K-Means Algorithm Implementation for Project Health Clustering
Indonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company revenue and productivity. On the other hand, there are some risks that need to...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-09-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/5181 |
| _version_ | 1797327909794349056 |
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| author | Ajeng Arifa Chantika Rindu Ria Astriratma Ati Zaidiah |
| author_facet | Ajeng Arifa Chantika Rindu Ria Astriratma Ati Zaidiah |
| author_sort | Ajeng Arifa Chantika Rindu |
| collection | DOAJ |
| description | Indonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company 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 clustering project health process, 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 groups or project health categories, consisting of groups 0, 1, and 2. The evaluation results with the Calinski-Harabasz index showed that the K-Means model in the PCA dimensionality reduction data performed better than the standard K-Means model with a Calinski-Harabasz index value of 55633,12776405707, which is higher than 25914,578262576793. |
| first_indexed | 2024-03-08T06:43:36Z |
| format | Article |
| id | doaj.art-b702cea28c8948ac92efc2527a46747d |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T06:43:36Z |
| publishDate | 2023-09-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-b702cea28c8948ac92efc2527a46747d2024-02-03T08:23:19ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-09-01751064107610.29207/resti.v7i5.51815181K-Means Algorithm Implementation for Project Health ClusteringAjeng Arifa Chantika Rindu0Ria Astriratma1Ati Zaidiah2Universitas Pembangunan Nasional Veteran JakartaUniversitas Pembangunan Nasional Veteran JakartaUniversitas Pembangunan Nasional Veteran JakartaIndonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company 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 clustering project health process, 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 groups or project health categories, consisting of groups 0, 1, and 2. The evaluation results with the Calinski-Harabasz index showed that the K-Means model in the PCA dimensionality reduction data performed better than the standard K-Means model with a Calinski-Harabasz index value of 55633,12776405707, which is higher than 25914,578262576793.http://jurnal.iaii.or.id/index.php/RESTI/article/view/5181projectproject healthclusteringk-means |
| spellingShingle | Ajeng Arifa Chantika Rindu Ria Astriratma Ati Zaidiah K-Means Algorithm Implementation for Project Health Clustering Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) project project health clustering k-means |
| title | K-Means Algorithm Implementation for Project Health Clustering |
| title_full | K-Means Algorithm Implementation for Project Health Clustering |
| title_fullStr | K-Means Algorithm Implementation for Project Health Clustering |
| title_full_unstemmed | K-Means Algorithm Implementation for Project Health Clustering |
| title_short | K-Means Algorithm Implementation for Project Health Clustering |
| title_sort | k means algorithm implementation for project health clustering |
| topic | project project health clustering k-means |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/5181 |
| work_keys_str_mv | AT ajengarifachantikarindu kmeansalgorithmimplementationforprojecthealthclustering AT riaastriratma kmeansalgorithmimplementationforprojecthealthclustering AT atizaidiah kmeansalgorithmimplementationforprojecthealthclustering |