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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Salsabila, Tasya Mulia
Universitas Indonesia
Computer Science & IT

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Design of a Data Mart for Optimizing Product Sales Analysis at PT. X Samantha, Velline; Beng, Jap Tji; Trisnawarman, Dedi; Tiatri, Sri; Salsabila, Tasya Mulia; Sefira, Fasia Meta; Zahro, Tiara; Latupono, Sania Alikha Rahmadira
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3158

Abstract

With the advancement of technology, companies can now efficiently manage and analyze their data, providing valuable insights that support better business decisions. PT. X is one such company that wants to leverage this capability to optimize its sales data analysis. Therefore, the goal of this study is to design an effective data mart. During the development process, we used Kimbal's Nine-Step Methodology alongside the ETL (extract, transform, load) process to ensure the data was accurately extracted, transformed, and loaded into the data mart. The outcome of this research is a data mart and star schema tailored to PT. X specific needs. Testing results showed that query execution time increased by 40% and data accuracy improved by 80%. Report generation time was also optimized, resulting in a process that was 83% faster. These results demonstrate how a well-structured data mart can improve decision-making efficiency and data reliability. 
Data Warehouse for Study Program Accreditation Data Management at Private University X Putri, Tifani Anasya; Beng, Jap Tji; Trisnawarman, Dedi; Tiatri, Sri; Nagm, Fouad; Naidas, Michael S.; Salsabila, Tasya Mulia; Nurkholiza, Rahmiyana
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3157

Abstract

Technological advancements in the Fourth Industrial Revolution have transformed higher education, yet accreditation processes often suffer from inefficient data management, causing delays, inconsistencies, and limited monitoring of performance indicators. This study designs a data warehouse integrated with a dashboard and Early Warning System (EWS) to improve accreditation data management at University X, West Jakarta. Using Kimball’s Dimensional Model, data were structured into fact and dimension tables, with collection methods including interviews with the Head of the IT Department and document analysis of accreditation processes. Descriptive statistics were applied to identify trends and patterns. The dashboard enables real-time visualisation of key metrics, while the EWS issues alerts on missing or outdated data. Results show that the snowflake schema enhances data organisation and clarity, reducing manual processing and facilitating proactive monitoring. The system supports more efficient accreditation management and strengthens institutional readiness for assessment. 
Co-Authors Beng, Jap Tji Latupono, Sania Alikha Rahmadira Nagm, Fouad Naidas, Michael S. Nurkholiza, Rahmiyana Putri, Tifani Anasya Samantha, Velline Sefira, Fasia Meta Tiatri, Sri Trisnawarman, Dedi Zahro, Tiara