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Agrivoltaics in Japan: A Review of Current Practices, Challenges, and Future Directions
Full text linkThis review examines agrivoltaics in Japan integrating solar photovoltaic (PV) systems with agricultural production as a dual-use land strategy to address constrained arable land, decarbonization goals, and energy security. Using a thematic synthesis of published studies and documented Japanese cases, the paper maps current deployment practices, reported agronomic and energy outcomes, and the main constraints shaping adoption. The literature indicates that well-designed agrivoltaic configurations can maintain crop production while adding renewable electricity generation, with outcomes strongly influenced by site conditions, crop type, shading design, and farm management. Evidence also points to potential co-benefits such as reduced heat stress and improved microclimate stability, but trade-offs may emerge for light-sensitive crops or under suboptimal PV spacing and height. Key barriers in Japan include high upfront investment, complex permitting and compliance requirements, and concerns over land-use integrity and long-term agricultural continuity. Future research should prioritize longitudinal field data on crop yield and quality, soil and water dynamics, and ecosystem effects, alongside standardized performance metrics and policy/financing mechanisms that align farmer incentives with grid and climate objectives
Application of the TAM model for assesing the acceptance of IoT technology in a residential security application
Full text linkResidential crime continues to be a significant concern, and traditional security systems relying oHousing is an area vulnerable to crime, especially if it is not supported by an adequate security system. Many housing complexes still rely on conventional security systems that only involve officers without technological support. Therefore, the application of technology, especially the Internet of Things (IoT), is needed to improve housing security systems. The success of a system is largely determined by the level of user acceptance, which can be measured using the Technology Acceptance Model (TAM). This study aims to measure user acceptance of an IoT-based housing security system using the TAM model. Data were obtained from 100 respondents and analyzed using the PLS-SEM method to test the research hypotheses. The results showed that four hypotheses had a significant relationship, namely the relationship between Subjective Norm (SN) and Perceived Usefulness (POU), Perceived Ease of Use (PEU) and POU, PEU and Attitude Toward Use (ATU), and POU and Behavioral Intention (BEI). Meanwhile, the other four hypotheses did not show a significant relationship.n manual monitoring are often insufficient in addressing modern security challenges. With the rapid development of Internet of Things (IoT) technology, digital security solutions offer new opportunities for improving surveillance and access control within housing environments. This study aims to assess user acceptance of an IoT-based residential security application by applying the Technology Acceptance Model (TAM). A quantitative survey method was used, involving 100 respondents who evaluated the prototype after testing it directly. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that Perceived Ease of Use significantly affects both Perceived Usefulness and Attitude Toward Using, while Perceived Usefulness strongly influences Behavioral Intention. However, Attitude Toward Using shows a marginal impact on Behavioral Intention, and Behavioral Intention does not significantly predict Actual Use. These findings reveal the dominant factors influencing acceptance and highlight areas for improvement in IoT-based security applications
Integrating Renewable Energy Solutions through Collaborative Research: A Pathway to Sustainable Urban Development
Full text linkThe rapid pace of global urbanization has intensified the demand for sustainable energy solutions, prompting the integration of renewable energy into urban development plans. This paper explores the critical role of collaborative research in overcoming the challenges associated with transitioning to renewable energy in urban areas, focusing on the contributions of bibliometric analysis. This study uses a bibliometric approach to analyze global research trends, key publications, and influential authors in renewable energy and urban planning. The analysis highlights the growing body of literature, revealing significant clusters of research activity centered around sustainable development, urban planning, and renewable energy technologies. Case studies of cities like Freiburg and San Francisco, which have successfully integrated renewable energy technologies, demonstrate how collaborative approaches can lead to more resilient and sustainable urban environments. Despite significant progress, challenges such as infrastructural investments, retrofitting complexities, and regulatory hurdles remain. The paper emphasizes the importance of continued collaborative research to address these barriers and ensure that the benefits of renewable energy are equitably distributed among urban populations. In addition, the study underscores the necessity of interdisciplinary collaboration and public engagement in fostering broader adoption and support of renewable energy technologies. The findings advocate for strengthened partnerships and continued innovation, supported by bibliometric insights, to facilitate the global transition towards sustainable urban energy systems. This research provides a comprehensive understanding of the evolving landscape of renewable energy research and its critical role in shaping sustainable urban development
