Tren Penelitian Dan Struktur Pengetahuan Explainable Artificial Intelligence Untuk Pemodelan Prediktif : Bibliometric Review
Keywords:
bibliometric review, explainable artificial intelligence, predictive modeling, machine learning, interpretability, research trendsAbstract
This study aims to analyze research trends and knowledge structures in explainable artificial intelligence (XAI) for predictive modeling through a bibliometric review approach. Data were obtained from Scopus metadata in BIB format exported on June 30, 2026. The initial search strategy yielded 343 documents, which were then filtered based on article document type, English language, and journal source, resulting in 177 articles analyzed for the period 2015-2026. The analysis was conducted using a bibliometric approach through mapping publication productivity, journals and primary authors, influential documents, country contributions, keyword co-occurrence, trend topics, thematic maps, and thematic evolution. The results show that XAI research for predictive modeling experienced a strong acceleration after 2023, indicating a shift in focus from predictive models that are solely accuracy-oriented to models that are transparent, explainable, and accountable. The intellectual structure of this field is interdisciplinary, with contributions from computer science, education, health, energy, environment, geospatial, industry, materials, and engineering. The dominant themes center on machine learning, data mining, forecasting, interpretability, SHAP, LIME, and deep learning, while emerging themes focus on counterfactual explanation, causality-aware forecasting, physics-informed learning, transformers, and graph neural networks. This study identifies five key gaps: method, data, application, theory, and evaluation. The primary contribution of this research is to provide a systematic mapping of the developments, intellectual actors, dominant themes, emerging themes, and future research agendas of XAI to build more accurate, transparent, auditable, and accountable predictive modeling
Downloads
References
M. Nassar, K. Salah, M. H. ur Rehman, and D. Svetinovic, “Blockchain for explainable and trustworthy artificial intelligence,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 10, no. 1, 2020, doi: 10.1002/widm.1340.
M. Moradi and M. Samwald, “Explaining Black-Box Models for Biomedical Text Classification,” IEEE J. Biomed. Heal. Informatics, vol. 25, no. 8, pp. 3112–3120, 2021, doi: 10.1109/JBHI.2021.3056748.
A. S. Albahri et al., “A systematic review of trustworthy artificial intelligence applications in natural disasters,” Comput. Electr. Eng., vol. 118, 2024, doi: 10.1016/j.compeleceng.2024.109409.
S. Ghimire et al., “Explainable deep learning hybrid modeling framework for total suspended particles concentrations prediction,” Atmos. Environ., vol. 347, 2025, doi: 10.1016/j.atmosenv.2025.121079.
W. Xiong et al., “Physics-informed and explainable machine learning framework for performance prediction and design of Ti (C, N)-based cermets,” Mater. Charact., vol. 229, 2025, doi: 10.1016/j.matchar.2025.115584.
S. Tang, S. Wang, J. Jiang, and Y. Zheng, “Improved flash drought forecasting and attribution: A spatial-temporal causality-aware deep learning approach,” J. Hydrol., vol. 667, 2026, doi: 10.1016/j.jhydrol.2026.134945.
M. Alshammari, O. Nasraoui, and S. Sanders, “Mining Semantic Knowledge Graphs to Add Explainability to Black Box Recommender Systems,” IEEE Access, vol. 7, pp. 110563–110579, 2019, doi: 10.1109/ACCESS.2019.2934633.
W. Zhang, F. Liu, L. Luo, and J. Zhang, “Predicting drug side effects by multi-label learning and ensemble learning,” BMC Bioinformatics, vol. 16, no. 1, 2015, doi: 10.1186/s12859-015-0774-y.
G. El-Khawaga, M. Abu-Elkheir, and M. Reichert, “XAI in the Context of Predictive Process Monitoring: An Empirical Analysis Framework,” Algorithms, vol. 15, no. 6, 2022, doi: 10.3390/a15060199.
