We would like to inform you that Volume 14, Number 2, and earlier issues of Energy: Jurnal Ilmiah Ilmu-ilmu Teknik are available on the old system at the following link: https://ejournal.upm.ac.id/index.php/energy/. This is due to technical issues caused by a recent hacker attack, which has prompted us to create a new, more secure system for future publications. As a result, issues from Volume 15, Number 1 onwards will be published on the new system at: https://e-journal.upm.ac.id/index.php/energy/. We appreciate your understanding and cooperation during this transition, and we sincerely apologize for any inconvenience caused.
Supply Chain Risk Analysis and Mitigation Using the House of Risk Method: A Case Study of an LPG Manufacturing Company
Supply chain disruptions in LPG manufacturing can significantly affect procurement, production, and distribution, requiring a proactive risk management approach. This study aims to identify supply chain risks, prioritize their root causes, and develop preventive mitigation strategies by integrating the Supply Chain Operations Reference (SCOR) model and the House of Risk (HOR) method. A quantitative case study was conducted using interviews, focus group discussions, and expert judgment involving five managers from procurement, operations, terminal, marine, and sales functions. The SCOR model identified 22 Risk Events and 41 Risk Agents across six supply chain processes. HOR Phase 1 prioritized seven Risk Agents, accounting for 55.59% of the total Aggregate Risk Potential, with procurement- and logistics-related risks emerging as the most critical. HOR Phase 2 proposed 13 preventive actions, including supplier performance evaluation, unloading schedule optimization, preventive maintenance, cross-functional coordination, and real-time shipment monitoring. The integration of SCOR and HOR provides a systematic framework for prioritizing critical Risk Agents and developing proactive mitigation strategies, thereby improving supply chain resilience and operational reliability in LPG manufacturing.
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Understanding Cognitive Processes Behind "I Don't Know" Responses in Sustainability Literacy Assessment Using Eye-Tracking and Usability Evaluation
Assessing sustainability literacy is essential for ensuring that sustainability literacy tests accurately measure individuals' understanding of sustainability concepts. However, conventional evaluation methods primarily rely on test scores and response accuracy, providing limited insight into the cognitive processes underlying participants' decision-making. This study investigates the use of eye-tracking technology to analyze participants' visual attention while completing a Sustainability Literacy Test (SLT). Eye movement data, including scanpaths, were analyzed to identify user interactions with test items and determine which visual elements attracted the greatest attention, particularly when participants selected the "I Don't Know" response option. In addition, system usability was evaluated using the Hybrid System Usability Scale (H-SUS). The results indicate that participants required an average response time of more than 60 seconds for items associated with the "I Don't Know" option, suggesting increased visual attention and cognitive load in specific areas of the interface. The website achieved an average H-SUS score of 63.34, indicating an acceptable level of usability, corresponding to Grade C and an "OK" adjective rating. These findings demonstrate that eye-tracking data can reveal hidden patterns of cognitive effort and user uncertainty that cannot be captured through response accuracy alone. The proposed approach provides valuable insights for improving test design, enhancing question readability, and optimizing the user interface of web-based Sustainability Literacy Tests, thereby supporting more effective assessment of sustainability literacy.
The Impact of Energy Price Volatility on Industrial Performance: A Review of Energy Efficiency and Renewable-Energy Adoption
The increasing volatility of global energy markets has created significant challenges for industrial sectors by increasing production costs, reducing competitiveness, and threatening operational stability. This study investigates how energy price volatility influences industrial performance and examines the roles of energy efficiency and renewable-energy adoption as adaptation mechanisms. A systematic literature review (SLR) was conducted based on 298 peer-reviewed articles published between 2020 and 2026 from Scopus, Web of Science, ScienceDirect, and Google Scholar. The selected studies were analyzed using thematic synthesis to identify major research trends, causal mechanisms, and emerging research gaps. The findings reveal that energy price volatility represents a critical external shock affecting industrial performance through increased operational costs, supply-chain disruption, investment uncertainty, and reduced competitiveness. However, the literature also demonstrates that firms can reduce vulnerability through internal adaptation capabilities. Energy efficiency functions as a strategic capability by reducing energy intensity, improving operational flexibility, and mitigating cost pressures, while renewable-energy adoption provides a buffering mechanism by diversifying energy sources and reducing dependence on volatile fossil-fuel markets. The review integrates Resource Dependence Theory, Resource-Based View, Dynamic Capability Theory, and Natural Resource-Based View to explain how industrial firms transform energy uncertainty into resilience capabilities. The study highlights that future industrial competitiveness will depend not only on access to affordable energy but also on firms’ ability to develop adaptive energy strategies, digital energy-management capabilities, and sustainable energy systems. This review provides a conceptual foundation for understanding industrial resilience under increasing global energy uncertainty.