Account receivable strategy in handling receivables’ goods at the aruss hotel semarang
No full textHotel Aruss Semarang has various payment methods that can be used by its customers. The account receivable is responsible for all transactions that are not in the form of cash. After the event ends, the accounts receivable section will send an invoice that must be paid before the due date. However, there are invoices that are past due so they require an account receivable role. The Problem of the research is what strategies are carried out by the accounts receivable section in handling receivables and the obstacles faced in the receivables collection process. Qualitative descriptive methods were used to obtain reliable data, researchers carried out observation, interviews and documentation activities. The results of this research are that the accounts receivable section has a strategy such as reminding receivables that are past due three times, if they are still not paid, the accounts receivable section together with the relevant sales person will make a visit. If payment has not been made in accordance with hotel policy, the relevant sales party will pay the receivable. The obstacles experienced by the accounts receivable section in the receivables collection process are internal obstacles and external obstacles
Improved human image density detection with comparison of YOLOv8 depth level architecture and drop-out implementation
Full text linkEnergy inefficiency due to Air Conditioners (AC) running in empty rooms contribute to unnecessary energy consumption and increased CO₂ emissions. This study explores how different depth levels of the YOLOv8 architecture and dropout regularization can enhance human density detection for smarter AC control systems. By evaluating model accuracy through Mean Average Precision (mAP50-95), we provide quantitative insights into how these modifications improve detection performance. Our dataset consists of 1363 images taken in an office environment at ITERA under varying lighting conditions and different human presence densities. The results show that the YOLOv8m model performs best, achieving an mAP50-95 score of 0.814 in training and 0.813 in validation, outperforming other YOLOv8 variants. Furthermore, applying dropout regularization improves model generalization, increasing mAP50-95 from 0.552 to 0.6 and effectively reducing overfitting. This study highlights the balance between architectural depth and dropout regularization in YOLOv8, demonstrating its effectiveness in energy-efficient smart buildings. The findings support the potential of deep learning-based human density detection in improving energy conservation strategies, making it a valuable solution for intelligent automation systems
Comparison of clustering analysis of K-means, K-medoids, and fuzzy C-means methods: case study of school accreditation in west java
Full text linkThis research aims to analyze school accreditation data in West Java using clustering methods: K-Means, K-Medoids, and Fuzzy C-Means, to identify patterns and groups of schools based on similar characteristics. K-Means, known for its simplicity, suggests an optimal two-cluster solution based on silhouette values but employs three clusters for detailed analysis. K-Medoids, noted for its robustness against outliers, achieves the best clustering with a lowest Davies-Bouldin Index (DBI) of 0.8 and the highest Silhouette Information (SI) value of 0.46. Fuzzy C-Means, which assigns membership degrees to each data point across clusters, performs reasonably well with a DBI of 0.87 and an SI value of 0.40, while K-Means shows the highest DBI of 0.9 and the lowest SI value of 0.39. The findings highlight K-Medoids as the superior method for clustering. Regions with lower educational quality, such as Bekasi and Cianjur regions, require priority interventions, whereas areas with better quality, like Bandung and Bekasi regions, can serve as models. Data-driven approaches, inter-regional collaboration, and continuous monitoring and evaluation are recommended to optimize educational policies and enhance overall educational quality in West Java
Classification of Student Grading Using Naïve Bayes Method with Under-sampling Approach to Handle Imbalance
