productive because it has a combination of the value of the production of the most high the results of clustering have the quality of purity worth 1 means good cluster quality.

Utilizing K-Means Clustering for the Detection of Cyberbullying Within Instagram Comments

“…Although these have not been extensively applied to Instagram, previous studies using Instagram data have successfully classified users based on the appropriateness of specific hashtags through the K-means and tf-idf methods [13]. This paper presents the first instance of employing K-means clustering to detect cyberbullying on Instagram, categorizing cyberbullying behaviors in comments [14,15].…”