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This research examines the implementation of green building principles in the design of the Multipurpose Building at SMP–SMA Islam Hidayatullah Semarang, focusing on energy-efficient strategies and spatial comfort based on the GREENSHIP GBCI certification criteria. The study employs a qualitative descriptive method through interviews with the architect, analysis of architectural drawings, interpretation of interior design visualizations produced by Falana Studio, and literature review on sustainable building design. The findings indicate that the building consistently applies passive design strategies, including the optimization of natural lighting through large openings and a central void, the application of cross-ventilation on each floor, and the integration of façade vegetation that reduces surface temperature and improves microclimate performance. Material selection such as GRC panels, HPL, and modular plywood supports long-term durability, while the interior design demonstrates strong visual comfort and ergonomic quality through indirect lighting, neutral color schemes, and activity-based furniture layout. According to the GREENSHIP assessment categories, the building fulfills Energy Efficiency and Conservation (EEC), Indoor Health and Comfort (IHC), Material Resources and Cycle (MRC), and Appropriate Site Development (ASD) criteria. In conclusion, the Multipurpose Building successfully integrates green building principles as an effective approach to energy efficiency and the enhancement of the educational environment.

This study aims to analyze the effect of asset growth, capital structure, and asset structure on the profitability of PT Astra Graphia Tbk during the period 2016–2023. The research employs a quantitative approach with a causal research design using secondary data derived from the company’s quarterly financial statements. A total of 32 quarterly observations were selected through purposive sampling. Profitability is measured using Return on Equity (ROE), while data analysis is conducted using multiple linear regression. Prior to hypothesis testing, classical assumption tests including normality, multicollinearity, heteroskedasticity, and autocorrelation tests were performed to ensure the robustness of the regression model. The results indicate that asset growth, capital structure, and asset structure simultaneously have a significant effect on firm profitability. However, partially, only asset structure has a significant effect on profitability, while asset growth and capital structure show no significant influence. These findings suggest that efficient asset composition plays a more critical role in improving profitability than mere asset expansion or increased leverage. The managerial implication of this study highlights the importance of optimizing asset structure to enhance the firm’s ability to generate sustainable profits.

The disparity in the quality of rehabilitation services across regional work units presents a significant challenge to effective public management. This study aims to bridge the gap between problem diagnosis and policy prediction by proposing a hybrid, data-driven approach. We integrate K-Means Clustering to map the current state of service quality and Stochastic Simulation to predict the impact of strategic interventions. Using the 2024 Public Satisfaction Index (IKM) dataset from the National Narcotics Agency (BNN), the K-Means algorithm initially identified 26 work units (15.7%) in the "Red Zone" (critical performance), highlighting urgent areas for improvement. Next, a stochastic simulation modeling a "Directed Priority Intervention" scenario was run. The results predicted a significant structural shift in the distribution of service quality, characterized by an 80.8% decrease in critical units (down to 5 units) and a 71.8% increase in excellent performing units (up to 67 units). These findings validate that the integration of clustering and simulation provides a comprehensive framework for evidence-based decision-making, enabling policymakers to optimize resource allocation and efficiently accelerate national service standardization.

This study aims to analyze inventory control of catfish seeds at ABC Company by comparing the conventional inventory method represented by the Economic Order Quantity (EOQ) approach and the Just In Time (JIT) system in order to improve cost efficiency. This research employed a descriptive quantitative approach using a case study design. Data were collected through direct observation, semi-structured interviews with company management, and documentation of inventory and cost records for the 2024 operational period. The analysis method involved calculating optimal order quantities, ordering frequency, delivery frequency, and total inventory costs using EOQ and JIT formulas, followed by a comparative cost efficiency analysis. The results show that the conventional method resulted in a total inventory cost of Rp 75,050,000 per year with high ordering frequency. In contrast, the implementation of the JIT system reduced inventory costs to Rp 18,762,500 per year, achieving a cost efficiency of 72%. These findings indicate that the JIT system is more cost-efficient than the conventional method; however, its implementation requires careful consideration of supplier capacity, logistics readiness, and biological risks associated with live inventory. This study contributes empirical evidence on the applicability of JIT in the aquaculture sector, which has different characteristics from manufacturing industries.

