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Photosynthesis is a vital process for plants in converting light energy into chemical energy stored in organic compounds. Light intensity is one of the main factors influencing the efficiency of this process. This study aims to determine the effect of different light intensities on the photosynthesis rate and growth of spinach plants (Amaranthus sp.), which is a C4 plant species.The method used was a quantitative experiment with three light intensity treatments: low (0–10 lux), medium (10–20 lux), and high (>20 lux), each replicated three times over six weeks. Observed parameters included plant height, number and length of leaves, as well as the photosynthesis rate measured using the IRGA method.The results showed that light intensity significantly affected the growth and photosynthesis rate of spinach plants. The high light treatment produced the most optimal growth, with an average plant height of 4.92 ± 0.4 cm and the highest photosynthetic activity. Conversely, the low light treatment caused symptoms of etiolation, reduced vitality, and faster plant death, averaging by the third week.In conclusion, increasing light intensity significantly enhances photosynthesis efficiency and growth in spinach plants. These results can serve as a basis for regulating light intensity in cultivation systems, both conventional and hydroponic.  

This study aims to design and develop a GPS tracking system for ships integrated with Internet of Things (IoT) technology. The system enables real-time ship position monitoring via the Telegram application by utilizing ESP32, SIM800L, and GPS Neo 6M modules. The research employs an experimental method with both static and dynamic testing of hardware components such as GPS modules, SD cards, and battery performance. The test results demonstrate that the system successfully transmits accurate coordinate data to Telegram and logs the location on the SD card module. This system is expected to serve as an alternative solution to improve ship tracking security and efficiency, particularly in emergency situations or AIS signal loss.

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.

This study aims to design and implement a web-based outpatient registration information system at Regional General Hospital (RSUD) X to overcome issues arising from the manual registration system. The current manual registration system at RSUD X causes problems such as long queues, potential data entry errors, lost files, and difficulty in finding patient medical history. As a result, patient waiting times are longer, the administrative staff's workload increases, and the overall effectiveness of services decreases. The research method used is a qualitative one, which describes various processes in the system, from patient data input to the digital registration receipt output. The system design approach uses the waterfall method. Data collection was carried out through purposive sampling, while data gathering was done through interviews with registration officers, direct observation, and a documentation study in the medical records unit. The system is built with a combination of technologies, including PHP as a server-side programming language and MySQL as the database. The result is a web-based outpatient registration information system that can improve the efficiency and quality of hospital services. The advantages of this system include wide accessibility, time efficiency, and effective data management. This system is expected to reduce patient queues, improve the efficiency of the registration process, and simplify patient data management. However, the implementation of this system may face challenges such as limited resources and user skills. Solutions to overcome these challenges are effective resource allocation and user training. Overall, the web-based outpatient registration information system can be an effective solution to improve service quality at RSUD X.

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

Ports are critical infrastructure in maritime transportation, facilitating the movement of goods and people while serving as hubs for economic activities. The Tanjung Pakis Class III Harbormaster and Port Authority Office (KSOP) utilizes the Electronic Reporting Information (SIRANI) application for reporting loading and unloading data. However, challenges such as discrepancies, technical disruptions, and delays in data processing often arise. This study aims to describe the loading and unloading data processing and the efficiency of reporting time on the SIRANI application at KSOP Class III Tanjung Pakis. Employing a descriptive qualitative approach through interviews, observations, and documentation, the research finds that data processing is less effective due to low data validity and errors in the Inaportnet platform. Reporting to SIRANI is also inefficient due to delays in data handling and technical issues with the application. Corrective measures, such as early data verification and manual data handling by reporting officers, have improved effectiveness by reducing the process from five to three steps and cutting reporting time from 6 hours to 3 hours and 30 minutes. The study concludes that optimizing data processing and inter-unit coordination can enhance the effectiveness and efficiency of reporting. Recommendations for future research include real-time integration and the development of backup mechanisms to address technical disruptions.

