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

Performance Analysis of OTR Machine In The Black Tea Leaf Rolling Process At PTPN IV Bah Butong Unit

2026-09-17T14:37:24+07:00

This study aims to analyze the performance of the Open Top Roller (OTR) machine in the black tea leaf rolling process at PTPN IV Bah Butong Unit. The OTR machine is essential in black tea processing, functioning to grind and roll tea leaves after the withering process to shape and release cell sap. The recorded variables were material input, scheduled batch duration, downtime, operating time, and good output. The batches processed 3,020 kg of withered leaves; recorded duration ranged from 40 to 50 min per batch, with a mean of 44.4 min, while total downtime was 5 min and good output was 3,010 kg. Availability, Performance, and Quality were 98.87%, 91.12%, and 99.67%, respectively, producing an OEE of 89.79%. Performance was the lowest component because most batches exceeded the selected ideal cycle time of 40 min, although all remained within the plant’s operational range of 40–50 min. Average loading was 302 kg per batch, equivalent to 80.53% of the 375 kg nominal maximum. These results identify cycle-time consistency, rather than downtime or recorded mass yield, as the immediate improvement priority. The result must nevertheless be interpreted as a short observational snapshot: the Quality factor represents mass yield rather than sensory or biochemical tea quality, and the widely cited 85% OEE benchmark is a comparative convention rather than a universal acceptance limit. The findings describe only this ten-batch observation window.

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

2026-08-27T13:23:30+07:00

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.

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

2026-07-22T17:54:40+07:00

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

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

2026-09-07T23:20:57+07:00

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.

Website-Based Medicinal Plant Information System Design for Universitas Pahlawan Botanical Garden Using the Prototype Method

2026-08-13T13:31:35+07:00

The Medicinal Plant Collection Garden of Universitas Pahlawan (UP) Botanical Garden manages approximately 60 species of medicinal plants as a means of conservation and education, yet its publication remains limited to physical banners and social media. The QR codes installed on-site also still direct visitors to the general Wikipedia platform, resulting in information that is less specific and whose scientific validity is difficult to verify. This study aims to design and build a Medicinal Plant Information System website as an independent and accurate information medium. The system was developed using the Prototype method to facilitate continuous interaction and feedback with users. The application was built using the Laravel 10 framework and a MySQL database, with three user roles: Visitor, Admin, and Person in Charge. Key features include a medicinal plant catalog, an activity photo gallery, news articles, public reviews, and a PDF report export module. Testing was conducted through Black Box Testing and User Acceptance Testing (UAT). The test results show that all feature functionalities ran successfully with a high level of user acceptance, indicating that this medicinal plant information system is feasible for implementation to optimize digital botanical literacy at the UP Botanical Garden.