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Jurnal Penelitian Pendidikan IPA (JPPIPA)
Published by Universitas Mataram
ISSN : 24602582     EISSN : 2407795X     DOI : -
Core Subject : Science, Education, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics
Science Educational Research Journal is international open access, published by Science Master Program of Science Education Graduate Program University of Mataram, contains scientific articles both in the form of research results and literature review that includes science, technology and teaching in the field of science. The Science Educational Research Journal is published twice in a year in January and July editions. The editors receive writing in Indonesian or English, either from the university or from outside the university.
Arjuna Subject : -
Articles 5,869 Documents
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English Language Jauhari, M. Reza; Suhartanto, Ery
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12650

Abstract

Reliable precipitation data are essential for hydrological modeling in data-scarce basins. This study evaluates five statistical bias-correction methods—Correction Factor (mean-ratio scaling), Linear Scaling (mean adjustment), Linear Regression, Local Intensity Scaling (LOCI; wet-day threshold and intensity adjustment), and Power Transformation—to improve satellite rainfall for the Gembong Watershed, Pasuruan, East Java, Indonesia. We used daily TRMM (2004–2013) and GPM IMERG (2014–2023) estimates harmonized to a common grid and time step and compared them with gauges using Pearson’s r, Nash–Sutcliffe Efficiency (NSE), and the RMSE-observation standard deviation ratio (RSR). LOCI delivered the best overall balance (NSE = 0.92; r = 0.84; RSR = 0.55), while Linear Scaling achieved a slightly lower NSE but the smallest RSR (NSE = 0.87; RSR = 0.49). Power Transformation showed limited skill (NSE = 0.57; RSR = 0.90) despite high correlation. Ranking prioritized NSE with r and RSR as supporting metrics. The coastal-lowland setting of Pasuruan—with strong convective rainfall and heterogeneous land use—makes accurate bias correction particularly consequential for flood and water-resources analysis. We conclude that LOCI’s adaptive thresholding is well-suited to such regimes and that the comparative framework aids method selection for similar data-scarce watersheds.
Model Ilmu Perilaku dalam Pendidikan: Analisis Pemodelan Persamaan Struktural (SEM) tentang Kecerdasan Emosional, Iklim Sekolah, dan Disiplin Guru pada Anak Usia Dini Setyowati, Wiwik; Sutarto, Joko; Diana
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12711

Abstract

This study addresses the problem of low teacher discipline in early childhood education (ECE), which is influenced by various psychological and organizational factors. The research aimed to analyze the relationships among emotional intelligence, school climate, work motivation, and teacher discipline. A mixed-methods approach was used involving 100 ECE teachers in Semarang City selected through purposive sampling. Quantitative data were obtained through standardized questionnaires and analyzed using structural equation modeling (SEM), while qualitative data were collected through semi-structured interviews with teachers and principals. SEM analysis showed a good model fit (χ²/df = 1.89; CFI = 0.95; TLI = 0.94; RMSEA = 0.052). Emotional intelligence significantly affected work motivation (β = 0.41, p < 0.001) and teacher discipline (β = 0.34, p = 0.001). School climate also significantly influenced work motivation (β = 0.38, p < 0.001) and discipline (β = 0.29, p = 0.003). Work motivation had a positive effect on teacher discipline (β = 0.30, p = 0.001) and partially mediated the effects of emotional intelligence and school climate. The study concludes that strengthening emotional intelligence, building a supportive school climate, and enhancing work motivation are essential strategies to improve teacher discipline in ECE settings.
Analysis of Learning Motivation of Master’s Students Using Learning Management System Sapitri, Eva Ramdanika; Budiningsih, C Asri
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12712

Abstract

This study examines the learning motivation of Master’s students in English Education in their use of Learning Management Systems (LMS). A descriptive quantitative survey was conducted involving 50 students from several universities in Java and Lombok. Two variables were measured—intrinsic and extrinsic motivation—adapted from established motivational constructs. Data were analyzed descriptively to identify students’ motivational tendencies without generalizing beyond the sample. The findings show that intrinsic motivation (M = 4.32) is slightly higher than extrinsic motivation (M = 4.01) on a 5-point scale. Intrinsic motivation is reflected in students’ interest in engaging with course materials and the enjoyment of structured LMS-based learning activities. Extrinsic motivation appears in responses related to academic expectations and lecturer guidance, though these external factors were rated lower than internal drivers. The results indicate that students perceive LMS use as supportive of their learning motivation, particularly in facilitating access to materials and organizing learning tasks. However, the study does not assess causal effects of LMS use on academic outcomes. The findings highlight the need for LMS designs that strengthen both intrinsic and extrinsic motivational aspects to enhance student engagement in English education contexts.
A Hierarchical Bayesian Model of Multi-Hazard Impacts on Property Prices in the Jakarta Metropolitan Area Fachrurrozi; Ambat, Jordi Enal; Parhusip, Hanna Arini; Trihandaru, Suryasatriya
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12717