Classification of Electrical Distribution Materials Based on Weight and Volume for Warehouse and Transportation Capacity Planning
Electricity distribution logistics involves highly heterogeneous materials with different physical characteristics, creating challenges for warehouse capacity and transportation planning. Conventional material classification approaches mainly focus on economic value, demand frequency, or criticality, while physical logistics burdens generated by material weight and volume remain insufficiently represented. This study develops a weight–volume-based classification framework for electricity distribution materials to support physical logistics planning. A quantitative descriptive approach was applied using material master data and historical demand records from electricity distribution facilities in Central Java and the Special Region of Yogyakarta, Indonesia. Materials were classified into representative categories based on material characteristics, unit weight, and unit volume. Historical demand was then converted into total physical loads expressed in kilograms and cubic meters and aggregated by logistics location. The results identified 24 representative material categories with unit weights ranging from 0.50 to 3,300 kg and unit volumes from less than 0.01 to 4.40 m³. The highest physical demand was observed at UP3 Jogja, reaching 59.30 million kg and 120,221.83 m³. The findings demonstrate that material quantity alone is insufficient to represent logistics requirements in heterogeneous electricity distribution systems. The proposed weight–volume classification provides a practical basis for warehouse capacity evaluation, material handling planning, and transportation resource allocation.
Determinants of Behavior-Based Safety Performance in Coal-Fired Power Plants Using Structural Equation Modeling
Occupational safety is a critical issue in coal-fired power generation industries due to the high-risk nature of operational and maintenance activities. Preliminary observations at a power generation company identified several unsafe behaviors, including non-compliance with work permits, improper use of personal protective equipment (PPE), violations of standard operating procedures, unsafe workplace arrangements, and inappropriate behavior during hazardous activities. This study aimed to identify factors influencing Behavior-Based Safety (BBS) and determine the most significant predictors of employees’ safety behavior. A quantitative cross-sectional approach was applied, with data analyzed using Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The results indicated that employee attitude (p = 0.004) and the implementation of occupational health and safety (OHS) regulations (p = 0.003) had significant positive effects on safety behavior (p < 0.05). Meanwhile, safety knowledge, work motivation, PPE availability, OHS training, reward systems, and punishment mechanisms showed no significant influence on BBS (p > 0.05). These findings highlight that strengthening safety attitudes and organizational OHS implementation plays a more dominant role in improving employee safety behavior compared to knowledge-based and incentive-based interventions. The SEM model developed in this study can support safety management strategies and contribute to establishing a sustainable zero-accident culture in high-risk industries.
Inventory Discrepancy Risk Analysis and Mitigation Prioritization at Finished Goods Warehouse Using the House of Risk Method
Inventory discrepancy in finished goods warehouses reduces inventory accuracy and may disrupt warehouse operations and product distribution. This study was conducted at PT Alpha Plastic Manufacturing (a pseudonym), a plastic injection moulding company operating in the plastic manufacturing sector that manages a finished goods warehouse for injection-moulded components supplied for industrial applications. This study aims to analyze the causes of inventory discrepancy and determine appropriate mitigation strategies by integrating the Supply Chain Operations Reference (SCOR) model and the House of Risk (HOR) method. A descriptive quantitative approach was employed using observation, interviews, focus group discussions, company documents, and questionnaires involving three warehouse experts. The SCOR model was used to identify risk events and risk agents, while HOR Phase 1 prioritized risk agents using Aggregate Risk Potential (ARP), and HOR Phase 2 determined preventive actions based on the Effectiveness-to-Difficulty Ratio (ETD). The integration of SCOR and HOR provides a systematic approach to connect warehouse operational activities, priority risk sources, and feasible mitigation strategies. The results identified 10 risk events and 8 risk agents, with six priority risk agents contributing 86.36% of the total ARP value and five priority preventive actions contributing 66.97% of the total ETD value. Based on the investigated case, the findings indicate that strengthening warehouse procedures and operational controls should become the initial priority for reducing inventory discrepancy before implementing advanced inventory technologies.
Quality Improvement Analysis of Timing Chain Cover Components Using Six Sigma DMAIC in an Automotive Component Assembly Line
Product quality is a critical factor in maintaining process stability and competitiveness in the automotive component manufacturing industry. An automotive component manufacturing company in Karawang, Indonesia, experienced quality problems in the Timing Chain Cover assembly process, particularly porosity and dantsuki defects. This study aims to analyze the defect rate, identify dominant critical-to-quality (CTQ) defects, evaluate process capability, determine potential defect causes, and propose improvement and control actions using the Six Sigma DMAIC approach. The research used production and quality inspection data from January to June 2025. The analysis was conducted through the Define, Measure, Analyze, Improve, and Control stages using defect rate, Defect Per Unit, Defects Per Million Opportunities, sigma level, p-chart, Pareto analysis, fishbone diagram, and 5W+1H improvement planning. The results showed that 1,142 CTQ defects were found from 64,130 units produced, consisting of 611 porosity defects and 531 dantsuki defects. The average defect rate was 1.78%, exceeding the company’s internal target of less than 1.5%. The average DPMO value was 8,899, with a process capability level of 3.87 sigma. The p-chart result indicated that the process was statistically stable, but stable at a defect level that remained above the company target. The potential causes of defects were related to machine parameter stability, mould condition, SOP compliance, inspection discipline, material handling, measurement control, and workplace conditions. This study proposes prioritized improvement actions and a measurable control plan as recommendations for future implementation. The findings provide a practical DMAIC-based framework for identifying dominant defects, evaluating process capability, and developing structured quality improvement strategies in automotive component assembly processes.
