Business intelligence system model to measure the performance of lecturers' scientific publications

• Published in: Telkomnika Vol. 23; no. 4; pp. 954 - 964
• Main Authors: Hidayat, Miwan Kurniawan, Sugiarto, Dedy, Fitriana, Rina, Liang, Yun-Chia
• Format: Journal Article
• Language: English
• Published: Yogyakarta Ahmad Dahlan University 01.08.2025
• Subjects: Algorithms; Bibliometrics; Business intelligence; Clustering; Customer relationship management; Data analysis; Data mining; Data visualization; Data warehouses; Decision making; Design analysis; Dimensional analysis; Executive information systems; Higher education; Hirsch index; Intelligence (information); Knowledge management; Machine learning; Management decisions; Online analytical processing; Performance evaluation; Performance management; Prototyping; Research methodology
• ISSN: 1693-6930; 2302-9293
• DOI: 10.12928/TELKOMNIKA.v2314.26221

Scientific publication data is sourced from the SINTA website to measure the performance of journals, institutions, and researchers in Indonesia. Accessing and analyzing data for institutional needs is restricted, and lecturer development patterns based on lecturer characteristics remain untapped. The study aims to analyze and design business intelligence system models to measure the performance of scientific publications using dimensional models, clustering, on-line analytical processing (OLAP), and prototyping. Research methods are carried out through data and information needs analysis, data warehouse design, data mining and OLAP application, business intelligence system development, and system evaluation. The resulting dimensional models are the researcher index model, the researcher score model, the publication article model, and the research subject model. Measurements of data size and processing time show that the star schema has data of 336 KB and a processing time of 0.00554 seconds, is the best model compared to the snowflake's schema, which has data of 368 KB and a processing time of 0.00611 seconds. Davies-Bouldin Index (DBI) measurements show the best clustering performance is the X-means algorithm with K as many as 5 clusters (Kmin=3, Kmax=5) and a DBI value of 0.537040.