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All Journal AlphaMath: Journal of Mathematics Education SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teladan: Jurnal Ilmu Pendidikan dan Pembelajaran Jurnal Riset Pendidikan dan Inovasi Pembelajaran Matematika (JRPIPM) Mosharafa: Jurnal Pendidikan Matematika Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Indonesian Journal of Mathematics Education Journal of Mathematical Pedagogy (JOMP) Riemann : Research of Mathematics and Mathematics Education
Gusti Uripno
Universitas PGRI Ronggolawe
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analysis of Artificial Intelligence Assisted Proof Process Through Principle of Mathematical Induction in Real Analysis Course Isnawati Lujeng Lestari; Mayang Sari; Gusti Uripno; Siti Suprihatiningsih; Firda Hariyanti; Ebenezer Bonyah
Journal of Mathematical Pedagogy (JoMP) Vol. 6 No. 2: July 2025
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jomp.v6n2.p94-102

Abstract

The low proficiency of Mathematics Education students in constructing mathematical proofs, especially using the principle of mathematical induction, highlights the need for enhanced learning approaches. One promising method is the integration of Artificial Intelligence (AI) into the proof process within Real Analysis courses. This study aims to describe how students carry out mathematical induction proofs with the assistance of AI. Ten voluntary students enrolled in Real Analysis participated in an initial test involving divisibility problem. From this group, two students were selected through maximum variation sampling based on their answer diversity and communication skills. One student employed a modulo-based approach, while the other used the divisibility-definition concept. Overall, the results demonstrate that AI significantly supports students in understanding problems, planning proofs, implementing strategies, and revising their reasoning. AI played a critical role in concept generation, solution evaluation, and embedded reflection across each stage of Polya’s problem-solving framework, combined with the three aspects of AI-assisted proof: construction, evaluation, and revision
Leveraging Contextual Worksheet to Reveal Students' Proofing in Counting Rules Gusti Uripno; Edy Nurfalah
Riemann: Research of Mathematics and Mathematics Education Vol. 7 No. 2 (2025): EDISI AGUSTUS
Publisher : Program Studi Pendidikan Matematika Universitas Katolik Santo Agustinus Hippo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38114/riemann.v7i2.114

Abstract

This study aims to explore how contextualized combinatorics tasks set within a cinema-themed worksheet support undergraduate students’ transition from concrete enumeration strategies to formal combinatorial reasoning through a qualitative case study design. Participants engaged with four scaffolded tasks that first had them list seating arrangements for three friends and subsequently required them to explain the principles governing ordered versus unordered selections, while their written responses and verbal protocols were analyzed using thematic coding of representational approaches and justification quality. Findings reveal that embedding tasks in a familiar narrative and providing multimodal supports such as tables and color-coded diagrams helped students explicitly identify variables, assumptions, and logical steps, enabling them to construct factorial-based arguments without reliance on rote computation. Although all participants ultimately derived correct combinatorial solutions, the depth and clarity of their proofs varied in relation to individual verbal reasoning skills, and common misconceptions were effectively addressed when scenarios contrasted ordered and unordered cases. This single-paragraph abstract highlights that progressively reducing concrete scaffolds within real-world contexts enhances both engagement and conceptual understanding in discrete mathematics, suggesting that culturally relevant narratives and adaptive supports merit further investigation in larger-scale, mixed-methods studies.
Pareto-TSP Decision Support Framework for Cafe Location Selection in Tuban Regency Andy Haryoko; Gusti Uripno; Suprapto; Mario Fahmi Syahrial
Riemann: Research of Mathematics and Mathematics Education Vol. 8 No. 2 (2026): EDISI AGUSTUS
Publisher : Program Studi Pendidikan Matematika Universitas Katolik Santo Agustinus Hippo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38114/riemann.v8i2.210

Abstract

The cafe industry in Indonesia faces a business failure rate of 60-70% within the first three years, primarily caused by poor location selection, lacking systematic, data-driven methods (Lahindah et al., 2020; Nugraheni & Suparna, 2024). Existing studies tend to address location selection and route optimization independently. This separation leaves a gap in integrated multi-objective frameworks applicable to the Indonesian small-to-medium enterprise (SME) context. Unlike prior single-domain approaches, this study contributes a unified, replicable Decision Support System (DSS) that simultaneously integrates the Analytic Hierarchy Process (AHP), Pareto Multi-Objective Optimization, and the Traveling Salesman Problem (TSP) heuristic for optimal cafe location selection and logistics route efficiency. A sequential explanatory mixed-method design was applied, combining quantitative computational modeling with qualitative expert validation, across eight candidate locations in Tuban Regency, East Java, using spatial data from the Central Bureau of Statistics, OpenStreetMap, and Google Places API. Seven weighted criteria were evaluated via AHP (consistency ratio < 0.1), Pareto frontier analysis was conducted to identify non-dominated solutions, and three TSP algorithms (Nearest Neighbor, 2-Opt, Simulated Annealing) were implemented and compared. Results indicate that location L01 (Jl. Basuki Rahmat) achieved the highest DSS score of 0.797, a ranking that remained stable across the full sensitivity range of DSS weightings scenarios tested. The Simulated Annealing algorithm produced the most efficient distribution route of 4.22 km, reducing travel distance by 5.2% compared to the Nearest Neighbor baseline. Empirical validation against real-world Tuban data, measured as the proportion of parameters matching field observations within a 10% deviation threshold, demonstrated an 87.5% agreementrate. The proposed framework provides a replicable, transparent, and data-driven decision tool for SME cafe investors and municipal spatial planners.
Co-Authors Abdul Haris Rosyidi Andy Haryoko Arief Budi Wicaksono Choiroz Zadit Taqwa Ebenezer Bonyah Edy Nurfalah Endah Budi Rahaju Firda Hariyanti Intan Rimawati Islami, Irma Fauztina Isnawati Lujeng Lestari Isnawati Lujeng Lestari Kartika Widya Putri Konstansia Hermiati Mario Fahmi Syahrial Mayang Sari Mayang Sari Milagros R. Baldemor Puji Rahayu Puji Rahayu Riska Andriani Rita Yuliastuti Rizki Kurniawan Rangkuti Siti Suprihatiningsih Suprapto Tatag Yuli Eko Siswono Tupulu, Nasri Warli Warli