Improving Energy Efficiency in Flavor Enhancer Production Using an Integrated Lean Manufacturing and Energy Value Stream Mapping Approach
The flavor enhancer industry faces significant challenges in improving energy efficiency because steam, electricity, and cooling utilities contribute approximately 16% of the Cost of Goods Manufactured (COGM). Although Lean Manufacturing has been implemented, most improvement initiatives have focused on production output and defect reduction rather than energy efficiency. This study aims to identify and reduce operational and energy waste in the Refinery unit using the Energy Value Stream Mapping (EVSM) approach. The proposed framework integrates EVSM for process and energy flow mapping, the Dotoli Matrix for critical waste prioritization, Root Cause Analysis using the 5 Whys method for identifying the underlying causes of waste, and Heat Exchanger Network (HEN) analysis for evaluating energy recovery opportunities, thereby providing a comprehensive approach to energy mapping, waste prioritization, root cause identification, and energy recovery evaluation. The results indicate that the dominant wastes are excessive cooling energy consumption and defect waste caused by delayed thin broth handling. Improvement proposals were developed through Heat Exchanger Network integration by utilizing glutamic mother liquor-1 as an internal cooling medium, generating potential savings of IDR 1,229,384,800 per year with a payback period of 2.4 years. In addition, the implementation of an automatic spray ball system reduced tank cleaning time from 49 minutes to 19 minutes and generated potential savings of IDR 274,007,580.52 per year. The study demonstrates that integrating Lean Manufacturing and EVSM effectively reduces energy consumption and operational waste in the Refinery unit.
Major Accident Hazard Analysis and Determination of Safety Critical Elements at Gresik Gas Distribution Station Using the HAZOP Method
The Gresik Gas Distribution Station (GDS) is a critical natural gas facility operating under high-pressure conditions, where process failures may lead to Major Accident Hazards (MAHs). Therefore, systematic hazard identification is essential to ensure process safety and operational reliability. This study aims to identify potential MAHs and determine the Safety Critical Elements (SCEs) required to prevent and manage these hazards. The study employed the Hazard and Operability Study (HAZOP) method to evaluate three key process nodes: the metering and regulating system, odorant injection system, and instrument air system. Process deviations were analyzed using guide words, followed by risk assessment to identify MAHs. Safety Critical Elements were subsequently identified based on their safety functions, including the prevention, detection, control, mitigation, and recovery of MAH events. The analysis identified 17 potential causes classified as Major Accident Hazards and 76 Safety Critical Elements, grouped into 26 SCE types. Based on the criticality assessment, 4 SCEs were classified as high criticality, 9 as medium criticality, and 13 as low criticality. The most critical SCEs were HC Pipework, Level Indicator, Fixed Point Flammable Gas Detectors, and Flame Detectors. These findings demonstrate that the HAZOP-based approach effectively identifies Major Accident Hazards and establishes priorities for Safety Critical Element management, providing a practical basis for improving process safety and operational reliability in natural gas distribution facilities.
The Effect of Acetone Vapor Smoothing Duration and Printing Orientation on The Mechanical Properties and Surface Characteristics of ABS Specimens from Fused Deposition Modeling 3D Printing
Fused Deposition Modeling (FDM) 3D printing technology is one of the additive manufacturing methods widely used due to its relatively low production costs, ease of processing, and compatibility with various thermoplastic materials such as Acrylonitrile Butadiene Styrene (ABS). However, FDM products generally exhibit high surface roughness due to layer formation during printing and display mechanical properties influenced by printing orientation. Acetone vapor smoothing (AVS) is commonly applied to improve the surface quality of ABS components, although excessive exposure may affect mechanical performance. This study aims to analyze the effect of acetone vapor smoothing duration and printing orientation on the mechanical properties and surface characteristics of ABS specimens produced using FDM. Specimens were printed with vertical and horizontal orientations and treated with acetone vapor smoothing for 0, 20, 40, and 60 minutes. Tensile testing was conducted according to ASTM D638 Type IV, while surface characteristics were evaluated based on layer line visibility and edge rounding. The results indicate that printing orientation has a significant influence on tensile performance, with horizontal specimens consistently exhibiting higher strength than vertical specimens due to better load distribution along the extrusion direction. A 20-minute acetone vapor smoothing treatment provided the most balanced condition by improving surface quality while maintaining mechanical properties. Longer exposure durations further reduced layer line visibility but caused greater edge rounding and a decline in tensile performance. These findings demonstrate that controlling both printing orientation and post-processing duration is essential to achieve a balance between surface quality and mechanical reliability of FDM-printed ABS components.
