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All Journal Indonesian Journal on Learning and Advanced Education (IJOLAE) Jurnal Penelitian Sains Teknologi
Irwan Akib
Faculty of Teacher Training and Education, Universitas Muhammadiyah Makassar
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Transportation Other

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Learning Social Arithmetic of Low-Ability Student through the Context of Snacks and Money Risty Mustika Hardini; Rully Charitas Indra Prahmana; Irwan Akib; Masitah Shahrill
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 4, No. 1, January 2022
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v4i1.14308

Abstract

Low-ability students are evidenced to have difficulties in understanding the concept of abstraction in mathematics, such as social arithmetic problems. It is because low-ability students have an IQ below the average of 70 to 90. Most teachers find it challenging to discover what kind of learning approaches that may be suitable for improving their mathematical understanding. An alternative approach that can be used to improve the understanding of low-ability students is the Indonesian Realistic Mathematics Education (IRME) approach by using real contexts as a starting point for learning to make it easier for students to study the abstract material. This study aims to determine the learning process and the role of IRME in improving low-ability student’s mathematical cognitive abilities regarding the concept of social arithmetic. This study used the Single Subject Research (SSR) method with a single subject, and a seventh-grade student at one of the Junior High Schools in Depok, Yogyakarta. The research data collected in this study are audio and video recordings, photos, and student worksheets. The data collected was then analyzed using in and between analysis with A-B research design. The results showed that the IRME approach with snack and money con-texts could improve a low-ability student’s mathematical understanding of the social arithmetic concept. This context could be a starting point for teachers in teaching social arithmetic problems and be a reference for finding other contexts that can make mathematics learning more easy and joyful for low-ability students.
NuminaMath 7B: Revolutionizing Math Solving with Integrated Reasoning Advanced Generative AI Tools and Python REPL Adi Jufriansah; Irwan Akib; Naufal Ishartono; Azmi Khusnani; Tanti Diyah Rahmawati; Edwin Ariesto Umbu Malahina; Osniman Paulina Maure; Nova Tri Romadloni
Jurnal Penelitian Sains Teknologi Vol. 2, No. 1, March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/saintek.v2i1.15728

Abstract

The efficacy of NuminaMath 7B, an AI model that was created to address mathematical challenges, is assessed in this investigation. We evaluated the model's accuracy and efficiency against conventional methods through experiments that produced quantitative data. Qualitative data were collected through surveys and interviews with users to gain insight into their experiences and pinpoint areas for improvement. The survey results indicated that users found NuminaMath 7B to be pertinent, effective, and user-friendly, as evidenced by the exceptionally high average scores in user experience (95), perception of features and interface (90), and additional feedback (85). NuminaMath 7B was able to offer mathematical solutions with logical and detailed explanations as a result of the model's development through two phases of adjustments, which were conducted using the Chain of Thought (CoT) methodology and inspiration from the Tool-Integrated Reasoning Agent (ToRA) framework. Testing demonstrated that the model achieved a score of 29 out of 50 in the AI Math Olympiad competition, despite encountering difficulties in resolving more intricate problems. This study underscores the significance and urgency of AI technology, particularly in the field of mathematics, as well as the significant potential of AI models to facilitate a more comprehensive comprehension of mathematical concepts.
Co-Authors Adi Jufriansah Azmi Khusnani Edwin Ariesto Umbu Malahina Masitah Shahrill Naufal Ishartono Nova Tri Romadloni Osniman Paulina Maure Risty Mustika Hardini Risty Mustika Hardini Rully Charitas Indra Prahmana Rully Charitas Indra Prahmana Tanti Diyah Rahmawati