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Journal : International Journal Software Engineering and Computer Science (IJSECS)

Business Intelligence and Decision Support to Enhance Decision-Making Quality in Higher Education Syamsiah, Syamsiah; Darmawan, Agus; Halimatusa'diah, Halimatusa'diah; Hidayatullah, Reko Syarif; Isnain, Nasrulloh
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4273

Abstract

The availability of accurate and reliable data is essential for organizational sustainability. Business intelligence (BI) enhances an organization's ability to analyze challenges, support decision-making, and improve performance. The term “Business Intelligence System” refers to applications and technologies that facilitate BI activities, including data collection, storage, access, and analysis—thus providing insights into performance and aiding informed decisions. These activities include decision support systems, querying, reporting, OLAP, statistical analysis, forecasting, and data mining. BI applications encompass reporting tools, analytics platforms, dashboards, alerts, and portals, and involve technologies such as data integration, quality management, warehousing, and content analysis. Accordingly, a Business Intelligence System can function as a Decision Support System. This study uses SPSS version 17 for data analysis to evaluate the impact of BI and decision support on decision-making quality in colleges in Jakarta and Bekasi. ANOVA (F-test) results show an F-value of 117.041, exceeding the F-table value of 3.29, with a significance of 0.000 < α = 0.05. Since the calculated F-value surpasses the critical value and the significance level is below 0.05, the null hypothesis is rejected. Thus, BI and decision support significantly and simultaneously influence decision-making quality (Y). These findings highlight the essential role of BI and decision support in improving decision-making within higher education institutions.
Log-Based Code Maniac E-Learning Web Development Model Utilizing Adaptive Web Development Techniques Hidayatullah, Reko Syarif
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4274

Abstract

Education is a fundamental requirement for human civilization, particularly for children and adolescents. The recent pandemic has compelled the education sector to adopt online learning alternatives. Codemaniac is an e-learning tool developed with gamification techniques to enhance student motivation. However, Codemaniac still lacks adaptive features that optimize user engagement based on individual behaviors. To address this limitation, further development will incorporate adaptive features by utilizing recorded user behavior from log files. This behavioral data will be clustered using the fuzzy c-means algorithm, resulting in three distinct user groups, each receiving a tailored user interface. The system is developed following the SDLC waterfall model, with Python used for clustering implementation. The development process involves three user roles, five additional functional requirements, and one non-functional requirement. System testing employs white-box methods for unit testing and black-box methods for validation.