Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Juliansyah, Mohamad Denis
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Evaluating Mdm Maturity In Human Capital Data: Case Study of Jasa Marga Juliansyah, Mohamad Denis; Ruldeviyani, Yova; Rahman, Henry Aulia; Astagina, Shania Eriadhani
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.871

Abstract

Effective Master Data Management (MDM) is essential for organizations to ensure data consistency, accuracy, and accessibility, particularly within intricate operational settings. This study assesses the maturity level of MDM for human capital data at PT Jasa Marga (Persero) Tbk, Indonesia’s leading state-owned toll road developer and operator. Human capital data is currently managed through two primary applications: SAP for the main company and the JM-Click Human Capital Information System (HCIS) for its subsidiaries. This separation has led to data silos and inconsistencies, diminishing the reliability and accessibility of critical human capital data. Utilizing the Master Data Management Maturity Model (MD3M) by Spruit and Pietzka, this research investigates five main topics and 13 focus areas within the organization. Data collection involved questionnaires and interviews with key experts. Results indicate that PT Jasa Marga has implemented 76.92% of necessary MDM capabilities, with Data Protection and Usage & Ownership scoring the highest maturity level of 5. However, Data Quality is at level 2, indicating a need for major enhancements in data maintenance and consistency across subsidiary data models. This study provides actionable recommendations for improving data quality, aligning data models, and integrating advanced technologies, stressing the importance of continuous MDM improvements to better support the organization’s strategic objectives and operational demands.
Co-Authors Astagina, Shania Eriadhani Rahman, Henry Aulia Yova Ruldeviyani