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All Journal Indonesian Community Empowerment Journal Sriwijaya Journal of Radiology and Imaging Research Sriwijaya Journal of Obstetrics and Gynecology Enigma in Education
Hesti Putri
Department of Informatics Science, CMHC Research Center, Palembang, Indonesia
Religion Humanities Biochemistry, Genetics & Molecular Biology Dentistry Education Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Frugal Innovation in Education: Designing and Evaluating Low-Bandwidth, Asynchronous Learning Systems for Remote Indonesian Schools Hesti Putri; Maya Enderson; Jasmila Tanjung; Matilda Munoz; Sarah Armalia; Jovanka Andina; Kevin Setiawan; Sudarto Sudarto; Khalil Jibran; Jasmine Alieva
Enigma in Education Vol. 3 No. 1 (2025): Enigma in Education
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/edu.v3i1.95

Abstract

The promise of educational technology (EdTech) to democratize learning in Indonesia is consistently undermined by a profound digital divide, particularly in remote and archipelagic regions where internet connectivity is poor and infrastructure is limited. This study explores the application of frugal innovation principles as a direct and context-aware strategy to address this challenge. A multi-phase, mixed-methods Design-Based Research (DBR) methodology was employed over 18 months. The study involved the collaborative design, development, and implementation of "Lentera," a low-bandwidth, asynchronous, and offline-first learning system, in six remote primary schools in the Maluku Islands. A quasi-experimental design compared three intervention schools with three control schools over one academic year. Data collection was extensive, including pre- and post-intervention literacy and numeracy assessments, System Usability Scale (SUS) surveys, system usage logs, semi-structured interviews with 18 teachers, and over 80 hours of classroom observation. Data were analyzed using a two-level Hierarchical Linear Model (HLM) to account for the clustered nature of students within schools. The Lentera system demonstrated high feasibility and positive user adoption, with offline peer-to-peer sharing proving to be a critical feature for content distribution. Quantitative analysis revealed a statistically significant and substantial improvement in learning outcomes for the intervention group in both literacy (γ = 11.85, p < 0.001) and numeracy (γ = 12.91, p < 0.001) compared to the control group, after controlling for pre-test scores. The mean System Usability Scale (SUS) score was 78.5, indicating well-above-average usability. Qualitative findings, drawn from a wide range of teacher interviews and classroom observations, highlighted the system's effectiveness in supporting student-centered, differentiated instruction and fostering teacher collaboration, aligning with the core principles of Indonesia's Kurikulum Merdeka. The study provides compelling evidence that frugal innovation, embodied in a context-aware learning system, presents a viable, effective, and scalable pathway to enhancing educational quality and equity in resource-constrained environments.
Evaluating Generative AI as a Pedagogical Tool for Creative Problem-Solving in University Classrooms Hesti Putri; Khalil Jibran
Enigma in Education Vol. 3 No. 2 (2025): Enigma in Education
Publisher : Enigma Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61996/edu.v3i2.117

Abstract

This study investigated the effectiveness of generative artificial intelligence (AI) as a pedagogical tool for enhancing creative problem-solving (CPS) skills and divergent thinking (DT) among undergraduate students. A quasi-experimental pretest-posttest control group design was employed with 120 education students at a private university in Palembang, Indonesia. Participants were assigned to an experimental group (n = 60) that engaged in a 12-week AI-assisted learning intervention within an Educational Psychology course, and a control group (n = 60) receiving conventional instruction. Creative problem-solving was measured using the Creative Problem-Solving Performance Inventory (CPSPI, α = 0.89), while divergent thinking was assessed through an adapted Torrance Tests of Creative Thinking (TTCT, ICC = 0.91). Results from mixed ANOVA revealed a significant interaction effect for CPS, F(1,118) = 89.34, p < .001, partial η² = 0.431. MANOVA confirmed significant multivariate differences across all outcome measures, Pillai’s V = 0.482, F(5,114) = 21.24, p < .001. Large effect sizes were observed for CPS (Hedges’ g = 1.66) and DT (Hedges’ g = 1.18). These findings suggest that structured integration of generative AI into university pedagogy can substantially improve students’ creative problem-solving and divergent thinking capacities.
Radiomics-Based Machine Learning for Automated Detection and Rupture-Risk Stratification of Cerebral Vascular Malformations: A Retrospective Cohort Study Hesti Putri; Nur Diana; Paula Magna Pablo-Rodriguez; Nadia Khoirina
Sriwijaya Journal of Radiology and Imaging Research Vol. 3 No. 2 (2025): Sriwijaya Journal of Radiology and Imaging Research
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjrir.v3i2.291