Nonlinear eigenvalue problems (NEPs) pose significant challenges in mathematical physics and other computational applications due to their nonlinear nature, which makes analytical solutions difficult to obtain. NEPs are encountered in various scientific and engineering fields, including signal processing, electronic structure calculations, and structural optimization. This study aims to explore the application of adaptive algorithms in solving nonlinear eigenvalue problems, with a primary focus on improving accuracy and computational efficiency. The proposed method combines an iterative solver with adaptive step-size adjustment, where the step size is dynamically adjusted during the iteration based on error estimates calculated at each step. This approach enables faster convergence and significant reductions in computational time without compromising accuracy. In experiments conducted on large-scale problems, the adaptive algorithm reduced computational time by 40% faster compared to fixed-step iterative methods. The comparison between the adaptive algorithm and traditional methods showed that the adaptive algorithm is not only more efficient but also more robust when dealing with high-complexity problems. Additionally, the adaptive algorithm provides more accurate error estimates, allowing better error control throughout the iteration process. Overall, this study concludes that adaptive algorithms offer a more effective and efficient solution for complex nonlinear eigenvalue problems and can be adapted to various types of problems in scientific and engineering applications. Further research could focus on optimizing the implementation of this algorithm for larger and more complex scales.

Technological advancements and digital transformation have significantly impacted business management, including the consulting sector. PQM Consultants faces challenges in managing inquiries, which are still manually handled using Microsoft Excel. This manual process results in fragmented data, delays in accessing information, input errors, and difficulties in real-time monitoring of inquiry statuses. These issues affect operational efficiency, data transparency, and timely decision-making.   This research focuses on developing a web-based inquiry management platform through the Design Thinking methodology, aiming to improve the efficiency and effectiveness of inquiry handling at PQM Consultants. Adopting a qualitative case study approach, the study gathers insights through in-depth interviews, direct observations, and document reviews involving consultants and support team members who are directly engaged in the inquiry process. Research participants consist of consultants and support team members directly involved in inquiry management. The study uses a Design Thinking approach, which focuses on solving problems through repeated refinement and actively involving users throughout the development process. This framework supports the development of creative solutions by deeply understanding user needs, defining key challenges, and continuously refining ideas through prototyping and testing (Costich 2021). The Empathize stage focuses on understanding user needs and challenges. The Define stage formulates the core problems specifically. The Ideate stage generates creative ideas as potential solutions. The Prototype stage develops an initial design of the platform, which is then tested during the Test stage to gather feedback for further refinement. The results indicate that the web-based inquiry management platform effectively addresses various challenges in managing inquiry data, such as access delays, input errors, and data fragmentation. The platform enables consultants to monitor inquiry statuses in real-time, access data transparently, and make faster and more accurate decisions.

This study explores the integration of AI-driven process optimization in Waste-to-Energy (WtE) systems to enhance urban sustainability. The research focuses on designing a gasification-based WtE system, incorporating AI predictive control to optimize energy conversion processes. The AI system adjusts operational parameters in real-time, improving energy conversion efficiency by 25% and reducing carbon emissions by 40%. Additionally, the system's waste-to-energy conversion rate is projected to increase by 20%, and operational costs are expected to decrease by 30%. Data collection and analysis are carried out using advanced sensors to monitor key parameters such as temperature, gas composition, and energy output, which are then processed by machine learning algorithms for predictive analysis. The results show that the AI optimization significantly enhances system performance, offering a sustainable solution for urban waste management. The study highlights the technical and operational challenges of integrating AI into existing WtE systems, including the need for infrastructure upgrades and scalability considerations. It also discusses the socio-economic impacts, including job creation, reduced energy costs, and improved public health. The findings demonstrate the potential of AI-based WtE systems in reducing waste, generating clean energy, and mitigating climate change, positioning them as a viable solution for sustainable urban development.

