Nurmala R
Universitas Borneo Tarakan, Indonesia

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Kajian Faktor Penentu Kematian Akibat Demam Berdarah Dengue di Kota Palangka Raya Menggunakan Regresi Poisson Fitriayu Fitriayu; Nurmala R
PESHUM : Jurnal Pendidikan, Sosial dan Humaniora Vol. 5 No. 3: April 2026
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/peshum.v5i3.16964

Abstract

Demam Berdarah Dengue (DBD) masih menjadi masalah kesehatan masyarakat di Indonesia, termasuk di Kota Palangka Raya. Penelitian ini bertujuan menganalisis pengaruh faktor lingkungan terhadap kematian DBD menggunakan regresi Poisson. Studi kuantitatif ini menggunakan data sekunder Dinas Kesehatan tahun 2023 dengan 11 puskesmas sebagai unit analisis (total sampling). Variabel dependen adalah jumlah kematian DBD, sedangkan variabel independen meliputi jamban layak, rumah sehat, pengelolaan sampah, air bersih, dan PHBS. Analisis dilakukan melalui statistik deskriptif, uji asumsi, estimasi Maximum Likelihood, uji Wald (α = 0,10), seleksi AIC/BIC, dan residual Pearson. Hasil menunjukkan bahwa model terbaik melibatkan variabel rumah sehat dan PHBS. Rumah sehat berpengaruh negatif (IRR = 0,934), menandakan peningkatan kualitas hunian menurunkan risiko kematian. Sebaliknya, PHBS berpengaruh positif (IRR = 1,054), yang mengindikasikan kemungkinan bias indikator atau ketidaksesuaian dengan kondisi lapangan. Temuan ini menegaskan pentingnya intervensi lingkungan dalam menurunkan fatalitas DBD serta perlunya evaluasi implementasi PHBS agar lebih efektif dan kontekstual.
Reframing Conceptual Understanding in Elementary Mathematics Through Adaptive Deep Learning: A Pedagogical Synthesis for DataInformed Personalized Learning jusmawati; Nurmala R
Elementary Journal of Education Vol. 1 No. 2 (2025): Pillar PGSD: Elementary Journal of Education
Publisher : Elementary Journal of Education

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Abstract

Background: The rapid expansion of artificial intelligence in education has transformed elementary mathematics instruction. However, most studies remain technology-centered and insufficiently examine how adaptive deep learning reshapes students’ conceptual understanding and pedagogical practice. Aims: This study synthesizes existing research to reframe adaptive deep learning as a pedagogical framework that supports conceptual understanding through data-informed personalized learning in elementary mathematics. Methods: A qualitative systematic literature review was conducted on peer-reviewed studies focusing on adaptive deep learning applications in mathematics education. Selected articles were analyzed using thematic synthesis to identify recurring pedagogical mechanisms, conceptual outcomes, and instructional implications. Results: The synthesis indicates that adaptive deep learning enhances conceptual understanding by diagnosing misconceptions, personalizing instructional pathways, and integrating dynamic visual representations of abstract concepts. Learning analytics generated by adaptive systems support teacher decision-making and formative assessment. The reviewed studies consistently report improvements in engagement, conceptual clarity, and learning equity, particularly in heterogeneous elementary classrooms. Conclusion: Adaptive deep learning should be conceptualized not merely as instructional technology but as a pedagogically grounded framework that restructures how conceptual understanding develops. When integrated with teacher mediation and curricular alignment, it fosters inclusive, responsive, and concept-oriented learning environments. Its transformative potential lies in harmonizing algorithmic adaptation with professional judgment, thereby strengthening both instructional precision and conceptual depth in elementary mathematics education.
Realistic mathematics education and wordwall to enhance students’ mathematics learning motivation Sirwanti; Naimah; Nur Asrawati; Nurmala R; Muhammad ikram
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 3 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i3.1523

Abstract

Background: Mathematics learning motivation among junior high school students remains low due to procedural, decontextualized instruction that fails to connect mathematical concepts to students' real-life experiences, as observed in several junior high schools in Central Indonesia. Purpose: This study examines the effects of Realistic Mathematics Education assisted by Wordwall, Realistic Mathematics Education without Wordwall, and conventional instruction on students' motivation to learn mathematics. It further examines the potential contribution of context-based instructional design reinforced by digital media to motivational outcomes. Methods: A quantitative quasi-experimental design was employed involving three intact classes: an experimental class implementing RME with Wordwall, an experimental class implementing RME without Wordwall, and a control class receiving instruction without RME reinforcement. Students’ mathematics learning motivation was measured using a validated questionnaire, and the data were analyzed descriptively and inferentially using independent samples t-tests to compare motivation levels across groups. Result: The findings indicate significant differences in mathematics learning motivation between the experimental and control groups. Both classes exposed to RME-based instruction demonstrated higher motivation than the control class, with the class using RME reinforced by Wordwall showing the highest levels of motivation, suggesting that the integration of digital game-based media further enhances student engagement. Conclusion: The findings indicate that context-based RME reinforced with Wordwall was associated with higher mathematics learning motivation than conventional instruction among the junior high school students examined, suggesting a potential contribution of instructional design quality to students' motivational engagement.