Journal of Engineering Science and Technology Management (JES-TM)

Psychological Dynamics of Anxiety in Pregnant Women: A Positive Psychology Perspective

Nadya Ali Suryani — Sun, 13 Sep 2026 00:00:00 +0700

Prenatal anxiety presents significant physical and psychological risks to both mothers and fetal development, yet conventional interventions often focus solely on symptom reduction rather than psychological empowerment. This study aims to explain the psychological dynamics of pregnant women experiencing anxiety from a positive psychology perspective. A Systematic Literature Review (SLR) approach was employed using the PRISMA framework, analyzing 20 selected peer-reviewed journals published between 2020 and 2025. The findings indicate that positive psychology interventions, specifically through the PERMA framework and the concept of “Flow,” are effective in mitigating prenatal anxiety, particularly during the third trimester. Positive emotions broaden cognitive awareness and facilitate a state of flow, serving as a cognitive coping mechanism to divert negative thoughts regarding childbirth. Deep engagement in activities such as prenatal yoga and self-hypnosis is proven to enhance maternal self-efficacy and a sense of accomplishment. Furthermore, supportive social relationships and the search for meaning assist mothers in redefining physical challenges and body image changes into empowering experiences. The integration of spiritual interventions and affirmation techniques also provides a significant relaxation effect, improving overall emotional preparedness and maternal-fetal attachment. In conclusion, these positive psychology strategies foster stable and holistic psychological well-being, empowering pregnant women throughout the gestational period

The Prediction of Hospital Readmission in Diabetic Patients Using Random Forest, XGBoost, and Support Vector Machine with an Explainable AI Approach

Zulfaqar Zul, Ridho Amanda Putra, Hadnan Hardiansyah Hardiansyah — Sat, 19 Sep 2026 00:00:00 +0700

Hospital readmission among patients with diabetes is an indicator of healthcare quality, treatment effectiveness, and hospitalization burden. Early prediction of readmission risk may support targeted interventions and reduce repeated hospital stays. This study developed prediction models using Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM), with SHapley Additive exPlanations (SHAP) applied to interpret model predictions. The Diabetes 130-US Hospitals dataset comprised 101,766 medical records containing demographic characteristics, hospital visit history, laboratory results, diagnoses, and medication use. The outcome was defined as unplanned readmission within 30 days of discharge by recoding the original “<30,” “>30,” and “NO” categories. Data were divided into training and test sets at an 80:20 ratio using stratified sampling, preserving the class distribution of 11.16% readmitted and 88.84% not readmitted without resampling. Random Forest and XGBoost were trained on the full training set, whereas SVM used a stratified subsample of 10,000 training records because of computational constraints. All models were evaluated using the same test set. XGBoost achieved the best performance, with a balanced accuracy of 0.632 and ROC-AUC of 0.684. McNemar’s test indicated that its performance differed significantly from SVM but not from Random Forest. SHAP identified payer type, diagnosis categories, age group, prior inpatient history, and Clinical Risk Score as influential predictors. These findings demonstrate the feasibility of combining machine learning and explainable artificial intelligence for clinically interpretable readmission prediction. However, reliance on a single historical public dataset and the absence of external validation limit the model’s readiness for clinical deployment.

Analysis Of The Causes Of Suboptimal Steering Gear Performance Based On Maintenance Factors And The Quality Of Spare Parts On MV. Mulianim

Sat, 19 Sep 2026 00:00:00 +0700

The operational reliability of a ship's steering gear system is vital for navigation safety and vessel maneuverability, as regulated by International Maritime Standards (SOLAS). However, performance degradation often occurs due to inadequate maintenance practices and spare part constraints. This study aims to analyze the root causes of suboptimal steering gear performance on MV. Mulianim using a qualitative case study approach. Data were gathered through direct field observations, semi-structured interviews with chief and engine officers, and documentation audits (Engine Log Book and PMS records) over a 12-month period. Data validity was verified through source triangulation and analyzed using Miles and Huberman’s interactive model combined with Cause-and-Effect Analysis. The results revealed that steering gear hydraulic pressure dropped below the operational baseline (85–90 bar vs. standard 100 bar) due to high fluid turbidity, degraded viscosity, and internal bypasses. This condition was directly triggered by a 26.7% delay in Planned Maintenance System (PMS) tasks and severe spare part stockouts (25% critical spares unavailable). To prevent navigational hazards, strict adherence to PMS schedules and optimization of the spare part procurement pipeline are strongly recommended.