Leading & Enlightening Journal UMY
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Key Drivers of Sustainable Development in the Clay Roof Tile Industry of Sleman Regency
No full textThe clay roof tile industry, a key subsector of small and medium enterprises (SMEs) in Sleman Regency, Indonesia, plays an important role in local economic development and employment. Despite its strategic significance, the industry has experienced declining productivity and a reduction in business actors due to limited access to capital and technology, weak market competitiveness, and environmental degradation resulting from raw material extraction. This study applies a systemic analytical approach using the MICMAC (Matrix of Cross-Impact Multiplication Applied to Classification) method to identify and examine direct and indirect interrelationships among 25 strategic variables across economic, technological, social, environmental, and institutional dimensions. The results reveal that environmental quality, capital constraints, and digital technology function as primary upstream structural driving variables shaping industrial sustainability, while institutional cohesion—particularly cooperatives and inter-actor relationships—acts as long-term systemic enablers. These drivers influence production efficiency, competitiveness, and environmental outcomes through complex interdependent pathways. The findings provide policy-relevant evidence for designing integrated development strategies that strengthen competitiveness, enhance resilience, and support sustainable industrial transformation. This study contributes to the literature on industrial cluster sustainability by demonstrating the value of a systems-based perspective for understanding structurally mediated sustainability dynamics in traditional manufacturing sectors
Internet Access, Electricity Access, and Education Implications for MSMEs Productivity in Indonesia: Evidence from Dynamic Panel GMM
No full textThis paper examines the relationship between access to digital infrastructure, basic utilities, and human capital and the productivity of Micro, Small, and Medium Enterprises (MSMEs) across Indonesian provinces using panel data from 2020 to 2024. A dynamic panel data approach employing the Arellano–Bond Generalized Method of Moments estimator is used to control for regional heterogeneity and address endogeneity issues. The results indicate that access to electricity is positively correlated with MSME productivity in the short run, highlighting its role as a basic precondition for the smooth operation of daily business activities. In contrast, access to the internet does not significantly affect MSME productivity in the short run, suggesting that digital readiness requires sufficient time for adaptation to translate into productivity gains. Moreover, average years of schooling are negatively correlated with short-run MSME productivity, potentially reflecting a transitional effect as more educated individuals are more likely to move toward formal employment. Diagnostic tests confirm the robustness of the results, with no evidence of weak instruments or second-order serial correlation. Overall, the findings suggest that while improvements in electricity access are important for short-run MSME productivity, long-run regional productivity differences are more strongly influenced by digital readiness and the effective deployment of human capital within the MSME sector. Accordingly, policy interventions should extend beyond the provision of basic utilities and focus on enhancing digital readiness and the effective utilization of human capital
Purandare Procedure for Management of Grade IV Uterine Prolapse in Women of Reproductive Age: A Case Report
No full textUterine prolapse, a common pelvic organ prolapse (POP), results from weakened pelvic floor support, causing the uterus to descend into the vagina. It is rare in reproductive-aged women with low parity. The Purandare technique offers a uterus-preserving option, using rectus abdominis fascia for abdominal suspension. The aim of this case report is to present the management of pelvic organ prolapse in a reproductive-age woman, with an emphasis on uterine preservation. This case report describes a 37-year-old, P1A0, with grade IV uterine prolapse, cystocele, and rectocele, experiencing a vaginal mass for nine years and defecation difficulties, but no urinary symptoms. After counseling and consent, the Purandare procedure was performed under spinal anesthesia. Postoperative follow-up at two weeks evaluated anatomical correction, recovery, and patient satisfaction, monitoring complications and functional outcomes. Results showed significant improvement in pelvic organ function, urinary and digestive health, sexual function, and overall quality of life. These improvements supported her roles as a working woman, wife, and mother, enhancing her dignity and social participation. In conclusion, the Purandare technique is a viable, effective option for reproductive-age women with POP who wish to preserve fertility and their uterus, providing favorable anatomical and functional outcomes
Developing an Integrated Information System for Performance Management in Indonesian Higher Education
No full textIndonesian higher education institutions continue to face significant challenges in implementing the Government Agency Performance Accountability System (SAKIP), primarily due to fragmented information systems and manual reporting practices. At Gorontalo State University (UNG), these inefficiencies undermine accountability, delay decision-making, and compromise data accuracy. This qualitative descriptive case study explores how digital integration can address these institutional challenges. The findings revealed that a lack of system interoperability across planning, financial, and performance units leads to data redundancy, inconsistent reporting, and high administrative workloads. A simulation of the e-SAKIP workflow demonstrated a 65% reduction in reporting time and a 40% decrease in data duplication, underscoring its operational impact. In response, this study introduces a prototype of a centralized, cloud-based platform, e-SAKIP, that integrates planning, budgeting, monitoring, and reporting functions into a single digital ecosystem. The novelty of this study lies in developing the first API-based integrated governance model for Indonesian public universities, addressing the critical gap between siloed legacy systems and national SAKIP compliance requirements. Theoretically, the research contributes to digital governance literature by operationalizing Good University Governance (GUG) and New Public Management (NPM) principles through technological integration. Practically, the proposed e-SAKIP model offers a scalable solution for other universities facing similar challenges and provides actionable policy insights to strengthen institutional accountability, data reliability, and digital readiness across Indonesia’s higher education sector
Language Enhancement Activities in the EFL Interpreting Classroom: A Cognitive Approach Proposal to Mitigate Omissions and Grammatical Errors
No full textBackground: Language enhancement activities (LEAs) are among targeted pedagogical intervention which can be proposed in developing critical skills of simultaneous interpreting among beginner trainee to address any cognitive-linguistic constrains causing common interpreting errors. As a cognitively demanding task, simultaneous interpreting can be demanding for Indonesian undergraduate students learning English as a Foreign Language who must manage both interpreting subskils and B language (English) proficiency simultaneously.
