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Dedi Purwanto Indra Kusuma
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RESWARA: Jurnal Riset Ilmu Teknik
Published by Lembaga Penelitian dan Pendidikan (LPP) Kalibra
ISSN : -     EISSN : 30259444     DOI : https://doi.org/10.70716/reswara
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation
RESWARA: Jurnal Riset Ilmu Teknik is a leading scholarly platform that examines the strategic role of engineering disciplines in advancing the Sustainable Development Goals (SDGs). The journal is dedicated to publishing recent research, innovations, and engineering solutions that support sustainable development. The editorial board welcomes manuscripts based on theoretical frameworks and empirical research. The journal scope covers scientific knowledge and research-based information, including applied research and recent developments in engineering research and development. The primary focus includes Civil Engineering and Spatial Planning, Industrial Engineering, Electrical Engineering and Informatics, Earth Science Technology, and Naval Architecture. All manuscripts published in RESWARA undergo a double-blind peer review process. The final decision on manuscript acceptance rests with the Editorial Board, based on recommendations from independent peer reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 55 Documents
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Cybersecurity Risk Assessment in Industrial Control Systems Wijaya, Ahmad Rudy; Ramadhani, Zulfa Ikhtiar; Sharkey, Daniel Thomas
RESWARA: Jurnal Riset Ilmu Teknik Vol. 2 No. 3 (2024): RESWARA: Jurnal Riset Ilmu Teknik, July 2024
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v2i3.388

Abstract

Industrial Control Systems play a critical role in modern industrial infrastructures, including manufacturing, energy, transportation, and critical utilities. The increasing integration of operational technology with information technology has significantly expanded the attack surface of these systems, making cybersecurity risk assessment an essential component of industrial resilience. This study aims to analyze and synthesize existing cybersecurity risk assessment approaches for Industrial Control Systems by examining quantitative, qualitative, and hybrid methods reported in recent literature. The research adopts a structured literature-based analytical method, focusing on models such as Bayesian networks, game theory, fuzzy logic, optimization-based frameworks, and vulnerability scoring systems. The results indicate that dynamic and asset-based risk assessment models provide more accurate and context-aware risk estimations compared to static approaches. Furthermore, integrating cyber and physical impact analysis enhances the capability to prioritize critical assets and predict worst-case attack scenarios. The findings contribute to a comprehensive understanding of current risk assessment methodologies and highlight key challenges related to data availability, model scalability, and real-time applicability. This study concludes that future cybersecurity risk assessment frameworks for Industrial Control Systems should emphasize dynamic modeling, cyber-physical integration, and adaptive evaluation mechanisms to address evolving threats effectively.
Assessment of Solid Waste Management Performance in Urban Areas Utama, Ahmad Rikia Tri; Costa, Maria Isabel; Zhou, Chuanbin
RESWARA: Jurnal Riset Ilmu Teknik Vol. 2 No. 2 (2024): RESWARA: Jurnal Riset Ilmu Teknik, April 2024
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v2i2.389

Abstract

Rapid urbanization has intensified the challenges of municipal solid waste management (MSWM) in cities worldwide, particularly in developing countries. Inefficient collection systems, limited recycling, inadequate disposal practices, and weak governance continue to undermine environmental quality and public health. This article aims to assess solid waste management performance in urban areas through a comprehensive synthesis of empirical studies and performance assessment frameworks published between 2015 and 2024. The study employs a systematic qualitative assessment approach, reviewing peer‑reviewed journal articles, book chapters, and dissertations indexed in international databases. The analysis focuses on key performance dimensions, including waste generation, collection efficiency, treatment and recycling rates, disposal practices, institutional capacity, and community participation. The results indicate that most urban areas exhibit low to moderate performance levels, with significant disparities between cities and regions. Socio‑economic development, governance quality, availability of infrastructure, and public awareness emerge as the most influential factors affecting MSWM performance. Comparative evidence from Asia, Africa, Europe, and Latin America reveals that cities adopting integrated assessment indicators and participatory governance models tend to achieve better outcomes. This study concludes that improving urban solid waste management requires integrated performance assessment tools, stronger policy enforcement, and sustained community engagement. The findings provide valuable insights for policymakers and urban planners seeking to enhance sustainable waste management systems in rapidly urbanizing contexts.
Design of Rainwater Harvesting System for Sustainable Urban Development Pratama, Hendri; Rahman, Ataur; Sánchez, Edith Rosalba Salcedo
RESWARA: Jurnal Riset Ilmu Teknik Vol. 2 No. 4 (2024): RESWARA: Jurnal Riset Ilmu Teknik, October 2024
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v2i4.390