The integration of cloud computing and data security systems is vital for the operational success and competitiveness of fintech startups. Cloud computing enables these startups to scale quickly, manage resources efficiently, and reduce infrastructure costs, making it an indispensable tool for businesses in the rapidly evolving fintech sector. However, with the benefits come significant challenges, particularly in data protection and cybersecurity. As fintech services handle sensitive financial data, ensuring robust security measures such as encryption, access controls, and continuous monitoring is crucial to maintaining user trust. Furthermore, regulatory compliance, both local and global, adds complexity to the data protection strategies of fintech companies. This research explores the key factors that drive cloud adoption in fintech, the security challenges associated with cloud environments, and the strategies implemented by startups to address these challenges. Interviews with IT managers from Indonesian fintech startups reveal that while cloud computing offers scalability and cost-effectiveness, issues like compliance with local regulations and the protection of sensitive data remain major concerns. The research suggests that fintech startups should invest in both cloud infrastructure and advanced cybersecurity measures to protect their operations and customer data. Additionally, creating a comprehensive roadmap for regulatory compliance and fostering partnerships with cybersecurity firms will help mitigate risks and ensure long-term success. The findings highlight the importance of integrating cloud computing with effective security strategies to navigate the complex regulatory and security landscape of the fintech industry.

The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.  

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

Fortuna Furniture still manages its inventory manually, which causes many problems, including delayed reports, inaccurate information, and the potential for lost records. These issues affect the quality of decision-making and operational efficiency. This service aims to build a web-based inventory information system that improves speed, accuracy, and integration in inventory management. The system development process follows the Waterfall model SDLC approach, with requirements analysis, design, implementation, and testing supported by interviews, observation, and documentation. The results of the activity show that the developed system can reduce input errors, speed up the process of recording incoming and outgoing goods, and enable real-time stock monitoring. Additionally, the report generation process becomes more efficient and easier for management to access. Overall, the use of this information system significantly improved the efficiency of Fortuna Furniture's inventory management and demonstrated the importance of digitalization in helping businesses run more smoothly.

This study aims to analyze and compare rice inventory valuation using the FIFO, FEFO, and Average methods in determining the cost of goods sold (COGS) at UD. Rahayu Indah. This study uses a quantitative descriptive approach with a perpetual inventory recording system. Data were collected through observation, interviews, and documentation from January to December 2024. The results show that each method produces different COGS values: the FIFO method produces the lowest COGS, followed by FEFO, while the Average method produces the highest COGS. This difference is influenced by cost allocation based on the order of goods in and out and price fluctuations during the production period. These findings indicate that the FIFO method is the most effective method to be applied at UD. Rahayu Indah because it reflects the logical physical flow of goods, supports cost efficiency, and increases the company's gross profit. In addition, this method is also in line with the company's operational characteristics, which have stable purchasing patterns and are in accordance with the principles of PSAK No. 14 on inventory. The results of this study are expected to assist UD. Rahayu Indah's management in determining an inventory valuation strategy that is efficient, accurate, and supports business sustainability.

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in the economy of Ketapang Regency but still face challenges in financial recording and management. Many MSME actors have not yet utilized digital technology optimally, leading to manual bookkeeping processes that are prone to errors. This study aims to develop a web-based financial bookkeeping application using the User-Centered Design (UCD) approach, focusing on user needs. The UCD method was applied through four stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating the results. The developed application includes key features such as product management, supplier management, sales recording, receipt printing, and financial reporting. Based on usability testing involving 25 respondents, the application achieved an average satisfaction level of over 85% across aspects of learnability, efficiency, memorability, error handling, and satisfaction. The findings indicate that the application effectively supports MSME actors in recording financial transactions more efficiently, accurately, and reliably. Future improvements may include the integration of digital payment systems, enhanced data security, and interactive graphical financial analysis features.