Abstract

Introduction: Cerebral vascular malformations (CVMs) are the leading cause of spontaneous intracranial haemorrhage, yet their detection on CT/MR angiography is operator-dependent and existing machine-learning models derive almost exclusively from Caucasian or East Asian cohorts. We developed and internally validated a population-specific radiomics pipeline for CVM detection and rupture-risk stratification in a Southeast Asian population. Methods: In this retrospective diagnostic-and-prognostic cohort (STARD 2015; CLAIM) at a tertiary hospital in Palembang, Indonesia (2020–2025), 486 adults with diagnostic-quality CTA or TOF-MRA were analysed. After resampling and normalisation, 1,218 PyRadiomics features were reduced by LASSO and used to train Random Forest, SVM and XGBoost models (70/30 split). The reference standard was blinded consensus segmentation by two consultant neuroradiologists. Diagnostic accuracy (Wilson CI), AUC (DeLong), likelihood ratios, Cohen κ, McNemar test and a multivariable rupture model were computed. Results: CVM prevalence was 42.8% (208/486). XGBoost was the best detector (AUC 0.963 (95% CI 0.950–0.977); sensitivity 88.5 (95% CI 83.4–92.1)%; specificity 92.4 (95% CI 88.7–95.0)%; LR+ 11.71 (95% CI 7.74–17.72)), outperforming a single radiologist (ΔAUC 0.128, p<0.001; McNemar χ2=9.72, p=0.002 (discordant pairs b=73, c=39)). Inter-reader agreement was almost perfect (κ 0.845 (95% CI 0.797–0.893)). A radiomics-clinical model stratified rupture (AUC 0.846 (95% CI 0.792–0.901)), with lesion size (OR 4.26 (95% CI 2.60–6.98)) and hypertension (OR 2.46 (95% CI 1.18–5.14)) dominant and good calibration (χ2=14.87, df=8, p=0.062). Conclusion: A population-specific radiomics machine-learning pipeline achieved high diagnostic accuracy for CVM detection and clinically useful rupture-risk stratification, supporting operator-independent neurovascular triage in Southeast Asian settings. External validation is warranted.
Development and Validation of an Explainable Machine-Learning Model to Predict Cumulative Live Birth in PCOS-Associated Infertility Anies Fatmawati; Hesti Putri; Theresia Putri Sinaga
Sriwijaya Journal of Obstetrics and Gynecology Vol. 3 No. 2 (2025): Sriwijaya Journal of Obstetrics & Gynecology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjog.v3i2.282

Abstract

Introduction: Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility, yet predicting the cumulative live birth rate (CLBR) after in vitro fertilization (IVF) is difficult because of marked clinical heterogeneity. Conventional linear models discriminate modestly and most machine-learning (ML) tools remain uninterpretable. We aimed to develop and internally validate an explainable ML model for CLBR in women with PCOS. Methods: In a multicenter retrospective cohort at two tertiary reproductive-medicine centers in Indonesia (January 2018–December 2023), 1,245 women with PCOS (Rotterdam criteria) undergoing a first IVF/intracytoplasmic sperm injection cycle were analysed. Five algorithms (logistic regression, support vector machine, random forest, gradient boosting and eXtreme Gradient Boosting [XGBoost]) were trained (70%) and internally validated (30%) with 5-fold cross-validation. Discrimination used the area under the ROC curve (AUC) with DeLong confidence intervals (CI); SHapley Additive exPlanations (SHAP) quantified feature importance. Multivariable logistic regression, calibration and number-needed-to-treat (NNT) were also derived. Results: Overall CLBR was 54.6% (95% CI 51.8–57.4%). XGBoost performed best (AUC 0.82, 95% CI 0.79–0.85; accuracy 76.4%; sensitivity 78.2%; specificity 74.1%) and significantly exceeded logistic regression (AUC 0.72; ΔAUC 0.10, p<0.001). SHAP ranked top-quality embryo number, maternal age, anti-Müllerian hormone and oocyte yield as dominant predictors. Each additional top-quality embryo raised CLBR odds (adjusted OR 1.85, 95% CI 1.66–2.06, p<0.001); ≥3 versus <3 embryos yielded an NNT of 3.3. Conclusion: An explainable XGBoost model accurately predicts CLBR in PCOS-associated infertility and can support individualised counselling and cycle-management decisions. Prospective external validation is warranted before clinical deployment.
Machine learning identification of psychosocial and sociodemographic determinants of repeat adolescent pregnancy: a retrospective cohort study in an Indonesian metropolitan setting Rachmat Hidayat; Hesti Putri; Mahmood Abbas
Sriwijaya Journal of Obstetrics and Gynecology Vol. 4 No. 1 (2026): Sriwijaya Journal of Obstetrics & Gynecology
Publisher : Phlox Institute: Indonesian Medical Research Organization