Abstract

Rapid urbanization has intensified pressure on conventional water supply systems while simultaneously increasing surface runoff and flood risks in urban areas. Rainwater Harvesting Systems (RHS) have emerged as a strategic solution to enhance urban water security and promote sustainable development. This study aims to analyze and synthesize design principles of urban rainwater harvesting systems based on recent empirical studies, modeling approaches, and real-world case applications. A systematic literature-based research method was employed, integrating comparative analysis of system components, design criteria, optimization techniques, and performance indicators reported in international peer-reviewed journals. The results demonstrate that well-designed RHS can reduce potable water demand by 25–90%, mitigate urban flooding, and enhance stormwater management efficiency when integrated with Sustainable Urban Drainage Systems (SUDS). Key findings emphasize the importance of site-specific rainfall patterns, storage capacity optimization, socio-economic considerations, and technological integration such as GIS, BIM, and stochastic optimization models. The study concludes that rainwater harvesting systems, when properly designed and implemented, represent a resilient and cost-effective approach to sustainable urban water management, particularly in water-stressed and rapidly urbanizing regions.
Slope Stability Analysis Using Limit Equilibrium and Numerical Modeling Bagaskoro, Muhamad Nur; Mulenga, François K.; Yu, Hai-Sui
RESWARA: Jurnal Riset Ilmu Teknik Vol. 2 No. 3 (2024): RESWARA: Jurnal Riset Ilmu Teknik, July 2024
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v2i3.391

Abstract

Slope stability analysis is a fundamental aspect of geotechnical engineering due to its direct implications for infrastructure safety, mining operations, and disaster mitigation. Traditional analytical approaches, particularly the Limit Equilibrium Method (LEM), have been widely applied owing to their simplicity and clear mechanical interpretation. However, the increasing complexity of slope geometries, heterogeneous material conditions, and hydro-mechanical interactions necessitates the integration of numerical modeling techniques such as the Finite Element Method (FEM). This study presents a comprehensive slope stability analysis by systematically integrating LEM and FEM to evaluate safety factors, failure mechanisms, and critical slip surfaces. The research employs Bishop and Janbu methods within the LEM framework and the Shear Strength Reduction technique in FEM-based numerical simulations. Results demonstrate strong consistency between the two methods, with safety factor deviations generally within 1–3%, confirming findings reported in previous studies. FEM provides enhanced insight into stress redistribution, plastic zone development, and progressive failure behavior, which cannot be fully captured by conventional LEM. The study concludes that a hybrid analytical–numerical approach significantly improves reliability in slope stability assessment, particularly for complex geological and loading conditions. This research contributes to methodological refinement and offers practical guidance for slope design in civil and mining engineering applications.
Effect of Wastewater Treatment Efficiency on River Ecosystem Sustainability Kartikasari, Rina; Rodriguez, Maria L.; Nakamura, Kenji
RESWARA: Jurnal Riset Ilmu Teknik Vol. 2 No. 2 (2024): RESWARA: Jurnal Riset Ilmu Teknik, April 2024
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v2i2.392