Abstract

This study examines the complex relationship between multi-hazard disaster risks and property prices in the Jakarta Metropolitan Area, one of the world's most disaster-prone urban regions. The research investigates how various natural hazards, including floods, earthquakes, and other environmental risks, influence real estate values across 138 districts encompassing 15,758 property data. This study pioneers the integration of hierarchical Bayesian modeling with causal machine learning techniques to quantify multi-hazard impacts on property prices, providing the first comprehensive analysis of disaster risk interactions in Indonesian real estate markets. We employ methodological triangulation across Bayesian inference, causal forests, and spatial econometrics to ensure robust causal identification. We employ a multi-methodological approach combining spatial analysis, hierarchical Bayesian modeling, and causal forest algorithms on a dataset of 15,758 properties. The analysis includes Moran's I for spatial autocorrelation (0.73 for risks, 0.65 for prices), PyMC for Bayesian inference with 12,000 MCMC samples, and EconML for causal effect estimation with heterogeneous treatment effects. Properties with high disaster risk experience an 12.2% price discount (95% CI: -20.5%, -3.7%), with each unit increase in average risk score reducing prices by 4.3% (95% CI: -7.9%, -0.4%). Spatial clustering is highly significant (Moran's I = 0.73, p < 0.001). Heterogeneous effects reveal progressive impacts from 3.2% in bottom quintile to 9.4% in top quintile. Policy simulation demonstrates that comprehensive flood mitigation could increase total property values by 840.6 billion IDR, generating an average price increase of 14.8% with benefit-cost ratio exceeding 3:1.
Development of a Rapid Diagnostic Method for Simultaneous Detection of Streptococcus viridans in Cases of Heart Disease Pestariati, Pestariati; Suhariyadi, Suhariyadi; Asryadin, Asryadin; Yuniati, Nilasari Indah
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12730

Abstract

Heart disease is a leading cause of death worldwide, with a complex pathophysiology often involving interactions between genetic factors, the environment, and pathogenic microorganisms. Streptococcus viridans is a clinically significant pathogenic bacterium associated with infections of the cardiovascular system, including infective endocarditis, pericarditis, and other complications. However, timely and accurate diagnosis of this bacterial infection in cases of acute heart disease is often challenging, requiring rapid and sensitive diagnostic methods. Currently, diagnostic methods for detecting Streptococcus viridans in cases of acute heart disease tend to be time-consuming; therefore, developing a rapid diagnostic method that can detect both bacteria simultaneously is crucial. The aim of this study is to develop a rapid and sensitive diagnostic method for detecting the presence of Streptococcus viridans in samples from patients with heart disease. The method used is the identification of specific genes, the design of primer sequences, and the design of probes using specific 16s rRNA genes using bioinformatics techniques. Based on the research results obtained primer pair sequences are: oligonucleotide primer forward 5’-GCGACGATACATAGCCGAC-3’; primer reverse is 3’- CGAGCCAGTCTGAAAGC-5’, while the probe sequence is 5’-GACTGAGACACGGCCCAGACTC-3’. Primer and probe pair quality tests showed very good primer and probe quality for amplification with a 120 bp amplification product. Suggestions in the research are that it is necessary to continue with qPCR optimization to determine the melting temperature which is then carried out sensitivity tests of primer pair sequences and specific 16s rRNA Streptococcus viridans gene probes.
Development of an Assessment Instrument for Understanding Physics Concepts and Nationalism Attitudes of Learners on Newton's Laws Material Ernasari; Dewi, Nila Mutia; Meilina, Ike Lusi; Faresta, Rangga Alif
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12735

Abstract

This study aimed to develop and validate an assessment instrument to measure students’ understanding of Newton’s laws and their nationalism attitudes. The instrument consisted of 15 essay questions on Newton’s laws and a 30-item Likert-scale questionnaire on nationalism attitudes. Content validity was evaluated by eight experts (lecturers, teachers, and peers) using Aiken’s V index, showing high validity. Empirical validation was conducted with 275 students from public senior high schools in Bima Regency, West Nusa Tenggara. Data were analyzed using Item Response Theory with the Partial Credit Model (PCM) via QUEST. Results indicated that all concept understanding items fit the PCM criteria, with acceptable reliability values ranging from 0.72 to 0.98. For the nationalism instrument, 29 items met the fit criteria, while one item was rejected. Overall, the developed instruments demonstrated strong validity and reliability, and are suitable for assessing both cognitive understanding of Newton’s laws and students’ nationalism attitudes.
Learning Innovation: The Impact of AI-Based Video Media in Improving the Skills of Adolescent Girls for Stunting Prevention Ramdani, Hasbi Taobah; Murtiningsih; Rudhiati, Fauziah; Maryati, Ida; Nurjanah, Nunung
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12742

