IMPLEMENTASI ALGORITMA K-MEANS UNTUK CLUSTERING PROJECT HEALTH PADA PT XYZ BERDASARKAN PROJECT BASELINE

• Main Author: Ajeng Arifa Chantika Rindu, (Author)
• Format: Book
• Published: 2023-04-12.
• Subjects: Thesis; NonPeerReviewed
• Online Access: Link Metadata
• Notes: http://repository.upnvj.ac.id/24069/14/ABSTRAK.pdf; http://repository.upnvj.ac.id/24069/16/AWAL.pdf; http://repository.upnvj.ac.id/24069/3/BAB%201.pdf; http://repository.upnvj.ac.id/24069/4/BAB%202.pdf; http://repository.upnvj.ac.id/24069/5/BAB%203.pdf; http://repository.upnvj.ac.id/24069/6/BAB%204.pdf; http://repository.upnvj.ac.id/24069/7/BAB%205.pdf; http://repository.upnvj.ac.id/24069/12/DAFTAR%20PUSTAKA.pdf; http://repository.upnvj.ac.id/24069/17/RIWAYAT%20HIDUP.pdf; http://repository.upnvj.ac.id/24069/18/LAMPIRAN.pdf; http://repository.upnvj.ac.id/24069/9/HASIL%20PLAGIARISME.pdf; http://repository.upnvj.ac.id/24069/15/ARTIKEL%20KI.pdf
• Keywords: QA75 Electronic computers. Computer science

PT XYZ is one of the companies in Indonesia that is engaged in the telecommunications sector. In supporting the success of PT XYZ, there are various projects that run in parallel, where each project certainly has its own potential and risk. A project's potential can boost the company's revenue and productivity. On the other hand, there are some risks that need to be taken for every project when it is about to start. Project data is recorded from the start to finish so that the project progress and improvements can be monitored and analyzed. As the project runs, the project assurance team at PT XYZ, which is responsible for the processes leading to project success requires a project health category. Therefore, the researcher developed a process for clustering project health, which is included in a type of unsupervised learning. One of the clustering algorithms is K-Means, which groups data based on similar criteria. Researcher also use dimensionality reduction with the Principal Component Analysis (PCA) method to determine its impact on the clustering process with the K-Means algorithm. Researcher conducted a study and evaluated clusters using the Calinski-Harabasz Index to find out the difference between clusters and the similarity of cluster members. From this study, the researcher obtained three clusters or project health categories consisting of cluster 0, 1, and 2. Evaluation results with the Calinski-Harabasz Index showed that the K-Means model on the dimensionality reduction data with PCA had better performance than the standard K-Means model with a Calinski-Harabasz Index value of 55633,12776405707, which is higher than 25914,578262576793.