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All Journal RISTEC : Research in Information Systems and Technology Journal of Computing Theories and Applications
Fahmi Reza Ferdiansyah
Universitas Ekuitas
Computer Science & IT Decision Sciences, Operations Research & Management

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Beyond Dashboards: A Systematic Literature Review of Learning Analytics, Business Intelligence, and Generative AI for Decision-Making in Universities Heri Purwanto; R. Rizal Isnanto; Qidir Maulana Binu Soesanto; Agus Nursikuwagus; Fahmi Reza Ferdiansyah
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15963

Abstract

The rapid proliferation of learning analytics, business intelligence (BI), artificial intelligence (AI), and generative AI (GenAI) has significantly expanded universities’ ability to collect, integrate, analyze, and operationalize institutional data. However, despite advances in predictive analytics, dashboards, and AI-driven systems, the translation of analytical outputs into consistent and accountable institutional decision-making remains uneven. This systematic literature review synthesizes contemporary research on analytics-enabled decision-making in higher education with the aim of moving beyond dashboard-centric perspectives toward a socio-technical and computing-oriented understanding of how data are transformed into institutional actions and outcomes. Guided by the PRISMA framework, the review synthesizes evidence across four interconnected dimensions: data ecosystems and learning analytics foundations; analytics capability, BI adoption, and digital readiness; AI and advanced analytics for decision support; and human-in-the-loop (HITL) decision routines and institutional outcomes. The findings show that predictive performance and analytical sophistication alone do not guarantee decision value. Instead, effective analytics-enabled decision-making depends on interoperable data ecosystems, organizational analytics capability, governance mechanisms, explainability, and sustained human oversight. Based on these findings, this review contributes a computing-oriented decision-intelligence framework that conceptualizes analytics-enabled decision-making as an end-to-end socio-technical pipeline linking heterogeneous data acquisition, integration, feature construction, analytical modeling, explainability, human validation, governance, and feedback-based refinement. By integrating learning analytics, BI, AI, GenAI, and HITL mechanisms within a unified framework, the review clarifies how universities can move beyond dashboard-based reporting toward accountable, adaptive, and institutionally actionable decision-support infrastructures.
Implementation of Hybrid LSTM-Light GBM and XG Boost Algorithm for Stock Increase Prediction Rudy Sofian; Melly Diyani; Fahmi Reza Ferdiansyah; Heri Purwanto; Rikky Wisnu Nugraha
RISTEC : Research in Information Systems and Technology Vol. 6 No. 2 (2025): RISTEC: Research in Information Systems and Technology
Publisher : Institut Pendidikan Indonesia Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31980/3hjjvt46

Abstract

The advancement of machine learning, particularly in time-series analysis, has created significant opportunities for improving the accuracy of stock price prediction. However, the volatile nature of financial markets and the complexity of temporal patterns often hinder the ability of single models to deliver consistent and optimal results. To address these limitations, this study proposes a hybrid approach by integrating three popular algorithms—Long Short-Term Memory (LSTM), XGBoost, and LightGBM—through a stacking ensemble method. The dataset used consists of daily stock prices of Apple Inc. (AAPL) for the period 2014–2024, obtained from Yahoo Finance. The research process includes preprocessing, the construction of time-series datasets using windowing techniques, training of single models, and the application of ensemble stacking. Experimental results reveal that LSTM achieved the best performance among the single models, with a MAPE of 3.70% and R² of 0.9204, demonstrating its ability to capture long-term temporal dependencies. In contrast, XGBoost and LightGBM performed poorly in recognizing sequential patterns, resulting in negative R² values. The combination of all three models through stacking ensemble significantly improved prediction accuracy, achieving a MAPE of 2.57% and R² of 0.9693. These findings confirm that integrating LSTM, XGBoost, and LightGBM not only enhances predictive accuracy but also improves model stability, while contributing to the scientific development of hybrid machine learning methods in stock market analysis.
Co-Authors Agus Nursikuwagus Heri Purwanto Heri Purwanto Melly Diyani Qidir Maulana Binu Soesanto R Rizal Isnanto Rikky Wisnu Nugraha Rudy Sofian