M. Garouani, A. Ahmad, M. Bouneffa, M. Hamlich, G. Bourguin, and A. Lewandowski, “Towards big industrial data mining through explainable automated machine learning,” Int. J. Adv. Manuf. Technol., vol. 120, no. 1–2, pp. 1169–1188, 2022, doi: 10.1007/s00170-022-08761-9.
J. Deng, H. Zhang, B. Yan, S. Lu, Z. Li, and Z. Yang, “Condition diagnosis method for hydraulic-system based on random forest-XGBoost stacking model,” Meas. Sci. Technol., vol. 37, no. 22, 2026, doi: 10.1088/1361-6501/ae6d22.
K. M. Sujon, R. Hassan, K. Choi, and M. A. Samad, “Accuracy, precision, recall, f1-score, or MCC? empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models,” J. Big Data, vol. 12, no. 1, 2025, doi: 10.1186/s40537-025-01313-4.
A. Zanellati, S. P. Zingaro, and M. Gabbrielli, “Balancing Performance and Explainability in Academic Dropout Prediction,” IEEE Trans. Learn. Technol., vol. 17, pp. 2140–2153, 2024, doi: 10.1109/TLT.2024.3425959.
P. Guleria and M. Sood, “Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling,” Educ. Inf. Technol., vol. 28, no. 1, pp. 1081–1116, 2023, doi: 10.1007/s10639-022-11221-2.
S. Ghimire et al., “Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course,” Comput. Educ. Artif. Intell., vol. 7, 2024, doi: 10.1016/j.caeai.2024.100331.
M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis,” J. Informetr., vol. 11, no. 4, pp. 959–975, 2017, doi: 10.1016/j.joi.2017.08.007.
N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” J. Bus. Res., vol. 133, pp. 285–296, 2021, doi: 10.1016/j.jbusres.2021.04.070.
K. Dedja, F. K. Nakano, K. Pliakos, and C. Vens, “BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor,” IEEE Access, vol. 11, pp. 41348–41367, 2023, doi: 10.1109/ACCESS.2023.3268866.
A. Ragno, B. La Rosa, and R. Capobianco, “Prototype-Based Interpretable Graph Neural Networks,” IEEE Trans. Artif. Intell., vol. 5, no. 4, pp. 1486–1495, 2024, doi: 10.1109/TAI.2022.3222618.
M. P. Bakht, M. N. H. Mohd, B. S. K. S. M. K. Ibrahim, N. Khan, U. U. Sheikh, and A. A.-H. Ab Rahman, “Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability,” Results Eng., vol. 25, 2025, doi: 10.1016/j.rineng.2024.103838.
S. Ahmed, M. I. Shanta, M. Ashrafuzzaman, and R. Hossen, “Integrating transformers to outperform classical and deep learning in ChatGPT-in-education sentiment analysis with an explainable web app,” Discov. Artif. Intell., vol. 6, no. 1, 2026, doi: 10.1007/s44163-026-01277-1.
N. Hidayat, L. Afuan, and H. R. Jannah, “Prescriptive Learning Analytics for Student Dropout: Integrating Temporal Velocity and Counterfactual Explanations in Longitudinal Data,” J. Comput. Theor. Appl., vol. 3, no. 4, pp. 627–646, 2026, doi: 10.62411/jcta.15920.
M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
N. J. van Eck and L. Waltman, “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, vol. 84, no. 2, pp. 523–538, 2010, doi: 10.1007/s11192-009-0146-3.
P. Mongeon and A. Paul-Hus, “The journal coverage of Web of Science and Scopus: A comparative analysis,” Scientometrics, vol. 106, no. 1, pp. 213–228, 2016, doi: 10.1007/s11192-015-1765-5.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eka Rahayu, Boni Oktaviana, Mufida Khairani, Arie Rafika Dewi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Universitas Harapan Medan
.png)
.png)
.png)
.png)
.png)