The manual submission of employee complaints often leads to slow handling, disorganized documentation, and limited transparency regarding complaint progress. These issues hinder internal communication and reduce the effectiveness of administrative processes. This study aims to design and implement a Web-Based Employee Complaint Submission System using PHP and the Bootstrap framework to improve efficiency and accuracy in managing complaints. The system supports three main categories of complaints: payroll, occupational safety and health, and work facilities. It also enables employees to track complaint status through request, approved, and rejected indicators. Administrators can manage user accounts and generate official follow-up letters. Implementation results show that the system improves data recording, enhances documentation order, and increases transparency in complaint handling. Overall, the system facilitates more structured, efficient, and traceable communication between employees and management, supporting better corporate governance.

The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.”  The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique.  Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.

The problems in rice fields are complex and varied, depending on geographic location, rice variety, and growing season. Pests often cause serious economic losses. The Solar Sonic Repeller is an innovative portable pest control device designed to address pest problems by utilizing renewable energy, specifically solar energy. This product aims to offer an environmentally friendly and efficient solution. It works by emitting ultrasonic sound waves with a frequency of 30,000–40,000 Hz. The device's advantages lie in its portability and energy independence, thanks to the use of a charging module powered by an integrated photovoltaic (PV) panel with automatic battery charging during the day. The first test measured the output frequency using an oscilloscope to verify that the oscillator circuit produced waves at the specified frequency. The second test measured the device's effectiveness by examining the pest response to the device at various distances. This test was effective within a maximum radius of approximately 14 m from the center point, covering a rice field area of ​​250 m2.

PAMTIRTA Tempino is an institution that provides clean water services in the Tempino area. The process of recording water use and monitoring water turbidity is still done manually, making it prone to recording errors and making it difficult to monitor the water quality distributed to the community. This study aims to design a website-based water turbidity recording and monitoring system by focusing on User Interface (UI) and User Experience (UX) aspects using the Design Thinking method. The research follows five stages of Design Thinking: empathize, define, ideate, prototype, and test. Data collection involves observation and in-depth interviews with PAMTIRTA officers. The results include a design with key features such as digital water meter recording, turbidity monitoring dashboards, and complaint services. The prototype was tested using Maze and the System Usability Scale (SUS), achieving a score of 80.1 and falling into the "Good" category (grade B). These results demonstrate that the UI/UX design effectively provides an easy-to-understand, operationally suitable, and efficient solution for PAMTIRTA Tempino's water recording and turbidity monitoring needs. This design offers a ready-to-implement solution to improve the efficiency, accuracy, and quality of clean water services in the Tempino area.  

The growing demand for energy-efficient and intelligent thermal systems has driven significant advancements in adaptive compressor design. This paper presents a comprehensive literature review on the development of AI-based compressor systems, with a specific focus on enhancing efficiency under partial-load conditions and optimizing the utilization of residual energy. Through the synthesis of five recent high-impact studies (2020–2025), we examine the application of deep reinforcement learning (DRL), hybrid evolutionary algorithms, and neural network surrogate modeling in compressor optimization. Key findings indicate that model-based DRL combined with surrogate CFD can achieve up to 8% efficiency gains at off-design conditions. Hybrid approaches integrating Genetic Algorithms (GA) with DRL reduce optimization time by 30% while improving pressure ratios. Neural network surrogates provide high-speed, real-time performance predictions with less than 1% error, enabling mass iterative design. Furthermore, intelligent load classification using radial basis function networks (RBFN) allows adaptive response to varying operating conditions with over 95% accuracy. Collectively, these methods form a framework for intelligent, self-optimizing compressor systems capable of real-time adaptation and energy recovery. The results suggest that AI-enhanced adaptive compressors represent a transformative direction for energy-sensitive sectors, including HVAC, power generation, and sustainable industry.