Comparative Finite Element Analysis of Wear Repair Methods for Vertical Kaplan Turbine Shafts Based on Structural and Dynamic Responses
Non-uniform journal wear on the vertical Kaplan turbine shaft increases shaft–bearing clearance, resulting in higher vibration and reduced operational reliability. This study evaluates the structural and dynamic effects of shaft wear and compares two restoration methods: Belzona 1111 coating and an SS410 stainless-steel shock sleeve. Finite Element Analysis (FEA) was performed in ANSYS Workbench using static structural and modal analyses for four shaft conditions: intact, worn, Belzona-restored, and shock sleeve-repaired. The numerical model was validated against the theoretical critical speed, yielding an error of 8.32%. The worn shaft exhibited the highest radial deformation (0.033123 mm), indicating a significant reduction in structural stiffness. Belzona restoration reduced deformation by 36.23%, while the SS410 shock sleeve achieved a 97.68% reduction, restoring shaft deformation close to the intact condition (0.00076983 mm). Although natural frequencies showed only minor variations among all cases, the shock sleeve produced the smallest deviation from the intact shaft and the most symmetrical mode shapes, indicating superior dynamic performance. The results demonstrate that journal wear primarily degrades shaft stiffness rather than natural frequencies. Among the evaluated restoration methods, machining followed by installation of an SS410 shock sleeve provides the closest structural and dynamic characteristics to the original shaft, making it the most effective solution for improving the reliability and operational stability of vertical Kaplan turbine shafts.
Techno-Economic Optimization of Diesel–PV Hybrid Systems for Isolated Island Grids: Case Study of Tomia Island, Indonesia
Tomia Island, an isolated power system in Wakatobi Regency, Indonesia, relies primarily on diesel power generation, resulting in high operating costs, fuel dependency, and greenhouse gas emissions. Although the island has abundant solar energy resources, determining an appropriate photovoltaic (PV) capacity that balances technical performance, economic feasibility, and land availability remains a key challenge. This study aims to identify the optimal hybrid PV–diesel configuration to support a reliable and sustainable electricity supply. The analysis combined solar resource assessment, land suitability evaluation, and energy system modeling using the Low Emissions Analysis Platform (LEAP). Several development scenarios were compared to identify the most suitable system configuration based on technical and economic performance. The results indicate that a 2.5 MWp PV system integrated with a 4 MWh Battery Energy Storage System (BESS) provides the best balance between renewable energy utilization, system reliability, and investment cost. Compared with larger PV capacities, this configuration delivers substantial reductions in diesel fuel consumption and carbon emissions while avoiding unnecessary increases in investment and excess energy production. In addition, the integration of BESS improves supply reliability by storing surplus solar energy and supplying electricity during periods of low solar generation. Economic evaluation confirms that the proposed hybrid system is financially feasible, offering a competitive cost of electricity and a relatively short investment payback period. These findings demonstrate that the proposed PV–BESS–diesel configuration provides a practical and cost-effective pathway for reducing diesel dependence and accelerating the energy transition in isolated island power systems
Optimizing Toluene Inventory Control Using Economic Order Quantity
Raw material inventory control plays an important role in maintaining production continuity and reducing inventory costs. CV. AK INDOPRIMA, a thinner manufacturing company, still determines the quantity and timing of Toluene raw material orders based on estimation. This condition causes inventory fluctuations, including overstock in several periods and shortage risk in other periods. This study aims to analyze the company’s current inventory control system and determine an optimal inventory policy using the Economic Order Quantity (EOQ) method, supported by Safety Stock, Reorder Point (ROP), and Total Inventory Cost (TIC) calculations. This study used a quantitative descriptive method. Data were collected through observation, interviews, and company documentation for the January to December 2025 period. The results showed that the annual demand for Toluene was 183,600 liters. The EOQ calculation produced an optimal order quantity of 5,777.70 liters per order, with an ordering frequency of 32 times per year or every 9 days. The Safety Stock was 2,799 liters, while the Reorder Point was 5,859 liters. The Total Inventory Cost based on the EOQ policy was IDR 63,554,796 per year. These results indicate that the integrated EOQ-based policy can provide a more structured inventory control system by determining order quantity, safety inventory, reorder timing, and annual inventory cost. From a managerial perspective, the proposed policy can help managers reduce subjective purchasing decisions, establish a measurable ordering schedule, prevent excessive stock accumulation, reduce shortage risk, and maintain the availability of Toluene raw material to support production continuity at CV. AK INDOPRIMA.