Objective: This research investigates the predominant error profiles of Indonesian interpreter trainees, maps these errors to cognitive effort imbalances using Gile's (2009) Effort Model and Gravitational Model, and proposes a Reflective LEA Framework to mitigate comprehension omissions and grammatical errors in bidirectional interpreting tasks.
Methods: A qualitative case study within an action-research framework was conducted over 14 weeks. Twelve stratified undergraduate students were selected from a class of 24 to ensure diverse performance representation. A total of 200 errors from transcribed SI performances of two authentic speeches were analyzed using an integrated coding scheme combining Barik's (1971) omission typology and Gile's (2011) Errors, Omissions, and Infelicities framework. Student questionnaires provided qualitative data on perceived cognitive effort and LEA effectiveness.
Findings: Comprehension omissions were the most frequent error type, predominantly in B→A (English-to-Indonesian) interpreting, indicating Listening Effort (L) overload. Grammatical errors were concentrated in A→B (Indonesian-to-English) interpreting, reflecting Production Effort (P) deficits arising from L1 interference such as, copula deletion, article omission, tense inconsistency. Authentic, contextualized LEAs received significantly higher student effectiveness ratings than decontextualized drills.
Conclusion: The study proposed a reflective LEA framework grounded in the Cognitive Effort Model and Gile's Gravitational Model, integrating directionality-aware activities within a cyclical diagnostic-intervention-evaluation structure. The framework offers a practical model for integrating language enhancement into undergraduate EFL interpreter training curricula
Impact of Steady-State Error Minimization on the Performance of Numerical Optimization Techniques in Linear Automatic Control Systems
Full text linkThis paper investigates the impact of steady-state error minimization on the performance of numerical optimization techniques in linear automatic control systems, introducing a novel framework that integrates advanced genetic algorithms and machine learning to enhance controller tuning It highlights the significance of selecting appropriate test signals to generate quality system responses, which directly affects stability and reliability. Various optimization techniques are discussed, including classical methods and modern algorithms such as genetic algorithms and machine learning. Special attention is given to astatic control, which minimizes static errors and enhances controller reliability. Experimental results reveal that optimizing for one signal type can significantly diminish performance for another type. The paper introduces trade-offs that facilitate simultaneous consideration of performance responses to various stimuli. The conclusions underscore the importance of carefully selecting test signals and provide recommendations for automatic control practitioners, ultimately leading to improved reliability and efficiency in systems under dynamic conditions
Implementation of Adaptive Hysteresis-Band Current Control for Bidirectional H-Bridge DC-DC Converter
Full text linkThe substantial adoption of electric vehicles (EVs) has the potential to disrupt the power grid due to uncontrolled charging. A promising solution is Vehicle-to-Grid (V2G) technology, which allows EVs to return surplus energy to the grid to maintain reliability. However, a key challenge is developing a bidirectional converter with a rapid response that can also maintain a constant switching frequency to avoid harmonic disturbances. This study introduces an innovative adaptive hysteresis bandwidth current control method for H-bridge converters. This method provides a rapid and precise response to dynamic current and voltage fluctuations while maintaining a constant switching frequency for the MOSFET. The adaptive bandwidth values are mathematically derived from converter principles and implemented via a lookup table in a microcontroller. The simulation results showed a response time of less than 1 millisecond without overshoot. Experimental validation demonstrated the system’s efficacy in maintaining a constant switching frequency under dynamic changes. In charging mode, the average frequency was 14.88 kHz (with a range of 0.79 kHz), and the efficiency was 85.74%, while the fixed hysteresis band has a frequency variation of 14.26 kHz to 20.66 kHz (with a range of 6.37 kHz). In discharge mode, the average frequency was 16.62 kHz (with a range of 0.33 kHz), the efficiency was 77.40%, and the fixed bandwidth is approximately 2.3 kHz on frequency from 15.82 kHz to 18.12 kHz. With adaptive control, the frequency change range is successfully constant with a small range
H-infinity Robust Controller for Precise Temperature Regulation in an Agricultural Growth Chamber Prototype
