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The role of engagement, content, and understanding of numeracy literacy in physics creativity projects Susilawati Susilawati; Andi Fadllan; Miftahida Pratama Mareta; Masturi Masturi; S Xaphakdy
Momentum: Physics Education Journal Vol. 9 No. 2 (2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/mpej.v9i2.11544

Abstract

Pre-service physics teachers engagement and content material influence the strategies for implementing creative learning. Behavioural, emotional, cognitive, and reciprocal aspects are less visible during classroom learning. Content material can present several interesting topics that are more engaging if pre-service physics teachers s are directly involved through demonstrations, experiments, and learning in their surroundings. The approach aims to build interest and active engagement to complete project activities. The study seeks to determine whether project-based learning with active pre-service physics teachers engagement, content delivery, and numeracy literacy understanding can be enhanced through creative projects. The research uses a quasi-experimental design with control and experimental groups using pretest and post-test. A total of 39 pre-service physics teachers s were enrolled in this study, divided into two groups: the experimental group consisted of 21 pre-service physics teachers s who received instruction using the STEM-based creativity project method, and the control group consisted of 18 pre-service physics teachers s who received instruction using the inquiry and assignment method. Both classes were analysed based on pre-service physics teachers engagement, content material presentation, and numeracy literacy understanding. The study measured pre-service physics teachers performance before and after the intervention using tests. Pretest findings showed a p-value greater than 0.05 between the control and experimental groups, indicating no statistically significant difference between the two groups. A post-test showed a p-value of <0.001 between the groups, indicating that the experimental group significantly outperformed the control group. The findings of this study suggest that pre-service physics teachers engagement, content material packaging, and numeracy literacy understanding influence creative projects. This study supports the development of STEM-based creativity project learning and active pre-service physics teachers participation.
Everyday Science in Action: Training on Simple Research for Enhancing Scientific Literacy among Science Teachers at MA Salafiyah Kajen Pati Susilawati; Irman Said Prastyo; Hamdan Hadi Kusuma; Ulil Albab; Ulya &#039;Uyun Latifah
Community Research and Application Journal (CRAJ) Vol. 2 No. 2 (2026): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Scientific literacy is essential for both teachers and students to understand natural phenomena and make informed decisions in daily life. However, many teachers face challenges in implementing research-based approaches to enhance scientific literacy in their classrooms. This community service project aimed to provide training on simple research methods for improving scientific literacy to science, mathematics, and geography teachers at MA Salafiyah Kajen Pati. Conducted on November 19, 2025, the training involved 17 teachers and introduced the PISA (Programme for International Student Assessment) framework for scientific literacy, encompassing four key components: science content knowledge, science processes, application contexts, and scientific attitudes. The training emphasized the concept of "everyday science" - recognizing that scientific principles are embedded in common daily activities such as cooking, walking, bathing, and using technology. Teachers were introduced to simple research approaches using everyday contexts to strengthen students' scientific literacy, covering the four aspects of scientific literacy: science as a body of knowledge, as an investigative tool, as a way of thinking, and as interaction between science, technology, environment, and society. The training demonstrated how teachers can utilize familiar daily activities as contexts for developing students' scientific competencies including identifying scientific questions, collecting evidence, drawing conclusions, and communicating findings. Participants engaged in discussions about implementing context-based learning in their subject areas and designing simple research activities that connect scientific concepts to students' lived experiences. The program successfully equipped teachers with frameworks and strategies for conducting simple research to enhance scientific literacy, emphasizing that science education should extend beyond textbooks and laboratories to encompass real-world applications.
Community-Based Health Screening: Assessment of Oxygen Saturation (SpO2), Heart Rate, and Body Temperature in RT 005 RW IV, Kelurahan Gondoriyo, Ngaliyan, Semarang Affa Ardhi Saputri; Susilawati; Hartono; Istikomah; Yeni Rossa Damayanti; Milta Bima Say Putri
Community Research and Application Journal (CRAJ) Vol. 2 No. 2 (2026): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Vital signs, namely oxygen saturation (SpO₂), heart rate (HR), and body temperature, are fundamental physiological indicators of overall health status. Their periodic monitoring in the general population supports early identification of oxygenation disorders, cardiovascular conditions, and infection or inflammation before these progress to more serious conditions. This research-based community service activity carried out a free vital-sign screening for residents of RT 005 RW IV, Kelurahan Gondoriyo, Ngaliyan, Semarang, on 28 June 2026, using a pulse oximeter for SpO₂ and HR and a non-contact infrared thermometer for body temperature. Twenty community members were screened, with duplicate SpO₂ and HR measurements taken for each participant; the second reading of each was used as the analysis reference after the first-attempt readings were assessed as reflecting an initial probe signal-stabilization phase. Results showed a mean SpO₂ of 96.8% (range 79–100%), with 90% (18 of 20 participants) within the normal range (≥95%) and one participant showing severe hypoxemia (79%) requiring urgent referral. Mean HR was 59.2 beats per minute (range 35–100), with only 35% (7 of 20) within the standard resting range of 60–100 bpm; two participants showed severe bradycardia (35 and 37 bpm) requiring immediate electrocardiographic evaluation. Mean body temperature was 35.65°C (range 34.5–36.3°C), a pattern consistent with the known sensitivity of non-contact infrared thermometry to outdoor ambient conditions. The activity produced individualized referral recommendations for participants with critical findings and supports periodic, six-monthly community vital-sign screening, particularly for residents above 40 years of age.
Classification of Alzheimer's Disease Based on Texture Characteristics in Magnetic Resonance Imaging Images Using the Random Forest Method Ibnul Bahar Al Basith; Muhammad Ulil Albab; Nani Kartika Dewi; Hartono; Affa Ardhi Saputri; Susilawati; Harisma Nugraha
Journal of Holistic Medical Technologies (JHMT) Vol. 2 No. 2 (2026): June
Publisher : Konsorsium Pengetahuan Innoscientia (KOPINNOS)

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Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and one of the leading causes of dementia worldwide. Early detection is critical to slowing disease progression and improving patient outcomes. While Magnetic Resonance Imaging (MRI) is commonly used for AD diagnosis, analyzing the high-dimensional data remains challenging. This study explores the use of texture features from MRI images to improve the accuracy of AD classification using machine learning. This research utilized MRI brain images from publicly available datasets to classify early-stage Alzheimer's disease. The Random Forest algorithm handled large, complex datasets while minimizing overfitting. Texture features, including Histogram, Gray Level Co-Occurrence Matrix (GLCM), and Gray Level Run-Length Matrix (GLRLM), were extracted to highlight microstructural changes in the brain, particularly in the hippocampus and cortex—critical areas affected by AD. The model achieved an accuracy of 88%, precision of 85%, and Recall of 92% in the 10th fold using the WEKA platform, demonstrating its effectiveness in detecting early neurodegenerative changes. The Random Forest model, combined with texture feature extraction, provides a computationally efficient approach for the early detection of Alzheimer's disease. By identifying subtle changes in brain tissue, this method can potentially support clinicians in early diagnosis and intervention planning. Future research should focus on integrating additional imaging modalities and clinical data to enhance diagnostic accuracy and reliability.