RCM-Based Maintenance Strategy for Coating Machine Downtime Reductione
The coating machine plays a critical role in pharmaceutical production because it affects coating uniformity, drying stability, product quality, and production continuity. However, maintenance studies on pharmaceutical coating machines are still limited, especially those that integrate reliability indicators, downtime dominance analysis, failure risk assessment, root cause analysis, and RCM decision logic into one structured maintenance framework. This study proposes an integrated RCM-based reliability decision framework to identify critical components and formulate maintenance priorities for reducing coating machine downtime at PT. Triyasa Nagamas Farma. The framework combines Mean Time Between Failure (MTBF), Mean Time to Repair (MTTR), Availability, Pareto analysis, Failure Mode and Effect Analysis (FMEA), Risk Priority Number (RPN), Fishbone Diagram, and internal operational benchmarking. The results showed that the coating machine experienced 12 failure events with a total downtime of 1,110 minutes, an MTBF of 743.92 minutes/failure, an MTTR of 92.50 minutes/failure, and an Availability of 88.94%. Pareto analysis indicated that the blower, spray gun/nozzle, and coating drum contributed 81.08% of total downtime. FMEA identified the blower as the most critical component, with the highest RPN value of 384 and the largest downtime contribution of 390 minutes. Internal benchmarking between March and April showed a 45.83% downtime reduction, a 125.21% increase in MTBF, a 24.17% decrease in MTTR, and a 10.76 percentage-point increase in Availability. The proposed strategy emphasizes preventive and condition-based maintenance through airflow inspection, filter cleaning, motor temperature monitoring, nozzle cleaning, control panel calibration, and coating drum inspection. This study contributes by providing a structured reliability-based maintenance priority framework for pharmaceutical coating machine operations.
Integrated ABC–AHP–TOPSIS Model for Used Distribution Material Management at PLN UP3 Mamuju
The management of used distribution materials at PT PLN UP3 Mamuju faces challenges related to idle inventory, dead stock, subjective decision-making, and the absence of a systematic prioritization framework. These issues reduce material utilization and limit the recovery of asset value. This study develops an integrated multi-criteria decision-making model by combining ABC Classification, the Analytical Hierarchy Process (AHP), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support objective and structured management of used distribution materials. The novelty of this study lies in the integration of inventory classification, multi-criteria weighting, and alternative ranking within a single decision-support framework for distribution material management, complemented by an economic feasibility evaluation. ABC Classification was used to identify economically significant materials, AHP to determine the relative importance of decision criteria, and TOPSIS to prioritize management alternatives, including reuse, redistribution, scrap, and disposal. The results indicate that safety is the most influential decision criterion, while conductors and distribution cables represent the highest-priority materials for reuse. The proposed model improves decision consistency, increases material utilization, reduces inventory losses, and generates significant economic benefits with a highly feasible investment. Overall, the integrated ABC–AHP–TOPSIS framework provides a practical decision-support tool for improving operational efficiency, optimizing asset value recovery, and supporting sustainable distribution material management in electric utility companies.
Component Reliability and Fleet Utilization for Spare Parts Management at Sidotopo Locomotive Depot
Locomotive reliability and spare parts availability are essential for maintaining railway operational performance and service continuity. However, previous studies have generally focused on component reliability or inventory management separately, with limited attention to integrating component reliability, fleet utilization, and spare parts demand characteristics into a unified framework for spare parts management. This study aims to address this gap by developing a reliability-based approach for identifying critical spare parts at Sidotopo Locomotive Depot, Operational Region 8 Surabaya, PT Kereta Api Indonesia (Persero). Historical maintenance and operational data were analyzed using Pareto analysis, material reconciliation, expert judgment, reliability analysis, fleet utilization assessment, and demand pattern classification. The analysis identified four critical components: AEP5, Victaulic 3.5", Flexible Hose, and Deadman. Weibull, Lognormal, and Exponential distributions were evaluated, and all components were found to follow the Weibull distribution, indicating wear-out failure behavior. At 1,008 operating hours, AEP5 exhibited the highest reliability, whereas Flexible Hose showed the lowest. Demand analysis classified AEP5 and Flexible Hose as intermittent demand, while Victaulic 3.5" and Deadman were categorized as lumpy demand. Fleet utilization analysis further revealed differences in component operating performance, providing additional insight into replacement priorities. The findings demonstrate that integrating reliability, fleet utilization, and demand characteristics provides a comprehensive basis for identifying critical spare parts, supporting maintenance planning, and improving inventory management in railway operations.
Optimization of pH Control in Acetic Acid (CH₃COOH)–Sodium Hydroxide (NaOH) Neutralization Process Using a Fuzzy Logic Controller
Conventional control systems, such as Proportional-Integral-Derivative (PID) controllers, have been widely applied in industrial process control due to their simple structure. However, these systems exhibit significant limitations when dealing with highly nonlinear processes and require retuning whenever plant conditions change. The pH control process represents one of the nonlinear systems in which small variations in hydrogen ion (H⁺) concentration can cause substantial changes in pH values. This study develops an acetic acid (CH₃COOH) and sodium hydroxide (NaOH) titration model as a case study and designs a pH control system based on a Fuzzy Logic Controller (FLC). The proposed FLC system employs two input variables, namely error (with a membership function range of −1 to 1) and error change (Δerror) (with a membership function range of −0.5 to 0.5), along with one output variable representing valve position (with a membership function range of 4–20). Simulations were conducted at three pH set points (4, 7, and 10) to evaluate the dynamic response characteristics of the proposed control system. The simulation results demonstrate that the FLC is capable of achieving a steady-state error below 0.5% for all evaluated set points. Furthermore, the overall simulation error remains below 5%, indicating that the developed titration model provides adequate accuracy. This study confirms that FLC is an effective approach for handling nonlinear characteristics in pH control processes.