Full text linkTemperature regulation is crucial for crop yield optimization in controlled environment agriculture, yet achieving such accuracy is challenging due to system nonlinearities and external disturbances. Since H_(∞ )control is an established theory, its experimental validation on low-cost hardware for agricultural systems remains limited. This paper presents robust control for a nonlinear system, targeting an internal temperature of growth chamber agriculture. Moreover, the primary contribution is the demonstration of a systematic and practical methodology for designing, implementing, and validating an H_(∞ ) controller on an Arduino-based growth chamber prototype, bridging the gap between complex control theory and accessible implementation. A simplified linearized thermal model was derived from a lumped parameter approach using energy balance equations. A second-order weighting function was systematically designed using loop-shaping principles to guarantee robust performance against unmodeled dynamics and sensor noise. The resulting controller was synthesized in MATLAB and deployed on an Arduino Mega microcontroller for experimental testing. Simulations predicted high-precision tracking with a Root Mean Square Error (RMSE) of 0.037 °C and an Integral Absolute Error (IAE) of 0.70. Subsequent experimental validation under real-world conditions confirmed the controller's efficacy, achieving stable temperature regulation within ±2 °C of the set point. The experimental validation yielded an RMSE of 1.04 °C and an IAE of 0.924, highlighting a notable but analyzed performance gap between the idealized simulation and the physical implementation. The results of this work were also compared with MPC and PID controllers, showing the proposed approach demonstrated satisfactory performance and confirming the robustness and stability of the control strategy in practical conditions. This work concludes that the H_(∞ ) framework provides a computationally efficient pathway to achieving robust temperature control on accessible hardware, making advanced control techniques more feasible for distributed agricultural applications
Simulation of Hybrid Trajectory Planning using RRT and Cubic Bezier Curve for a Mecanum Wheeled Mobile Robot
No full textThis study presents the development and validation of a hybrid trajectory planning framework that combines Rapidly-exploring Random Tree (RRT) and Cubic Bezier Curve Curve (CBC) methods for mobile robot navigation in a simulated environment using CoppeliaSim. The main contribution of this research lies in the integration and evaluation of the hybrid RRT–CBC approach, demonstrating its effectiveness across various navigation complexities. The proposed system consists of two subsystems mapping and path generation implemented on a laptop with a Ryzen 4000 series CPU, GTX 1650 Ti GPU, and 16 GB of RAM. Mapping was performed using a simulated Hokuyo FS LiDAR sensor, achieving an average mapping error of only 0.67% across three different map configurations, validating the reliability of the wall-following algorithm. The hybrid trajectory planning was tested under three environment configurations, showing improvements in both travel efficiency and computational performance. Experimental results indicate that the hybrid approach reduced travel distance and travel time by up to 24.21% and 18.15% in complex environments, while computation time decreased by an average of 13.61%. Overall, the proposed framework enhances both path efficiency and computational performance, while the CoppeliaSim-based simulation environment provides a reliable and cost-effective platform for validating robotic navigation algorithms prior to real-world deployment. Future work will focus on integrating adaptive threshold control and predictive obstacle avoidance to further improve system robustness in dynamic environments
Adaptive Replay Strategies Stabilize Multi-Agent Reinforcement Learning for Differential-Drive Robot Coordination
No full textBackground: Coordinating differential-drive mobile robots for landmark coverage is challenging due to non-holonomic dynamics, clutter, and sparse rewards. Standard multi-agent RL pipelines often show unstable learning and inconsistent completion in this setting. Methodology: We adopt a centralized-training, decentralized-execution actor-critic without inter-agent commu nication. Our replay-centric design combines a tagged buffer that up-samples goal-reaching transitions and an offline replay initialization that seeds early learning with curated trajectories. Dynamic task assignment uses the Hungarian algorithm during training and evaluation, and we benchmark against uniform replay and established variants. Results: In a cluttered six-robot arena, the approach improves training stability relative to uniform replay. Convergence is faster and requires fewer updates to reach consistent success. Coverage efficiency increases as landmarks are reached earlier across runs. Collisions per episode decrease without adding communication or architectural changes. Multi seed evaluations show gains that persist with narrow confidence intervals. Train-evaluation gaps shrink on unseen maps, indicating improved generalization. Ablations attribute complementary ben efits to the tagged and offline components. Performance remains competitive with prioritized and hindsight replay baselines under matched budgets. Computational overhead is small because sam pling logic changes while network sizes do not. Conclusions: Focus ing on replay design substantially stabilizes multi-agent learning for differential-drive coordination. The pipeline integrates cleanly with standard CTDE implementations and supports practical deployment in coverage tasks