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E-MODUL INTERACTIVE E-MODUL SUPPORTED BY HEYZINE FLIPBOOK IN MATHEMATICS LEARNING.: INTERACTIVE E-MODUL SUPPORTED BY HEYZINE FLIPBOOK IN MATHEMATICS LEARNING. della yolanda putri; As Elly S; Yufitri Yanto
Journal of Mathematics Science and Education. Vol 8 No 2 (2025): Penelitian kependidikan Matematika
Publisher : Universitas PGRI Silampari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62112/journalofmathematicsscienceandeducation..v8i2.442

Abstract

This article aims to determine the impact of using interactive e-modules supported by Heyzine Flipbook in mathematics learning on students' learning outcomes and to assess students' mathematical problem-solving abilities. The method used is a Systematic Literature Review (SLR). A total of 12 articles related to the use of interactive e-modules supported by Heyzine Flipbook in mathematics learning from the years 2018 to 2024 were reviewed. These articles were sourced from Google Scholar and the Science and Technology Index (Sinta). The results of this Systematic Literature Review indicate that mathematics learning supported by Heyzine Flipbook can improve students' learning outcomes as well as enhance their mathematical problem-solving abilities.
SYSTEMATIC LITERATURE REVIEW INTERACTIVE E-MODUL SUPPORTED BY HEYZINE FLIPBOOK IN MATHEMATICS LEARNING. della yolanda putri; As Elly S; Yufitri Yanto
Journal of Mathematics Science and Education. Vol 8 (2025): Penelitian kependidikan Matematika
Publisher : Universitas PGRI Silampari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62112/journalofmathematicsscienceandeducation..v8i1.445

Abstract

This article aims to determine the impact of using interactive e-modules supported by Heyzine Flipbook in mathematics learning on students' learning outcomes and to assess students' mathematical problem-solving abilities. The method used is a Systematic Literature Review (SLR). A total of 12 articles related to the use of interactive e-modules supported by Heyzine Flipbook in mathematics learning from the years 2018 to 2024 were reviewed. These articles were sourced from Google Scholar and the Science and Technology Index (Sinta). The results of this Systematic Literature Review indicate that mathematics learning supported by Heyzine Flipbook can improve students' learning outcomes as well as enhance their mathematical problem-solving abilities.
Development of a Machine Learning-Based Clean Water Demand Forecasting Model for Decision Support at the Lubuklinggau City Water Utility yogi primadasa; Ihsan Verdian; Yufitri Yanto
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.28748

Abstract

Clean water demand forecasting is essential for supporting production planning and distribution management in municipal water utilities. This study developed an integrated machine learning and rule-based Decision Support System (DSS) for forecasting clean water demand at the Lubuklinggau Municipal Water Utility. The study used 60 monthly observations from 2021–2025, which resulted in 58 observations after preprocessing and feature engineering. Random Forest Regression (RFR) was evaluated using five-fold TimeSeriesSplit and compared with Support Vector Regression (SVR) using the same temporal validation framework. The results showed that RFR outperformed SVR, achieving an overall out-of-fold R² of 0.7153, with lower prediction errors than the comparative model. Feature importance analysis indicated that water production, lagged demand, population, and Non-Revenue Water were among the most influential predictors. The final RFR model was subsequently retrained using all 58 observations to generate a baseline forecast for January–December 2026, with monthly clean water demand ranging from 675,563.98 to 691,313.64 m³. The forecast outputs were integrated into a rule-based DSS to classify demand conditions and generate corresponding operational recommendations. The proposed framework demonstrates the integration of temporally validated machine learning forecasting with interpretable rule-based decision support. However, the DSS remains a prototype and requires expert validation, user acceptance testing, and operational evaluation before implementation.