The major work of the Cijurey Dam Project Package III is located in Bogor Regency, West Java Province, which is a spillway structure consisting of iron structures and concrete structures. To achieve the right quality, on time, on cost, and zero accidents, concrete casting calculations and appropriate work implementation methods are required. This research method uses descriptive with quantitative and qualitative approaches. The stages of concrete casting work for the Cijurey Dam spillway structure's launch channel consist of preparation, measuring the launch channel block boundaries, lean concrete work, reinforcement work, formwork panel preparation, formwork panel installation, waterstop and dowel bar work, concrete sampling and slump test, concrete casting, concrete curing/maintenance, formwork dismantling, and finishing of tierod holes. The results of the study showed that the calculations for casting the concrete channel were a large volume of concrete channel casting of 135,000 m3, concrete material requirements (in the form of: 1,115,100 sacks of cement, 58,050 m3 of sand, 1,020,600 m3 of gravel, and 29,025,000 liters of water), channel formwork (surface area) of 27,675 m2, productivity of casting the concrete channel launcher of 24 m3/hour or 193 m3/day, and the efficiency of casting the concrete channel launcher time of around 125% (25% faster than planned). Meanwhile, the stages of work implementation for channel concrete casting include preparation work, measurement work for the boundaries of the launcher channel blocks, lean concrete work, reinforcement work, formwork panel preparation work, formwork lubrication work, formwork panel installation work, waterstop and dowel bar work, concrete sampling and slump test work, channel concrete casting work, concrete curing/maintenance work, formwork dismantling work, and finishing work for tierod holes. In addition, an inter-segment locking system with a shear key is used to overcome problems with the concrete pump, joint inspections are carried out during mobilization, routine inspections, repairs and replacement of the concrete pump.

Pollution of hydrocarbons in marine waters was recorded up to 2003 around 6.44 million tons and dominant due to the results of fishery port activities to cause the balance of coastal ecosystems disrupted. The drained state funds for its handling can reach 1000 USD per ton up to 33,000 USD in each region. Various efforts have been made is still not effective enough. The purpose of this program is to get the appropriate technology design in overcoming the problem of hydrocarbon pollution in the fishing port. The method used by literature and field study and a series of testing tools. The solution is called MABOA (Magic Briquette Oil Absorbent) is a technology that is applied aplikatif appropriate to overcome the problem of oil pollution in the port area. This tool is a net with the main components of magic briquettes, auto-spray containing bacteria degradation and microcontroller which as a whole has the ability to absorb and degrade hydrocarbon compounds. The circular MABOA net will prevent the expansion of the oil spill zone by the absorption process by magic briquettes. Pseudomonas puttidae and Bacillus sp. In auto-spray will be automatically sprayed over the surface of the spill zone to perform the decomposition of hydrocarbon compounds. Bacteria will grow and utilize hydrocarbons that have been absorbed and accumulated in the body of magic briquettes so that the cleaning process becomes more effective and faster. Results from a series of trials showed that MABOA with 3meter diameter dimension able to absorb hydrocarbon compound as much as 35.000mL with 3-5min time absorption rate and with density of colonies of bacteria 3,5x109 able to degrade 32% of existing hydrocarbon compound with efficiency time 3-7 days. The data is an accumulation of those component test result data.

This study aims to design and develop a prototype of an Internet of Things (IoT)-based land watering monitoring system using the ESP8266 module and soil moisture sensor. This system is designed to help farmers manage watering automatically based on soil moisture conditions, so that they can save water and labor. The process at this time begins with a literature study, hardware and software design, to prototype testing on two soil conditions, namely dry and wet. Data from the sensor is sent in real-time to the IoT platform, if the soil is detected dry, the system automatically activates the water pump. The results of the test show that the sensor works accurately in distinguishing soil conditions, and the system is able to water automatically according to needs. Furthermore, this system can also be monitored and controlled remotely via the internet. The conclusion of this study shows that the use of IoT technology is very potential to increase efficiency and effectiveness in agricultural land management.

This study aims to determine the influence of Work Motivation, Work Environment, and Job Satisfaction on Employee Productivity at PT. Nihon Seiki Indonesia. This research is motivated by the phenomenon of declining employee productivity as shown by the non-achievement of work targets in recent months. This problem indicates the possibility of a decline in internal quality that needs to be evaluated, such as low motivation, less supportive work environment conditions, and less than optimal job satisfaction. This decline can also have an impact on the company's overall performance, including in terms of efficiency, production quality, and customer satisfaction that are declining over time. The method used in this study is a quantitative method with a descriptive and verifiable approach. The data collection technique was carried out through the distribution of questionnaires to 90 employees as a sample of the total population of 115 employees working in the company. The research instrument was tested through validity and reliability tests. Data were analyzed using multiple linear regression analysis, t-test (partial), F test (simultaneous), and determination coefficient (R²) test, with the help of SPSS software version 26. The results of the study show that Work Motivation, Work Environment, and Job Satisfaction have a significant effect on Employee Productivity, both partially and simultaneously. These findings confirm that the increase in employee work productivity is not only dependent on external factors such as technology and management systems, but is also highly determined by internal factors that are directly related to individual comfort and satisfaction at work. Therefore, company management needs to focus more on efforts to create a conducive work environment, build strong motivation, and increase employee job satisfaction as a strategy to optimize productivity. In addition, continuous training and effective communication between teams also need to be improved to maintain morale, collaboration, and the achievement of overall organizational targets.