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59345/sjog.v4i1.295

Abstract

Background: Repeat adolescent pregnancy compounds obstetric and socioeconomic risk, yet conventional models seldom capture the non-linear interplay of psychosocial and sociodemographic determinants, particularly in low- and middle-income registries where such variables are rarely recorded. Objective: To develop and compare interpretable machine-learning models to identify determinants of repeat adolescent pregnancy in an Indonesian metropolitan cohort. Methods: In this retrospective cohort study, electronic medical records (2018–2023) from a tertiary referral hospital and affiliated primary-care network in Palembang, Indonesia, were analysed for 1,245 adolescents (aged 10–19 years) with at least one prior pregnancy. The outcome was a second pregnancy before age 20. Five models (logistic regression, random forest, support vector machine, multilayer perceptron, and XGBoost) were trained with SMOTE and 5-fold cross-validation; discrimination was assessed by AUC-ROC and interpretability by SHAP. A multivariable logistic model provided adjusted odds ratios (aOR). Results: Repeat pregnancy occurred in 312/1,245 adolescents (25.06%; 95% CI 22.7–27.5%). XGBoost achieved the highest discrimination (AUC 0.89, 95% CI 0.86–0.92; F1 0.84). Independent determinants were non-use of postpartum LARC (aOR 8.86, 95% CI 6.00–13.07; p<0.001), low family support (aOR 3.82; p<0.001), education at most junior high (aOR 3.76; p<0.001), elevated EPDS (aOR 2.92, 95% CI 1.95–4.37; p<0.001), and age under 16 at first pregnancy (aOR 2.85; p<0.001). Postpartum LARC was strongly preventive (NNT approximately 3). Conclusion: Interpretable gradient boosting accurately stratified repeat adolescent pregnancy risk, and psychosocial determinants carried predictive weight comparable to contraceptive non-use. These findings support risk-stratified, bio-psycho-social postpartum care and targeted LARC counselling for adolescent mothers in Indonesia.
Integrating Kearifan Lokal (Local Wisdom) with Climate Adaptation Strategies: A Participatory Action Research on Enhancing Community Resilience and Achieving SDG 13 in Indonesia's Coastal Communities Jasmila Tanjung; Caelin Damayanti; Neva Dian Permana; Andi Fatihah Syahrir; Hesti Putri; Aman Suparman; Susi Diana
Indonesian Community Empowerment Journal Vol. 5 No. 2 (2025): Indonesian Community Empowerment Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/icejournal.v5i2.45

Abstract

Coastal communities in Indonesia face existential threats from climate change. Conventional top-down adaptation strategies often fail due to a disconnect from local socio-ecological realities, overlooking a critical resource: traditional ecological knowledge, or kearifan lokal. This study investigates a knowledge co-production model that synergizes kearifan lokal with modern climate science to build community resilience. We employed a 24-month, mixed-methods Participatory Action Research (PAR) design in three highly exposed coastal villages in North Java, Indonesia. Ethical protocols, including Free, Prior, and Informed Consent (FPIC), were foundational. Qualitative data were gathered from in-depth interviews (n=30), focus groups (n=12), and ethnographic observation. Quantitative data came from a pre-test/post-test household survey (n=450) measuring a validated, multi-dimensional Community Resilience Index (CRI). Interventions were co-designed, blending traditional practices like the pranata mangsa (ethno-astronomical calendar) and the wana tirta (mangrove philosophy) with scientific recommendations. A linear mixed-effects model was used to analyze changes in CRI scores. The co-designed strategies led to a statistically significant increase in the mean CRI from a baseline of 2.8 (SD=0.65) to 4.2 (SD=0.48) post-intervention (p<0.001). Significant improvements were observed across all resilience dimensions, most notably in Economic Capital (+59.1%) and Adaptive Capacity & Governance (+51.7%). The revitalization of practices such as the restoration of 50 hectares of mangroves, guided by both wana tirta principles and scientific species selection, enhanced coastal protection and local livelihoods. In conclusion, the co-production of knowledge, facilitated through a PAR framework, is a potent mechanism for building effective, culturally embedded, and sustainable climate resilience. This model empowers communities as active agents in their adaptation journey and offers a scalable, evidence-based pathway for achieving SDG 13 in Indonesia and other climate-vulnerable nations.
Co-Authors Aman Suparman Andi Fatihah Syahrir Anies Fatmawati Caelin Damayanti Jasmila Tanjung Jasmine Alieva Jovanka Andina Kevin Setiawan Khalil Jibran Mahmood Abbas Matilda Munoz Maya Enderson Nadia Khoirina Neva Dian Permana Nur Diana Paula Magna Pablo-Rodriguez RACHMAT HIDAYAT Sarah Armalia Sudarto Sudarto Susi Diana Theresia Putri Sinaga