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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPP (Jurnal Pendidikan dan Pembelajaran)
Verra Budhi Lestari
Universitas Media Nusantara Citra
Computer Science & IT Engineering

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Research Evolution of Artificial Intelligence in Mathematics Learning: A Bibliometric Review from 2015 to 2026 Nadya Syifa Utami; Hafsah Adha Diana; Verra Budhi Lestari; Sani Sahara
JPP (Jurnal Pendidikan dan Pembelajaran) Vol. 33 No. 1 (2026)
Publisher : Lembaga Pengembangan Pendidikan dan Pembelajaran, Universitas Negeri Malang in Collaboration with Asosiasi Pendidik dan Pengembang Pendidikan Indonesia (APPI)

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Abstract

Background: The recent growth of Artificial Intelligence (AI) has attracted increasing attention in mathematics learning research, yet a comprehensive understanding of its development remains limited.Objective: This study aims to explore the research evolution of AI in mathematics learning from 2015 to 2026 using a bibliometric approach.Method: Data were collected from the Scopus database, yielding 169 publications comprising journal articles and conference papers. Analysis was conducted using Bibliometrix and VOSviewer to examine publication trends, leading sources, contributing countries, influential references, keyword cooccurrence, and thematic evolution.Results: The findings reveal a significant growth in publications, particularly after 2021, reflecting expanding research interest. The United States and China are the most productive contributors, while several recent works demonstrate strong citation impact. Keyword and thematic analyses reveal a focus on the intersection of AI and mathematics education, with emerging topics such as generative AI, chatbots, and personalized learning.Conclusion: Overall, the research of AI in mathematics learning has shifted from general technological exploration toward more pedagogically oriented applications. These findings suggest that educators and curriculum developers should prioritize AI tools for personalized and effective learning, while researchers are encouraged to focus empirically evaluating their impact in real classroom contexts.
Quantifying Target Leakage in SEER Breast Cancer Mortality Prediction Sestri Novia Rizki; Vani Maharani Nasution; Eko Amri Jaya; Verra Budhi Lestari; Elfina Maulid
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.8105

Abstract

Recent machine learning studies on the SEER Breast Cancer dataset report 95–98% accuracy (2022–2025), but their methods are rarely examined. Because the dataset is imbalanced (84.7% Alive), accuracy alone is a poor discrimination metric — a trivial always-Alive classifier scores near 85% — so this audit quantifies leakage inflation on ROC-AUC, computed identically across 12 models. We measure how much performance stems from target leakage via Survival Months, which encodes retrospective survival time unavailable at prediction. Undetected, such leakage could lead clinicians to overtrust a model's risk estimates, risking inappropriate care decisions. We train 12 models under three conditions: a) naïve, with Survival Months and a non-stratified split; b) leakage-free with stratified evaluation; and c) fixed-horizon (36- and 60-month) binary mortality, excluding censored cases. Under Condition A, the best ROC-AUC is 0.889 (accuracy 0.899). Removing leakage yields a consistent AUC ceiling of 0.74–0.77 across all 12 models — an inflation of +0.07 to +0.20 AUC points. SHAP shows Survival Months dominates feature importance by 3.0–4.3× under leakage; imbalance-handling methods (BorderlineSMOTE, class weighting) raise minority-class F1 but lower mean Brier Skill Score to −0.585 and −0.597. The contribution is the audit methodology itself — a reusable protocol for detecting leakage-driven inflation in registry-based clinical prediction.
Co-Authors Eko Amri Jaya Elfina Maulid Hafsah Adha Diana Nadya Syifa Utami Sani Sahara Sestri Novia Rizki Vani Maharani Nasution