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All Journal Jurnal Elemen Global Science: Journal of Information Technology and Computer Science
Jerhi Wahyu Fernanda
Universitas Islam Negeri Syekh Wasil Kediri
Computer Science & IT Other

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Undergraduate students’ mathematical reasoning in numeracy-based tasks: A Rasch model approach Dewi Hamidah; Jerhi Wahyu Fernanda; Zun Azizul Hakim; Galuh Nuril Lathifah
Jurnal Elemen Vol 12 No 2 (2026): April
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jel.v12i2.34118

Abstract

Mathematical reasoning is essential in higher education because it enables students to formulate conjecture, generalize patterns, and justify. This study examined undergraduate students’ mathematical reasoning, operationalized through conjecturing, generalizing, and justifying, in two numeracy tasks. A purposive sample of 185 mathematics education students from the first, third and fifth semesters at a State University in Kediri, Indonesia, participated in the study. Responses were scored using an analytic rubric and analyzed with the Rasch model to estimate item difficulty and person ability, evaluate item fit and reliability, and examine Differential Item Functioning (DIF) across gender, Grade Point Average (GPA), and semester level. The results indicated that conjecturing and justifying were the most challenging aspects for students, while evidence of generalizing was relatively limited. Rasch analysis verified the instrument’s validity and reliability, with all items satisfying fit requirements, while DIF analysis indicated no significant demographic bias. However, observed patterns suggested that female students tended to be more systematic and accurate, male students were generally more flexible but less consistent, and students with higher GPAs displayed stronger logical reasoning. These findings highlight the importance of instructional strategies that intentionally foster mathematical reasoning and suggest future research involving multiple institutions and longitudinal designs.
Predictive Modelling of Disease Patterns Using Time-Series Patient Data in Primary Healthcare Moh. Khoridatul Huda; Jerhi Wahyu Fernanda; Darmatasia
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.204

Abstract

Understanding and predicting disease distribution patterns in primary healthcare settings require models capable of integrating both spatial and temporal dimensions. Traditional statistical approaches often fail to capture complex non-linear relationships across locations and time, leading to delayed detection of disease clusters. Objective: This study aims to develop a spatiotemporal machine learning framework to identify and forecast potential disease hotspots using electronic primary care records from 2024. Methods: The dataset comprised 5,343 patient visit records containing temporal, geographic (village-level), and clinical attributes. Data preprocessing included temporal aggregation, spatial encoding, and feature normalization. Three models—Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot—were trained and evaluated. Model performance was assessed using AUC, F1-score, and spatial accuracy metrics to ensure both predictive precision and spatial coherence. Results: Analysis revealed a clear seasonal pattern, with disease incidence peaking between April and August. Spatial mapping identified consistent hotspots in Sungai Asam and Beringin, accounting for over 70% of total cases. Among all tested models, Multi-EigenSpot achieved the best performance (AUC = 0.91; F1 = 0.86), effectively capturing multi-cluster spatial variability across months. Conclusions & Implications: The findings demonstrate that spatiotemporal learning models can significantly enhance disease surveillance and early warning capabilities in primary healthcare systems. Integrating spatial intelligence with explainable machine learning improves predictive accuracy, supports evidence-based policy, and enables targeted interventions for emerging disease hotspots in resource-limited settings.
Co-Authors Darmatasia Galuh Nuril Lathifah Hamidah, Dewi Moh. Khoridatul Huda Zun Azizul Hakim