Application of Artificial Intelligence in the Development of Automated Website Creation to Support Digital Transformation
The rapid advancement of digital technology has encouraged organizations and educational institutions to adopt digital transformation strategies to improve efficiency and service quality. Website development plays a crucial role in supporting this transformation; however, traditional website creation often requires significant time, technical expertise, and continuous maintenance. This study aims to investigate the application of Artificial Intelligence (AI) in automated website development to support digital transformation. The proposed system utilizes Artificial Intelligence and Natural Language Processing (NLP) techniques to automatically generate website content, structure, and layout based on user requirements. A comparative analysis was conducted between conventional website development methods and AI-based automated website generation. The system performance was evaluated using five criteria: website development time, content update efficiency, information accuracy, user satisfaction, and website adaptability. Development time was measured in minutes required to complete a website, content update efficiency by the average time required to publish updates, information accuracy through expert validation, user satisfaction using a 5-point Likert-scale questionnaire based on the System Usability Scale (SUS), and website adaptability by assessing the system's ability to accommodate changing user requirements. Statistical analysis was performed using descriptive statistics and paired sample t-tests (p < 0.05) to determine significant differences between the conventional and AI-based approaches. The results indicate that AI implementation significantly improves website development speed, content update efficiency, information accuracy, user satisfaction, and website adaptability. Furthermore, the automation process reduces administrative workload and enables real-time content updates. The findings demonstrate that AI-based website development provides a more efficient, scalable, and adaptive solution for organizations seeking to accelerate digital transformation. Therefore, the integration of Artificial Intelligence into website development can enhance operational effectiveness and improve digital service delivery.
Design and Implementation of a Web-Based Monthly Health Reporting System for the North Lampung District Health Office
The management of public health data at the district level still faces significant challenges, including manual reporting processes, data redundancy, and delayed information aggregation. These issues are also observed in the North Lampung District Health Office, where monthly health reports are still processed using fragmented and unintegrated systems. This study aims to design and implement a web-based monthly health reporting information system to improve data accuracy, integration, and reporting efficiency. The system was developed using the Rapid Application Development (RAD) method, which emphasizes iterative development and active user involvement. The system integrates health center data, disease classification (ICD-10), and LB1 morbidity reporting into a centralized platform equipped with real-time dashboards. Software evaluation was conducted using black-box testing and white-box testing, including cyclomatic complexity and code coverage analysis. The results indicate that all functional modules operate correctly, with code coverage exceeding 87% across all components. The system successfully reduces reporting time, improves data consistency, and enhances decision-making support for health authorities. Therefore, the proposed system provides an effective and reliable solution for digital transformation in regional health reporting systems.
Web-based Mobile New Student Admission Information System at Darul Fikri Wattarbiyah Islamic Boarding School
Darul Fikri Wattarbiyah Islamic Boarding School currently faces challenges in the efficient management of new student admissions due to its conventional and manual registration system. This poses a risk of data redundancy, difficulty in quickly validating files, and limited accessibility for prospective guardians outside the region. This study aims to design and build a mobile web-based New Student Admissions Information System (PSB) to digitize the entire registration cycle. The system was developed using the Waterfall method, which includes sequential stages of needs analysis, system design, coding, and testing. The selection of a mobile web platform is intended to allow the system to be accessed flexibly via mobile devices without requiring the installation of additional applications. The result of this study is an integrated platform capable of managing digital forms, uploading required files, administrative validation by the committee, and announcing selection results in real time. The implementation of this system is expected to improve data transparency, accelerate the operational performance of the PSB committee, and provide easy access for the community to reach educational services at Darul Fikri Wattarbiyah Islamic Boarding School in a more professional and efficient manner.
CTDI-Based Assessment of Radiation Dose Output Accuracy in a 128-Slice CT Scanner Using Head and Body PMMA Phantoms
Verification of Computed Tomography Dose Index (CTDI)-based dose output is essential for ensuring the accuracy of radiation delivery and supporting quality assurance in Computed Tomography (CT). This study aimed to verify the radiation dose output of a 128-slice CT scanner at the Radiology Department of RS Jiwa Menur Surabaya using CTDI measurements with standard PMMA head (16 cm) and body (32 cm) phantoms. Dose measurements were performed using a 100 mm pencil ionization chamber under standardized exposure parameters of 120 kV and 200 mA. CTDI100 values were measured at the center and peripheral positions of each phantom and subsequently used to calculate CTDIw and CTDIvol. The measured CTDI values were compared with the corresponding dose indices displayed by the CT scanner to determine the percentage deviation. The measured CTDIvol showed a deviation of 10.65% from the displayed value, while the body phantom exhibited a deviation of 10.16%. Although the measured CTDI values were consistently higher in the head phantom than in the body phantom due to differences in attenuation characteristics and phantom geometry, all deviations remained within the ±20% tolerance limit specified by BAPETEN regulations. These findings indicate that the CT scanner's dose output performance is consistent with the applicable regulatory acceptance criteria. Periodic CTDI-based verification is recommended to support ongoing quality assurance, maintain dosimetric accuracy, and optimize radiation protection practices in accordance with the ALARA principle.
