K-Means Algorithm Implementation for Project Health Clustering

Abstract

Indonesia has several companies that are engaged in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. A project’s potential can boost the company’s revenue and productivity. On the other hand, there are some risks that need to be considered for every project when it is about to start. Project data is recorded from start to finish so that the project's progress and improvements can be monitored and analyzed. As the project runs, the project team at one of Indonesia's telecommunication companies, which is responsible for the processes leading to project success, requires a project health category. Therefore, this study is conducted to develop a process for clustering project health, which is included in a type of unsupervised learning that runs on unlabeled data. One of the clustering algorithms is K-Means, which groups data based on similar criteria. Researchers also use dimensionality reduction with the Principal Component Analysis (PCA) method to determine its impact on the clustering process with the K-Means algorithm. From this study, the researcher obtained three clusters or project health categories, consisting of clusters 0, 1, and 2. Evaluation results with the Calinski-Harabasz Index showed that the K-Means model on the dimensionality reduction data with PCA performed better than the standard K-Means model with a Calinski-Harabasz Index value of 55633,12776405707, which is higher than 25914,578262576793.