Abstract

Wastewater treatment plants (WWTPs) play a crucial role in controlling pollutant discharges into river systems, thereby influencing river ecosystem sustainability. However, increasing evidence suggests that conventional and even advanced wastewater treatment processes may not fully mitigate ecological impacts on receiving waters. This study aims to synthesize and critically analyze global scientific evidence on the effects of wastewater treatment efficiency on river ecosystem sustainability, focusing on physicochemical, biological, and metabolic responses. A systematic literature-based research design was employed, analyzing 30 peer-reviewed journal articles, conference proceedings, and preprints published between 2016 and 2024. The reviewed studies reveal that enhanced wastewater treatment efficiency significantly improves water quality, reduces nutrient and micropollutant loads, stabilizes ecosystem metabolism, and supports biodiversity recovery. Nevertheless, residual contaminants, altered flow regimes, and nutrient imbalances persist, leading to changes in microbial communities, food web dynamics, and fish assemblages. The findings indicate that while improved wastewater treatment is essential for achieving river sustainability, current treatment targets remain insufficient in many regions. This study highlights the need for integrated wastewater management strategies that combine advanced treatment technologies, ecological flow considerations, and watershed-scale planning to ensure long-term river ecosystem resilience.
Artificial Intelligence-Based Demand Forecasting for Industrial Supply Chains Roesnadi, Renita Amara; Nugroho, Binar Ayustika; Jones, Jennifer
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 1 (2025): RESWARA: Jurnal Riset Ilmu Teknik, January 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i1.403

Abstract

Accurate demand forecasting is a critical determinant of efficiency and resilience in industrial supply chains. Increasing market volatility, shortened product life cycles, and complex global networks have exposed the limitations of traditional statistical forecasting approaches. Artificial Intelligence (AI), particularly machine learning and deep learning, has emerged as a transformative solution capable of processing large-scale, heterogeneous data and capturing nonlinear demand patterns. This study aims to systematically analyze and synthesize empirical and conceptual evidence on AI-based demand forecasting in industrial supply chains. Using a structured literature-based analytical method, this research reviews and integrates findings from peer-reviewed journal articles, conference proceedings, and authoritative preprints published between 2020 and 2025. The results demonstrate that AI-based forecasting models—such as neural networks, ensemble learning, hybrid ARIMA–LSTM architectures, and predictive analytics—consistently outperform traditional methods in terms of accuracy, adaptability, and responsiveness. Moreover, AI-driven forecasting contributes significantly to improved inventory optimization, cost reduction, and supply chain resilience. However, challenges related to data quality, implementation cost, system integration, and skill gaps remain substantial barriers. The study concludes that AI-based demand forecasting is not merely a technological enhancement but a strategic capability for industrial supply chains. Practical implications and directions for future research are discussed to support broader and more effective adoption of AI-driven forecasting systems.
Power Quality Improvement Using Active Power Filters in Industrial Systems Anindya, Mikaela Shopie; Zaro, Fouad; Popescu, Mihaela
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 2 (2025): RESWARA: Jurnal Riset Ilmu Teknik, April 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i2.404

Abstract

Power quality issues have become a critical concern in modern industrial systems due to the increasing use of nonlinear loads, power electronic converters, and renewable energy integration. These factors significantly contribute to harmonic distortion, reactive power imbalance, voltage fluctuations, and reduced power factor, which can degrade system performance and shorten equipment lifespan. This study aims to analyze the effectiveness of active power filters in improving power quality in industrial environments. The research adopts a qualitative analytical approach by synthesizing recent empirical and simulation-based studies on shunt, series, and hybrid active power filter configurations. The findings indicate that active power filters provide superior harmonic mitigation, dynamic reactive power compensation, and voltage stabilization compared to conventional passive solutions. Shunt active power filters are particularly effective in reducing current harmonics, while series active power filters address voltage-related disturbances such as sags and swells. The study concludes that active power filters represent a robust and flexible solution for enhancing industrial power quality, supporting compliance with international standards and improving overall system reliability.
Load Forecasting Analysis for Electrical Distribution Systems Using Time Series Methods Putra, Rizky Mahendra; Damayanti, Rosita Mei; Lopez, Juan Camilo
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 3 (2025): RESWARA: Jurnal Riset Ilmu Teknik, July 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i3.405