Digital transformation has fundamentally reshaped auditing practices, particularly through the integration of technologies such as Artificial Intelligence  (AI), big data, and blockchain. This study aims to identify the challenges, opportunities, and implications of digital transformation on audit quality in the big data era. A qualitative descriptive approach was employed, using case studies and simulations as the primary methods of analysis. The findings reveal that digitalization contributes positively to audit quality by enhancing time efficiency, testing accuracy, and risk detection capabilities. Nevertheless, challenges such as technological resource limitations, the need for auditor skill development, and ethical and data security risks remain significant barriers. This study highlights the urgency of developing a holistic digital audit framework and updating regulatory standards to ensure sustainable digital transformation. Recommendations are provided for practitioners, regulators, educational institutions, and scholars to collaborate in building an adaptive, professional, and ethical auditing ecosystem in the digital era.

This research discusses issues related to the Navigation Aids, particularly the buoy objects. The study aims to design and develop a monitoring device for buoys based on the Internet of Things (IoT). This study aims to design and develop a buoy monitoring device based on the Internet of Things (IoT) as a solution to the lack of monitoring and supervision of buoys in the Tanjung Perak Navigation District. The identified contributing factors include: (1) displacement of buoys due to ocean currents, (2) suboptimal performance of the flasher lights, and (3) disappearance of the buoys. Using the Research and Development (R&D) method, the researcher designed a monitoring system capable of real-time observation through internet connectivity. The product feasibility test conducted by experts resulted in an average percentage index of 94%, placing it in the 'Highly Feasible' category for use. The results demonstrate that the IoT-based buoy monitoring device significantly enhances the efficiency and effectiveness of buoy supervision. This research is expected to contribute to the development of knowledge within the Bachelor of Applied Science in Marine Transportation program and provide technological innovation in the field of maritime navigation in Indonesia.    

This study discusses the arrangement of queue lanes in the supervision of weighing freight transportation at UPPKB Singosari to improve service efficiency. Based on the analysis of the Minimum Service Standards (SPM), three main indicators, namely compliance, conformity, and implementation, have reached 100%, while the accuracy indicator has only reached 50% due to the duration of weighing exceeding the set time limit. The main factor causing long queues is the lack of clear lane markings, making it difficult for vehicles to be directed without an officer. In addition, the parking area of 3,330.6 m² has not been utilized optimally. Improvement efforts include the implementation of queue lane markings to reduce dependence on officers, optimization of facilities such as Pos 2 which is parallel to Pos 1, and improvement of human resource management to suit operational needs. The use of Weight in Motion (WIM) technology is also recommended to reduce workload and accelerate the supervision of freight transport vehicles. By implementing this strategy, it is hoped that services at UPPKB Singosari can be more efficient, transparent, and in accordance with applicable standards.

The issue of illegal parking in Batam City has become a serious concern, where unofficial parking attendants often collect fees from drivers without contributing to the local government. This situation not only disrupts order and security but also harms local revenue (PAD). Illegal parking practices create discomfort for the public and reduce trust in the existing parking system. Moreover, it hinders the optimization of PAD potential that should be generated from a well-organized and orderly parking system. This research aims to develop a technological solution to address this issue by creating an Android application called Fast Parking System. The application is designed to improve parking system efficiency through a fast, secure, and transparent cashless payment system. By using this application, drivers can pay for parking digitally, reducing direct interaction with parking attendants and minimizing the potential for illegal fees. The application is also integrated with an official surveillance system, allowing local authorities to monitor parking activities in real-time and ensure that parking fees are directed to the local treasury. It is expected that the implementation of Fast Parking System will create a more orderly, safe, and efficient parking system. With this solution, the local government can not only increase transparency but also optimize revenue from the parking sector. In addition, the application offers greater convenience for the public in using well-managed parking facilities. The findings from this research can serve as a useful reference for the Batam City Government and other cities in Indonesia facing similar illegal parking issues. It can also be used as a guideline for developing more efficient and modern technology-based parking systems.