Design and Development of a Web-Based SBMPTN Try-Out Application: A Case Study at SMK N 2 Kotabumi
This research is motivated by the suboptimal state of exam simulation media for students at SMK N 2 Kotabumi in preparing for the Joint Entrance Examination for State Universities (SBMPTN), where conventional tryout processes result in slow and inaccurate data processing. As a solution to these issues, this study designs a web-based tryout application developed using the Waterfall method through the stages of requirement analysis, design, coding, and systematic testing. The application is designed to simulate the Computer Assisted Test (CAT) mechanism in real-time, thereby providing instant and transparent assessment feedback for both students and the school. The results of this research are expected to provide an effective self-simulation platform to enhance academic readiness and the accountability of student learning evaluations at SMK N 2 Kotabumi, ultimately achieving a more competitive graduation quality at the national level.
Development of Inventory and Goods Lending Systems Using QR Code at Universitas Muhammadiyah Kotabumi
Asset and inventory management in higher education institutions is a crucial aspect of supporting academic operational efficiency. At Universitas Muhammadiyah Kotabumi (UMKO), the current inventory recording system and lending procedures are still conducted conventionally, leading to risks of data inaccuracy, slow information retrieval, and the potential loss of physical lending forms. This study aims to design and develop an integrated inventory and goods lending information system utilizing Quick Response (QR) Code technology. The system development method employed is the Waterfall model. This method was selected based on the characteristics of inventory systems that require structured development stages and comprehensive documentation. QR Code technology is implemented to automate item identification and accelerate lending transactions through mobile device scanning. The result of this research is a web-based application capable of providing real-time asset data, facilitating item tracking, and generating accurate inventory reports. The implementation of this system is expected to minimize human error, increase asset management transparency, and support the modernization of digital infrastructure within Universitas Muhammadiyah Kotabumi for more professional services.
Development and Functional Evaluation of a Web-Based Medical Equipment Maintenance Reminder System Integrated with Telegram Notifications
Proper maintenance of medical equipment is essential for ensuring patient safety and clinical operational continuity, yet traditional manual documentation often leads to missed maintenance schedules. This research aims to develop and functionally evaluate a web-based Computerized Maintenance Management System (CMMS) integrated with Telegram notifications. The system was developed using the Waterfall software development model, utilizing XAMPP (PHP and MySQL) for backend management and the Telegram Bot API via webhook for automated alert delivery. The functional evaluation was conducted in a prototype environment using black-box testing and API response time analysis. Results demonstrated that the system successfully automates maintenance scheduling and delivers real-time notifications with a 100% success rate across all functional test cases. While this study confirms the technical reliability of the prototype in a controlled environment, further clinical deployment is required to measure its impact on operational efficiency. It is concluded that this digital solution provides a functional and reliable baseline for improving medical equipment management in healthcare facilities.
Computerized Maintenance Management System
Integration of Analysis Power Flow, Short Circuits, and Coordination Relays on the System Power Distribution: A Systematic Literature Review
This literature review comprehensively examines the integration of power flow analysis, short-circuit analysis, and relay coordination in electrical power distribution systems. Drawing upon 25 peer-reviewed journal articles and textbooks published between 2015 and 2023, this review synthesizes methodologies, findings, and trends across three interrelated domains. Power flow analysis underpins system planning and operational efficiency; short-circuit analysis ensures equipment rating adequacy and safety; and protective relay coordination guarantees selective fault isolation and system continuity. The increasing penetration of distributed generation (DG), photovoltaic (PV) systems, and battery energy storage (BESS) presents unprecedented challenges to traditional protection paradigms, necessitating adaptive and integrated analytical approaches. This review identifies research gaps, highlights emerging tools such as heuristic optimization and smart-grid-enabled adaptive protection, and proposes directions for future integrated distribution system analysis frameworks.
Designing Solar Power Plants as a Source of Electricity for Insect Traps
This study proposes the design and evaluation of a solar-powered insect trapping system as an environmentally friendly solution for pest control in agricultural fields. The system integrates a photovoltaic (PV) module as the primary energy source with a microcontroller-based control unit. A Passive Infrared (PIR) sensor is employed to detect insect movement, while an electric grid mechanism is used for pest eradication. The system is controlled using an Arduino microcontroller to ensure synchronized operation between detection and execution components. The research methodology involves system design, hardware and software implementation, and field testing in rice cultivation areas. Experimental results indicate that the device provides an effective illumination radius of approximately 5 meters and successfully captured 87 insect pests within two days of operation. Furthermore, the system demonstrates stable energy performance, with efficient battery charging during the daytime and reliable operation at night. These findings confirm that the proposed solar-powered insect trap is a viable, sustainable, and eco-friendly alternative to conventional pesticide-based pest control methods. Future work will focus on optimizing light intensity, improving sensor accuracy, and integrating ultraviolet light to enhance insect attraction.