Abstract

Accurate load forecasting plays a critical role in ensuring the reliability and efficiency of electrical distribution systems. Increasing load variability, the integration of renewable energy, and changes in consumption behavior have intensified forecasting complexity. This study analyzes the effectiveness of time series methods for load forecasting in electrical distribution systems through a structured literature-based analytical approach. The study reviews and synthesizes empirical findings from peer-reviewed journal articles, conference proceedings, and patents published between 2002 and 2025. The methods analyzed include classical statistical models such as ARIMA, SARIMA, ARIMAX, and exponential smoothing, as well as hybrid and advanced approaches including LSTM, Prophet, fuzzy time series, wavelet-based models, and probabilistic forecasting frameworks. The results indicate that classical time series models remain effective for short-term forecasting with stable patterns, while hybrid and machine learning-based time series models provide superior performance under high volatility and complex load dynamics. Studies consistently report improvements in forecasting accuracy, measured using RMSE, MAE, and MAPE, when external variables and hierarchical structures are incorporated. The findings highlight the continued relevance of time series analysis as a foundational approach for load forecasting, while emphasizing the need for adaptive and hybrid models to address modern distribution system challenges. This study contributes a systematic synthesis that supports methodological selection for researchers and practitioners in electrical load forecasting.
Quality Control Improvement Using Statistical Process Control in Manufacturing Industry Soeryono, Joan Michael; Putri, Rina Kartikasari; Doğan, Onur
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 1 (2025): RESWARA: Jurnal Riset Ilmu Teknik, January 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i1.406

Abstract

Quality improvement remains a critical challenge in manufacturing industries due to increasing product complexity, strict customer requirements, and competitive market conditions. Statistical Process Control (SPC) has been widely recognized as an effective approach for monitoring process stability, reducing variability, and improving product quality. This study aims to analyze the application of SPC as a systematic method to enhance quality control performance in manufacturing operations. A quantitative case-study-based research design was adopted, focusing on production processes where quality deviations frequently occurred. Data were collected through direct observation, historical production records, and defect inspection reports. SPC tools, including control charts, process capability analysis, and Pareto analysis, were employed to identify process variations and root causes of defects. The results demonstrate that SPC implementation significantly improves process stability, reduces defect rates, and enhances process capability indices. Comparative analysis with previous studies confirms that SPC contributes to continuous improvement when supported by structured data analysis and corrective actions. This study concludes that SPC is a practical and reliable quality control approach that supports decision-making and operational excellence in manufacturing industries. The findings provide practical insights for quality engineers and managers seeking to strengthen quality assurance systems.
Ergonomic Risk Assessment to Improve Worker Safety in Assembly Line Operations Gunawan, Olivia Adeline; Colim, Ana; Duffy, Vincent G.
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 2 (2025): RESWARA: Jurnal Riset Ilmu Teknik, April 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i2.408

Abstract

Ergonomic risks in assembly line operations remain a major contributor to work-related musculoskeletal disorders and reduced productivity. Assembly work is characterized by repetitive motions, awkward postures, forceful exertions, and time pressure, which together increase physical strain on workers. This study aims to develop and apply an integrated ergonomic risk assessment framework to improve worker safety in assembly line operations. The research adopts a descriptive–analytical design by combining observational ergonomic assessment methods, digital ergonomics tools, and multi-criteria decision-making techniques. Data were collected from representative assembly workstations through direct observation, posture analysis, and task cycle evaluation. The results indicate that several workstations exhibit high ergonomic risk levels, particularly related to upper limb posture, repetitive actions, and static loading. By applying integrated assessment approaches such as REBA, RULA, OCRA-based indicators, and decision-support techniques, targeted improvement strategies were identified, including workstation redesign, task redistribution, and job rotation. The discussion demonstrates that the proposed approach aligns with and extends previous ergonomic studies by providing a systematic and scalable framework suitable for mixed-model assembly lines. The study concludes that integrated ergonomic risk assessment significantly enhances worker safety while supporting operational efficiency, offering practical implications for manufacturing practitioners and directions for future research.

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