Full text linkThis study explores the application of the Naive Bayes classification method to predict student grades based on important attributes such as timeliness of assignment submission, attendance rate, and quality of work. This research uses a dataset that includes three attributes, namely timeliness of submission, attendance level in learning, and evaluation of the quality of assignments collected by students. The pre-processing is performed to clean the data, followed by an under-sampling stage to balance the class distribution. Then, the classification model is evaluated and tested using specific data samples to measure prediction accuracy. The results showed a significant improvement in model accuracy after applying under-sampling, highlighting the importance of handling data imbalance in predictive analysis. The implications of these findings are not only relevant in the context of higher education, but also offer opportunities for further development in data-driven decision-making in various fields
Antioxidant and trombolitic activity of etanol extract and fractions of carica culver (Carica pubescens) in vitro
Full text linkThe prevalence of degenerative diseases arising from cell damage and free radicals continues to increase. The reactivity of oxidant compounds that exceed the limit can form a chain reaction capable of damaging parts of blood vessel cells that cause thrombolysis. Carica skin waste (Carica pubescens) has not been widely utilized, carica skin has the potential to have the ability as an antioxidant and thrombolytic. This study aims to determine the potential antioxidant and thrombolytic activity of carica peel. Carica peel samples were extracted by remaceration method using 70% ethanol and then fractionated with n-hexane, ethyl acetate, and water solvents. Antioxidant activity testing was carried out using the DPPH (2.2 Diphenyl-1-picrylhydrazyl) method and thrombolytic activity with the clot lysis method. The results showed that the ethyl acetate fraction had the highest antioxidant activity compared to ethanol extract and other fractions with an IC50 value of 37.04 ppm with an AAI value of 0.54. The thrombolytic activity test of ethyl acetate fraction is also the highest thrombolytic agent with clot lysis value reaching 46.06% close to the clot lysis value of nattokinase positive control of 52.39%
Implementing a Dual Payment Method for Mobile Charging Stations in Urban Areas
Full text linkThe mobile phone has become the communication device of choice for most people due to its practicality compared to other communication devices. A mobile phone requires electrical energy from its battery to function. Over time, the electrical energy stored in the battery depletes, and the phone needs to be recharged. A frequent problem arises when users are outside without a charger or have difficulty finding a safe power source. To address this issue, this final project proposes a solution: a secure mobile charging station that can be placed in public locations such as stations, hospitals, banks, hotels, malls, restaurants, and other venues. The paid mobile charging station consists of six charging lockers. Each locker is secured with a solenoid lock controlled by a Node MCU ESP32 microcontroller. Payment can be made using either coins or an RFID card. To charge their mobile phones, users must insert coins or swipe an RFID card. Once payment is made, users can charge their phones in the provided lockers for a specified period. System testing demonstrates high reliability, with a 100% success rate in coin validation, accurate RFID balance deductions, and seamless data transmission between the ESP32 and the database. The results indicate that the proposed system is a cost-effective, scalable, and secure solution for providing public mobile charging services in locations such as malls, hospitals, and transportation hubs
Implementation of Retrieval-Augmented Generation (RAG) and Large Language Models (LLM) for a Document and Tabular-Based Chatbot System
Full text linkThe challenge of accessing information from disparate sources—unstructured documents and structured tabular data—hinders efficiency in enterprise information systems. This study addresses this challenge by presenting the design, implementation, and validation of a unified chatbot system powered by Retrieval-Augmented Generation (RAG) and Large Language Models (LLM). For unstructured documents, the system implements a RAG pipeline utilizing ChromaDB for vector indexing and OpenAI embeddings. Meanwhile, for structured data, it leverages a Text-to-SQL engine to translate natural language queries into SQL commands, with results visualized via QuickChart. The architecture is built upon a modular FastAPI backend with role-based access control and was rigorously validated through blackbox functional testing. Results demonstrate 100% functional success across all endpoints, confirming the architecture's reliability. This study confirms the viability of a unified RAG and Text-to-SQL architecture, offering a practical blueprint for creating more intelligent and integrated data interaction systems in enterprise environments