This study examines the relationship between market efficiency and digital financial innovation in the context of global financial transformation over the past decade, when fintech, cryptocurrency, and Decentralized Finance (DeFi) have significantly altered price formation and information dissemination mechanisms. The main issue raised is whether the Efficient Market Hypothesis (EMH) theory remains relevant in the face of digital market dynamics characterized by high volatility, speculative behavior, and regulatory uncertainty. The objective of this study is to assess the impact of digital innovation on information efficiency, price transparency, and the stability of modern financial markets. The study used the Systematic Literature Review (SLR) method, examining 15 scientific articles published between 2015 and 2025 from various academic databases. The findings indicate that digital technology increases access and speed of information distribution, but does not always result in consistently efficient markets. Crypto and DeFi markets have been shown to exhibit fluctuating efficiency due to price anomalies, information asymmetry, and weak regulation. Overall, the literature synthesis confirms that market efficiency in the digital era is dynamic and influenced by the interaction between technology, investor behavior, and governance quality. This study concludes that the EMH remains relevant as a basic framework, but needs reinterpretation to suit the complex and rapidly changing characteristics of digital markets.

PT Mandiri Banana Indonesia still requires further development and arrangement of its layout and facilities to enhance the efficient and optimal use of land. This study aims to determine whether the existing layout at PT Mandiri Banana Indonesia is effective in supporting operational flow and the uninterrupted progression of the production process. The research adopts a quantitative descriptive approach, which is used to measure the correlation between activities through the Activity Relationship Chart (ARC) and Total Closeness Rating (TCR) methods. These methods enable the analysis to be presented in a measurable and objective manner as a basis for improving the facility layout. This study proposes a new layout design for the main production area and supporting facilities. The proposed layout is based on the results of ARC and TCR analyses, which indicate several critical spatial interrelationships among the facilities. Based on the findings and discussion regarding the application of the Activity Relationship Chart (ARC) and Total Closeness Rating (TCR) in designing the facility layout at PT Mandiri Banana Indonesia, it may be inferred that layout improvement exerts a significant impact on enhancing workflow efficiency and the overall work environment. The proposed alternative layout is expected to achieve a safer, more hygienic, and more productive spatial arrangement by establishing a clear separation between the production zone, administrative zone, and supporting areas.

This study aims to analyze multiple-choice questions related to phase e elements, namely the Principles and Basic Concepts of Accounting and Basic Banking, using the Anates program. The study involved 20 participants, consisting of college students and vocational high school students. The Anates application program serves as a tool to process academic evaluation data accurately and efficiently. The method used in this study is quantitative and is described descriptively, resulting in analytical findings including weighted data scores, test reliability, discriminative power, difficulty level, validity level, and distractor quality. The results from the Anates program make it easier for teachers to evaluate the quality of learning and develop more focused, innovative, and student-centered teaching plans. This research is expected to provide benefits in improving students' accounting knowledge, and teachers are expected to be able to adjust their teaching methods and strategies dynamically, thereby supporting the optimal improvement of students' learning outcomes in this digital era.

Zinc (Zn) is one of the most common heavy metal contaminants found in industrial wastewater and solid residues such as slag, electroplating waste, and metal ash. At excessive concentrations, Zn can cause environmental disturbances, including toxicity to aquatic organisms, disruption of microbial activity, and groundwater contamination. Long-term exposure may also lead to bioaccumulation and potential health risks to humans. This article presents a comprehensive literature review that discusses the chemical properties of Zn, its environmental behavior, and the development of recent treatment technologies within the last five years. Several techniques, including adsorption using modified or composite materials, biosorption utilizing microalgae and agricultural biomass, as well as solidification–stabilization with amendment agents such as biochar or iron-sulfide compounds, are evaluated and compared. The literature indicates that no single treatment method is universally effective for all waste types; therefore, hybrid or integrated treatment systems are considered more efficient and sustainable. Based on the reviewed evidence, this study proposes an engineering concept that emphasizes environmental safety, cost-effectiveness, and industrial applicability.