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

Ajeng Arifa Chantika Rindu, . (2023) IMPLEMENTASI ALGORITMA K-MEANS UNTUK CLUSTERING PROJECT HEALTH PADA PT XYZ BERDASARKAN PROJECT BASELINE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf

Download (142kB)
[img] Text
AWAL.pdf

Download (891kB)
[img] Text
BAB 1.pdf

Download (100kB)
[img] Text
BAB 2.pdf
Restricted to Repository UPNVJ Only

Download (548kB)
[img] Text
BAB 3.pdf
Restricted to Repository UPNVJ Only

Download (203kB)
[img] Text
BAB 4.pdf
Restricted to Repository UPNVJ Only

Download (1MB)
[img] Text
BAB 5.pdf

Download (81kB)
[img] Text
DAFTAR PUSTAKA.pdf

Download (137kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository UPNVJ Only

Download (130kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only

Download (139kB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only

Download (14MB)
[img] Text
ARTIKEL KI.pdf
Restricted to Repository staff only

Download (1MB)

Abstract

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.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1910511048] [Pembimbing: Ria Astriratma] [Penguji 1: Widya Cholil] [Penguji 2: Ika Nurlaili Isnainiyah]
Uncontrolled Keywords: Project, Project Health, Clustering, K-Means
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Ajeng Arifa Chantika Rindu
Date Deposited: 24 Jul 2023 03:50
Last Modified: 24 Jul 2023 03:50
URI: http://repository.upnvj.ac.id/id/eprint/24069

Actions (login required)

View Item View Item