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INDONESIA
Eduvest - Journal of Universal Studies
Published by Green Publisher Indonesia
ISSN : 27753735     EISSN : 27753727     DOI : 10.36418
Core Subject : Health, Science, Social, Engineering,
Aerospace Engineering Computer Science & IT Health Professions Neuroscience Social Sciences
Eduvest - Journal of Universal Studies is a double blind peer-reviewed academic journal and open access to multidiciplinary fields. The journal is published monthly by Green Publisher Indonesia. Eduvest - Journal of Universal Studies provides a means for sustained discussion of relevant issues that fall within the focus and scopes of the journal which can be examined empirically. This journal publishes research articles multidisciplinary sciences, which includes: Humanities and social sciences, contemporary political science, Educational sciences, religious sciences and philosophy, economics, Engineering sciences, Health sciences, medical sciences, design arts sciences and media. Published articles are from critical and comprehensive research, studies or scientific studies on important and current issues or reviews of scientific books.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 2,734 Documents
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Digitalization System to Improve Efficiency & Quality Through the Selective QC 7 Tools Method in the Component Automotive Industry Simamora, Yantono; Jaqin, Choesnul; Humiras, Humiras
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.52976

Abstract

The automotive component industry faces significant challenges in improving operational efficiency and production quality, particularly due to reliance on manual processes. A key issue identified is the company's Overall Equipment Effectiveness (OEE) performance, which has not met the established target, with an average achievement of only 72%, indicating considerable process inefficiencies and productivity losses. External pressures such as global market dynamics, economic uncertainty, and increasing Regional Minimum Wage (UMP) rates have further exacerbated the need for digital transformation to enhance operational efficiency and organizational competitiveness. This study aims to design a digitalization system to address operational inefficiencies and low production quality in the automotive component industry, focusing on improving OEE performance, reducing operational costs, and supporting data-driven decision-making. This research proposes an integrated digitalization system developed through the combination of the selective QC 7 Tools and Design Thinking approaches, with the selective QC 7 Tools applied to systematically identify and analyze the root causes of operational inefficiencies. The implementation resulted in significant improvements, including the complete elimination of 195 minutes per shift of non-value-added activities, the reduction of reporting lead time from 2–3 days to near real-time (0.5 days for analysis), and a 10% increase in OEE performance. The system also enabled the reallocation of 13 administrative personnel to more value-added roles, reduced operational costs, and minimized paper waste and CO₂ emissions. This study confirms that the selective QC 7 Tools prove effective in supporting digital transformation, and that data-driven digitalization can significantly improve competitiveness and business sustainability.
Maturation of Radiocephalic Arteriovenous Fistula with and without Hydro dilatation: A Study on the Diameter and Flow Volume of the Draining Vein Dion Pandin Purba Sigoemondrong, Eko; Darwis, Patrianef; Kusumadewi , Dian
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53007

Abstract

Radiocephalic arteriovenous fistula (AVF-RC) is the primary vascular access in patients with end-stage chronic kidney disease undergoing hemodialysis. One of the main challenges of using AVF-RC is the failure of early maturation, characterized by inadequate diameter and flow volume of the draining vein. Hydro dilatation is a simple intraoperative technique that aims to increase the distensibility and diameter of the veins, so that it is expected to accelerate the maturation process of the fistula. This study used a retrospective cohort design involving patients with chronic kidney disease who were undergoing hemodialysis and had AVF-RC created for the first time at Yarsi Hospital between October 2023 and September 2025. Subjects were divided into two groups, namely the group with and without hydro dilatation, with 60 patients in each group. A total of 120 patients were analyzed, with a median age of 54 years, and the majority were male. The diameter of the draining vein increased significantly from preoperative to 6 weeks postoperative in all patients (p < 0.001). The increase in draining vein diameter at weeks 1 and 6 postoperatively was significantly greater in the hydro dilatation group than in the non-hydro dilatation group (p < 0.001). Hydro dilatation has a significant effect on increasing the diameter and flow volume of the draining vein of AVF-RC, particularly in the early period of maturation. This technique has the potential to improve the success of AVF maturation and can be considered as part of an intraoperative strategy for optimizing vascular access for hemodialysis.
Analysis of Factors Affecting the Performance of Medical Laboratory Technologists (MITS)/Laboratory Analysts in Hospitals: A Scoping Review Riyanto, Agus; Sriyatmi, Ayun; Tri PurnamiI, Cahya
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53011

Abstract

The performance of Medical Laboratory Technologists (MLTs) and Laboratory Analysts is essential for maintaining the accuracy and reliability of diagnostic services in hospitals. Suboptimal laboratory performance can negatively affect clinical decision-making and patient outcomes. Many hospitals in Indonesia face challenges such as limited staffing, high workloads, and inadequate career development systems, which may hinder optimal employee performance. This study aims to systematically analyze key factors influencing the performance of Medical Laboratory Technologists and Laboratory Analysts in hospital settings. A systematic review methodology was applied by synthesizing findings from multiple related studies to derive comprehensive conclusions. Literature searches were conducted using Google Scholar and focused on factors associated with employee performance, including compensation, leadership, motivation, work culture, organizational commitment, work environment, salary structures, and career development, with article selection restricted to publications from 2014 to 2024. Articles were screened through title and abstract review, followed by full-text assessment based on eligibility criteria. The review results indicate that compensation, leadership, motivation, and work culture are major determinants of performance, while organizational commitment, work environment, salary structures, and career development also significantly contribute to job satisfaction and productivity. The findings highlight that strengthening both financial and non-financial incentives, providing effective leadership, and fostering supportive organizational cultures can enhance employee performance and improve the efficiency and quality of laboratory services.
Development of a Machine Learning Model for Estimating GRDP at Constant Prices (PDRB ADHK) for Regencies and Cities in West Java Angga Prasakti, Lukito; Budiarto, Isniar
Eduvest - Journal of Universal Studies Vol. 6 No. 4 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i4.53015

Abstract

Gross Regional Domestic Product (GRDP) at constant prices (ADHK) is a key indicator for measuring real economic growth at the regional level. However, estimating GRDP at the regency/city level in Indonesia still faces challenges related to limited real-time data availability, publication delays, and reliance on conventional statistical methods that are often unable to capture complex and nonlinear relationships. This research aims to develop and compare several machine learning models in estimating ADHK GRDP for 27 regencies/cities in West Java Province using data from 2010–2024. The study employs a quantitative explanatory approach with panel data consisting of 405 observations obtained from the West Java Open Data portal. Feature engineering was conducted by incorporating historical growth rates, temporal variables, and regional encoding to capture temporal dynamics and spatial heterogeneity. Four predictive models were developed, namely linear regression, Random Forest, Gradient Boosting, and Support Vector Regression (SVR), and were evaluated using RMSE, MAE, MAPE, and R² metrics with cross-validation. The results indicate that ensemble-based models outperform traditional methods, with Gradient Boosting demonstrating the best performance by achieving the lowest error values and the highest explanatory power. Random Forest also shows strong predictive capability, while linear regression yields the lowest accuracy. These findings highlight the superiority of machine learning, particularly tree-based ensemble methods, in modeling complex regional economic data. The study contributes to the limited literature on regency/city-level GRDP estimation in Indonesia and suggests that machine learning can serve as a reliable tool for supporting data-driven policy formulation.

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