Enzymatic Conversion of Biomass to Biofuels: Process Mechanisms, Enzyme Optimization, and Industrial Challenges
Biomass conversion to biofuels has gained significant attention as a sustainable alternative to fossil-based energy, with enzymes playing a crucial role in enhancing process efficiency and selectivity under mild operating conditions. This review article examines the roles of key enzymes, including cellulases, hemicellulases, amylases, lipases, and laccases, in lignocellulosic hydrolysis, lignin structure disruption, and lipid esterification and transesterification reactions, enabling the production of bioethanol and biodiesel under milder operating conditions with high selectivity. Comparative insights are provided to highlight the relative performance of these enzymes in terms of conversion efficiency, substrate specificity, operational stability, and tolerance to process conditions, as reported in recent studies. The review further examines enzyme optimization strategies, including immobilization techniques, protein and metabolic engineering approaches, and advanced bioreactor configurations, which have been shown to improve enzyme reusability and overall process productivity. Quantitative performance indicators reported in the literature, such as biofuel yields and conversion efficiencies, are critically analyzed to assess the feasibility of enzymatic routes compared to conventional chemical processes. Finally, key industrial challenges related to enzyme cost, stability, and scalability are discussed, along with emerging technological pathways that support the potential industrial implementation of enzymatic biomass conversion for biofuel production.
Nonlinear Trade-Off Between Agitator Speed and Biodiesel Quality in Transesterification: A Lab-Scale Study
The agitator speed controls the intensity of interaction between the immiscible oil and methanol phases during transesterification. This, in turn, shapes the overall reaction environment during the biodiesel production process. While higher agitation rates are often associated with increased mass transfer, their impact on the resulting fuel properties is not always proportional. In a controlled laboratory setting, the effect of agitator speed was examined at 300, 500, 700, and 900 rpm, with particular attention paid to biodiesel yield, density, kinematic viscosity, and flash point. A gradual shift in system behavior became apparent as the agitation rate increased. In the intermediate range of 500–700 rpm, the process tended to stabilize, producing biodiesel with relatively consistent characteristics. The measured kinematic viscosity was between 4.42 and 4.45 cSt, accompanied by a density of approximately 861 kg/m³. However, pushing the system to higher agitation rates led to a different response. At 900 rpm, the yield dropped sharply to 49.53%, and the viscosity increased to 4.92 cSt. This behavior indicates the onset of unfavorable hydrodynamic conditions, where intensive mixing likely disrupts phase separation and promotes the formation of a stable emulsion. Meanwhile, the flash point continued to increase from 124.7 °C to 166.8 °C, indicating a progressive decrease in residual methanol. Overall, these observations suggest that the influence of stirrer speed is governed by competing mechanisms rather than a simple monotonic trend. Operation in the 500–700 rpm range appears to offer a more reliable compromise, where conversion and fuel properties remain balanced. From an engineering perspective, this range provides a useful reference for establishing operating conditions in biodiesel reactors, especially when considering process development beyond the laboratory scale while maintaining consistent product quality.
Design and Development of an Automatic T-Shirt Folding Tool Based on the Arduino Uno Microcontroller
One of the household chores that is concerned about this problem is folding the t-shirt from the drying process. When there are lots of stacks of t-shirts, especially for laundry entrepreneurs. Manually folding a t-shirt will certainly look less presentable if done very quickly. The purpose of this study is to accelerate the activity of the laundry entrepreneur, therefore a model of tool that can help in t-shirt folding quickly with a workforce is automatically created. Using six servo motors as a folding drive on the right, left and bottom. To start the process when the tool is used, by utilizing an ultrasonic sensor to start and stop the folding process automatically. The components in this study are controlled by the Arduino UNO microcontroller so that it can work automatically in accordance with the commands that have been applied. There are ten tests using t-shirts of the same size. So the results of the ten testing processes carried out a folding duration of about 3 seconds with 1 t-shirt, 9 seconds by folding 3 t-shirts, 15 seconds by folding 5 t-shirts, 21 seconds by folding 7 t-shirts, and 30 seconds by folding 10 t-shirts. It can be concluded that folding a t-shirt using this tool feels faster and neater than the manual folding process.
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Design of a Portable Tidal Power Plant (PLTPS) Using Static Feet
The potential of marine energy to generate electricity is one of the potentials that is not widely known to the general public. One of the natural phenomena that can be used as a power plant is the tides of sea water. Tidal Power Plant is a power plant using sea water when it is high tide and using estuary water power when the sea water is receding. Tidal Power Plants are generally built permanently using dams, then a Portable Tidal Power Plant is developed that can be placed on the beach when the tide is high and in the estuary when the sea water is receding. The method used in this research is the experiment method on Portable Tidal Power Plant. The Portable Tidal Power Plant uses seawater and estuary water to turn the mill, then the windmill will produce mechanical energy, and the generator will convert the mechanical energy from the wheel into electrical energy. The Portable Tidal Power Plant was tested on the beach when the sea water is high, the highest voltage is 3.3 V and the highest RPM is 413 RPM. When tested on the estuary when the sea water is receding, the highest voltage is 2.6 V and the highest RPM is 284.4 RPM