Abstract

This study examines the effect of Artificial Intelligence (AI)-based video media on improving adolescent girls’ skills in preventing stunting. A quantitative quasi-experimental design was applied using a pre-test–post-test approach with a control group. The intervention group received AI-based video learning, while the control group used ChatGPT-based text interaction. Results show a significant improvement in the intervention group, with a post-test mean score of 82.19 compared to 41.16 in the control group. The intervention group also demonstrated a narrower confidence interval (78.55–85.83), indicating higher consistency in skill acquisition, whereas the control group showed a wider range (36.27–46.05). These findings confirm that visual, structured, and AI-enhanced learning materials improve comprehension and practical skills more effectively than text-based interactions alone. The study reinforces the importance of integrating innovative digital media into health education programs. In conclusion, AI-based video media is a highly effective tool for strengthening adolescents’ stunting prevention skills and holds strong potential for broader implementation in early prevention strategies.
Thermal Image-Based Classification of Okra Maturity: A Comparative Study of CNN, SVM, and LSTM Sumardi, Tedi; Robiyana, Iqbal; Permana, Roni; Suhendra, Muhamad Agung
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12748

Abstract

Post-harvest quality assessment remains a major challenge in agriculture, particularly for okra (Abelmoschus esculentus), which deteriorates rapidly due to high moisture content. Traditional grading based on manual inspection often results in inconsistency and product damage. This study explores thermal imaging as a non-destructive alternative for okra maturity classification. A dataset of 501 thermal images was acquired under controlled conditions and analyzed using three machine learning models: Convolutional Neural Network (CNN), Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) features, and Long Short-Term Memory (LSTM) network. Experimental results show that CNN achieved the highest accuracy (99.01%), outperforming SVM (95.05%) and LSTM (91.09%). Confusion matrix and ROC analyses confirmed CNN’s superiority in capturing spatial thermal patterns related to maturity stages. Compared with RGB or hyperspectral imaging reported in prior studies, thermal imaging integrated with AI provides a more robust, illumination-independent, and non-destructive solution. The findings demonstrate the potential of CNN-based thermal imaging systems for automated sorting of okra in agricultural supply chains. Future work will focus on larger datasets, multi-class maturity levels, and real-time implementation to enhance practical deployment in post-harvest management.
Integrating Local Wisdom and Generative AI in Ethno-STEM Materials: A Case Study in Border Education Nawawi; Nur, Syafrial; Januardi, Arif; Moad
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12795

Abstract

The Indonesia-Malaysia border region faces educational challenges in the form of limited learning resources, low digital literacy, and lack of pedagogical innovation. This research aims to develop and evaluate generative AI-assisted Ethno-STEM teaching materials based on Dayak and Malay local wisdom to increase student engagement and creativity. The research uses a mixed methods design with a sequential exploratory model. The qualitative phase was carried out through interviews and observations to explore local wisdom, followed by the preparation of Ethno-STEM-based modules with the integration of AI interactive media, and the quantitative phase involved a limited trial of 30 students of class X and 3 science teachers at SMA Negeri 1 Sajingan Besar. The data were analyzed using descriptive statistics and N-gain calculations. The results showed that the modules were positively rated by students, with 87% stating that they agreed or strongly agreed on the aspects of attractiveness, cultural relevance, and ease of use. The improvement in learning outcomes was also significant, indicated by an average N-gain of 0.91 (high category). The recapitulation of student creativity showed achievements in the category of quite creative, with elaboration obtaining the highest score (55.8%). These findings confirm that the integration of Ethno-STEM and generative AI not only improves conceptual understanding, but also fosters students' critical-creative thinking skills, and is worthy of being recommended for schools in border areas.
Efektivitas Rempah dan Herbal sebagai Antioksidan Alami dalam Pengawetan Ikan Tuna (Thunnus sp.) dan Ikan Nila (Oreochromis niloticus): Meningkatkan Keamanan Mikroba dan Daya Simpan Widhianata, Hani; Lidiastuti, Arinda Eka
Jurnal Penelitian Pendidikan IPA Vol 11 No 11 (2025): November: In Progress
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i11.12802

Abstract

Fish is a protein-rich and highly nutritious food. Natural herbs and spices contain antioxidant and antibacterial compounds that may enhance microbiological safety and extend shelf life. This study aimed to evaluate natural herb and spice-based processing methods for preserving tuna (Thunnus sp.) and nile tilapia (Oreochromis niloticus), along with the effectiveness of specific spices in reducing oxidation and microbiological contamination. Two marination approaches were applied, using both uncooked and cooked spice mixtures. Three recipes were prepared, recipe 1: garlic, pepper, salt, turmeric, coriander, lemongrass, recipe 2: garlic, pepper, salt, rosemary, oregano, thyme, lemon and recipe 3: garlic, pepper, salt, coriander, lemongrass, chili pepper, curry leaves, asam sunti. Unmarinated fish served as the control. All samples were vacuum-packed in retort pouches and stored at 1-4 °C for 1 h, 3, 5, and 7 days. Microbial analysis included total aerobic bacteria, Escherichia coli, Coliforms, Salmonella spp., Shigella spp., yeast and mold. Marination with herbs and spices significantly reduced microbial contamination compared to the control. The combined use of herbs and spices effectively improved microbial safety and extended the shelf life of tuna and nile tilapia. These findings support the application of herbs and spices as natural preservation agents in fish products, offering a safer and more sustainable alternative in fish preservation.

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