K-Means Algorithm Implementation for Project Health Clustering
Dublin Core
Title
K-Means Algorithm Implementation for Project Health Clustering
Subject
project; project health; clustering; K-Means
Description
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.
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.
Creator
Ajeng Arifa Chantika Rindu, Ria Astriratma, Ati Zaidiah
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
October 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Ajeng Arifa Chantika Rindu, Ria Astriratma, Ati Zaidiah, “K-Means Algorithm Implementation for Project Health Clustering,” Repository Horizon University Indonesia, accessed February 3, 2026, https://repository.horizon.ac.id/items/show/10100.