Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 5 No. 3 (2026): June 2026

Analysis of Student Errors in Solving Problems Involving Curved-Surface Geometric Shapes Based on Newman’s Error Analysis

floricytha sihombing (State University of Medan)
Rifki Aidil Fikri (State University of Medan)
Amelia Putri (State University of Medan)
Sherlyta (State University of Medan)
Devina Zuhra Utami (State University of Medan)
Kairuddin (State University of Medan)



Article Info

Publish Date
15 Jun 2026

Abstract

This study aims to evaluate the errors made by students when solving problems involving three-dimensional shapes with curved sides, using Newman’s error analysis approach. The research employed a descriptive qualitative method and was conducted at Imelda Private Junior High School in Medan during the second semester of the 2025/2026 academic year, with the participation of 18 students selected through purposive sampling. Data collection tools consisted of written tests, interviews, and documentation. Data analysis was conducted by referring to Newman’s five stages of error: reading, comprehension, transformation, process skills, and coding. The research findings indicate that the most common errors were process skill errors, accounting for 25.9%, and transformation errors, accounting for 20.3%, while errors in the reading, comprehension, and coding stages were not identified. Students with low ability typically struggle to find the formula and proceed with the problem-solving process; students with moderate ability tend to make errors during calculations; whereas high-ability students successfully solve problems accurately and in an organized manner. Thus, it can be concluded that most student errors are caused by an inability to select the correct formula and a lack of precision during calculations. Therefore, it is crucial to implement teaching methods that focus on conceptual understanding and procedural skills to minimize the errors students make when tackling mathematical problems.

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Journal Info

Abbrev

JAIEA

Publisher

Subject

Automotive Engineering Computer Science & IT Control & Systems Engineering

Description

The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering ...