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A Decision Support System is a system designed with the capability to solve problems, particularly in decision-making. One example is making decisions regarding the recruitment of prospective employees or crew members for a company. In this research case study, the Decision Support System will be implemented to assist in recruiting crew members for Wedding Organizer Rahwana. A Wedding Organizer is a service-oriented business that plans and manages all aspects of a wedding event. In this case, Rahwana WO is still facing difficulties in selecting potential crew members. The implemented Decision Support System will use the MOORA method, a multi-objective optimization technique that has been successfully applied to solving various complex decision-making problems. The system’s performance was evaluated using the SUS method, and the implementation of this approach for Rahwana WO’s crew selection received a good rating, categorized as GOOD, with a score of 78.6.

This study aims to assess the optimization of organic fertilizer in plant growth kale (Ipomea sp.) using a hydroponic system. The research method used is a complete random design of 2 factorial. The first factor is the concentration of POC which consists of 3 levels of treatment, namely K1 = 100 ml POC + 1 L of water. K2 = 200 ml of POC + 2 L of water. K3= 300 ml of POC + 3 L of water. The second factor is the frequency of administration of POC (W) which consists of 3 levels of treatment W1 = every 2 days W2 = every 4 days, W3 = every 6 days. So there are 9 combinations, namely: K1W1, K1W2, K1W3, K2W1, K2W2, K2W3, K3W1, K3W2, K3W3 between treatments with repeated 3 times so that a total of 27 trials. The measured parameters include temperature, plant height, number of leaves and Root Length. The results showed that the interaction occurred at the final temperature, plant height: 21 HST 28 HST, number of leaves: 7 HST, 14 HST ,21 HST and 28 HST and Root Length. The frequency of POC using banana stems with a time of 2 days was able to increase, plant height 21 HST, 28 HST, number of leaves 7 HST,14 HST,21 HST,28 HST, Root Length, while at a frequency of 4 days was able to increase the growth of kale plants in the late afternoon temperature parameters. At a frequency treatment once every 6 days is able to increase the final temperature. Concentration of POC using banana stem with a dose of 100 ml POC +1 L of Water, able to increase plant height 21 HST and 28 HST, the number of leaves 7 HST, 14 HST,21 HST,28 HST, Root Length. This research is expected to contribute to sustainable agricultural practices and the development of efficient hydroponic systems.

The optimization of community empowerment is a strategic step in improving the quality of human resources (HR) in Kedungsari Village. This community service aims to implement literacy-based education as a means of empowering the community by utilizing existing village facilities and infrastructure. Through a participatory approach, the community is involved in various socialization and training activities relevant to local needs to enhance their skills, awareness, and knowledge. The results of the program indicate that literacy-based education not only improves reading and writing skills but also strengthens the community’s understanding of the importance of maintaining environmental cleanliness, social values, and managing local potentials to support village development. The utilization of facilities such as the village library and community hall is key to the program's success. With a targeted and collaborative implementation, this program is expected to enhance the quality of life of Kedungsari Village residents in social, economic, and environmental aspects. In conclusion, sustained support from universities, village governments, and local communities is necessary to create an inclusive, competitive, and sustainable empowerment ecosystem.

. Ekspor memainkan peran penting dalam pertumbuhan ekonomi, memberikan kontribusi terhadap devisa negara dan meningkatkan daya saing di pasar internasional. Menentukan produk ekspor unggulan menjadi tantangan karena harus mempertimbangkan berbagai faktor, seperti nilai ekonomi, permintaan pasar, stabilitas harga, dan keberlanjutan produksi. Keputusan yang kurang tepat dapat berdampak pada efisiensi, daya saing produk lokal, dan bahkan ketergantungan pada pasar global. Oleh karena itu, dibutuhkan pendekatan yang lebih sistematis dan berbasis data untuk membuat keputusan yang tepat dalam pemilihan produk ekspor unggulan. Penggunaan sistem pendukung keputusan (SPK) dapat membantu mempermudah proses ini dengan menyediakan analisis berbasis data yang lebih objektif. Metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) dapat digunakan untuk menentukan bobot kriteria, membantu pengambil keputusan untuk menilai seberapa penting setiap faktor yang terlibat. Sementara itu, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) digunakan untuk memilih alternatif terbaik berdasarkan kedekatannya dengan solusi ideal yang diinginkan. Pendekatan ini bertujuan untuk memberikan rekomendasi yang lebih objektif dan akurat, mendukung pengambil kebijakan serta pelaku usaha dalam menetapkan strategi ekspor yang optimal. Dengan cara ini, daya saing produk lokal dapat ditingkatkan secara berkelanjutan, memastikan pertumbuhan ekonomi yang lebih stabil dan meningkatkan posisi negara di pasar global  

Termination of Employment (TOE) is the termination of an employment relationship due to a matter that results in the expiration of rights and obligations between workers/laborers and employers regulated in Law No. 13 of 2003 concerning employment and secondary legal materials obtained from employment, internet papers and others. The implementation of termination of employment must be in accordance with Law No. 13 of 2003 concerning employment that termination of employment is carried out in several processes, namely deliberation between employees and the Company, if there is an impasse, the last resort is carried out through the court to decide the case. Therefore, the strength of the implementation of labor law is needed through increased supervision, education for the parties, and optimization of the role of labor dispute resolution institutions.

This study aims to optimize the architecture of Artificial Neural Networks (ANN) for rainfall prediction using meteorological data from Indonesia, which is known for its complex and highly variable climate patterns. Climatic variables such as temperature, humidity, air pressure, wind speed, and historical rainfall records serve as the main input features to evaluate the performance of various network configurations. Optimization is carried out by determining the appropriate number of neurons, hidden layers, activation functions, and training algorithms to enhance prediction accuracy. Model evaluation employs indicators such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to ensure output stability. The findings indicate that a multilayer architecture combined with optimized parameters significantly improves the network’s ability to capture non-linear patterns in Indonesia’s tropical rainfall data. The optimized model produces more stable and accurate predictions compared to standard configurations. These results highlight the importance of ANN architecture optimization in supporting early warning systems, agricultural planning, water resource management, and hydrometeorological disaster mitigation across Indonesia.

Optimizing human resources (HR) in digital marketing through the synergy between creativity and technology is an important strategy to create effective and innovative marketing campaigns in the digital era. Creativity plays a crucial role in producing engaging and relevant content, which is able to build an emotional connection with consumers as well as strengthen the brand identity. Meanwhile, technology allows for personalization, data-driven analysis, and efficiency in the implementation of marketing strategies. This study examines how the combination of human resource creativity and the use of digital technology can create a competitive advantage for companies. The results of the study show that companies that successfully integrate these two elements can increase consumer engagement, customer loyalty, as well as the effectiveness of marketing campaigns. From a conceptual perspective, this approach is changing the way companies view innovation and cross-functional collaboration within organizations, which in turn improves responsiveness and adaptability to market changes. The practical implications of this research are the importance of investing in the development of creative and skilled human resources in technology, to create a sustainable digital marketing strategy and be able to compete in an increasingly competitive global market.

The increasing competition among higher education institutions necessitates effective marketing strategies to build brand awareness. Digital marketing has become a crucial tool in achieving this goal. This study aims to analyze the role of digital marketing in enhancing brand awareness in higher education institutions. The research method employed is a literature review, gathering and analyzing various relevant sources, including scientific journals, books, and previous research reports. The results indicate that implementing digital marketing strategies, such as the use of social media, content marketing, and search engine optimization (SEO), significantly contributes to the enhancement of brand awareness in higher education institutions. Moreover, active interaction and relevant content on digital platforms can increase audience engagement and loyalty. The implications of this study highlight the importance for higher education institutions to integrate digital marketing into their communication strategies to expand reach and strengthen brand image in the public eye.

The application of algorithms in the mentoring activity scheduling system is an innovative solution to overcome the complexity of time and resource management. This study aims to develop a mentoring activity scheduling system for Reading and Writing the Qur'an (BTQ) using the particle swarm optimization (PSO) algorithm. The PSO algorithm was chosen because of its ability to find optimal solutions efficiently through a particle population approach. This system is designed to meet the schedule preferences of students and supervisors, taking into account the limited time available and the interrelationships between schedules. This study makes a significant contribution to improving the efficiency of BTQ mentoring activity scheduling, as well as demonstrating the potential of PSO in solving other scheduling problems.

The optimization of thread management in operating systems is essential to improve the performance of multithreading-based applications. This study analyzes various techniques to enhance thread management efficiency, including thread pooling, dynamic scheduling, load balancing, and synchronization contention reduction. Experimental results indicate that thread pooling reduces overhead by reusing threads, while dynamic scheduling improves multitasking responsiveness by prioritizing urgent tasks. Load balancing ensures equitable workload distribution across processor cores, minimizing execution time. Furthermore, reducing synchronization contention using mechanisms like semaphores decreases thread waiting time, thereby boosting application performance. These optimizations enable modern operating systems to efficiently utilize multi-core architectures, improving speed, responsiveness, and user experience. This research highlights the importance of thread management optimization as a foundational step towards developing adaptive and scalable operating systems for future complex and data-intensive applications.

This research aims to optimize the operating conditions of nickel-chrome electroplating to increase the corrosion resistance of L profile steel materials. Nickel-chrome electroplating is a metal coating method used to protect steel surfaces from corrosion and improve the aesthetic appearance. The electroplating process involves several variables such as solution concentration, temperature, current density, and plating time. In this research, various operating conditions were optimized to obtain maximum corrosion resistance. The experimental method was carried out with variations of these parameters and the results were evaluated using corrosion tests and microscopic analysis. The research results show that certain operating conditions produce a protective layer that is more effective in preventing corrosion on L profile steel. This optimization is expected to make a significant contribution in industrial applications that require high corrosion protection.

This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.

This research is a Systematic Literature Review (SLR) aimed at analyzing the application of Artificial Intelligence (AI) technology in the management of information technology (IT) projects. This study focuses on identifying the AI technologies employed, the benefits gained, and the challenges faced in implementing these technologies. The study gathers and analyzes literature from various leading databases, including Scopus, IEEE Xplore, and SpringerLink, within the timeframe of 2015–2025. The findings reveal that AI technologies such as machine learning, predictive analytics, and natural language processing play a significant role in improving efficiency, reducing risks, and supporting decision-making in IT project management. However, challenges such as data quality, organizational resistance, and implementation costs remain major obstacles in adopting this technology. This review provides comprehensive insights into trends, benefits, and barriers associated with AI utilization, along with recommendations for more effective implementation in the future.

This study addresses the legal responsibility of carriers for the loss or damage of goods during road transportation, as stipulated in Law Number 22 of 2009 concerning Traffic and Road Transportation (UU LLAJ). Article 193 of UU LLAJ obliges carriers to provide compensation for any loss incurred during transportation, except in cases of force majeure or third-party negligence. The study also explores the application of breach of contract (wanprestasi) and strict liability principles as the legal foundation for carrier accountability. However, the practical implementation of these regulations often faces challenges, including unclear contractual clauses on liability, difficulties in proving carrier negligence, and slow or inefficient dispute resolution mechanisms. To address these issues, the study proposes strategic measures such as regulatory revisions to clarify carrier liability boundaries, the establishment of standardized transportation contracts, enhanced legal education for stakeholders, strengthened non-litigation mechanisms like mediation and the Consumer Dispute Resolution Agency (BPSK), and the optimization of insurance to mitigate risks. These recommendations aim to foster a more efficient, reliable, and legally protective transportation system for all stakeholders involved.

School social workers have a strategic role in supporting the welfare of students in Indonesia, especially in dealing with various social, emotional, and psychological issues that can affect academic performance. As a liaison between students, families, schools, and the community, school social workers play a role in providing interventions that aim to create an inclusive, safe, and supportive learning environment. However, the implementation of this role still faces various challenges, such as the limited number of professionals, the lack of support for facilities in schools, and social stigma against the social worker profession. This condition hinders the optimization of student welfare services as a whole. This article aims to explore the role of school social workers in the context of Indonesian education, identify the challenges faced, and examine opportunities for role strengthening through holistic intervention approaches and policy support. The results of the study show that increasing the effectiveness of school social workers can be achieved through the integration of student welfare programs into the curriculum, ongoing training for social workers, and synergy between the government, schools, and local communities. Strong policy support and adequate resource allocation are decisive factors in strengthening the function of school social workers. With the right approach, school social workers have the potential to be agents of change in creating an education system that is more responsive to students' social and emotional needs, as well as encouraging the creation of a healthy and sustainable learning climate.

The rapid development of multimedia technology has significantly advanced 3D animation techniques, enabling the production of high-quality visual content across industries such as film, gaming, architecture, and product visualization. Rendering, as the final stage of the 3D production pipeline, plays a crucial role in determining both visual realism and production efficiency. This study compares the performance of three rendering engines—Eevee, Cycles, and Radeon ProRender—by evaluating rendering speed, visual quality, and memory efficiency in Blender. The objective is to provide practical insights for designers and digital content creators in selecting the most suitable rendering engine based on project requirements. In this research, three identical 3D scenes were rendered using each of the three rendering engines under controlled experimental conditions. The comparison was conducted based on several parameters, including rendering time, output file size, shadow accuracy, lighting effects, and overall visual realism. Quantitative measurements were used to evaluate render speed and memory consumption, while qualitative analysis assessed differences in shadow detail, global illumination behavior, reflection accuracy, and material realism. The results indicate that Eevee outperforms the other engines in terms of rendering speed, making it highly suitable for real-time applications and projects requiring fast previews. Cycles produces the highest level of visual realism due to its physically based path-tracing algorithm, although it requires longer rendering time and higher computational resources. Meanwhile, Radeon ProRender demonstrates competitive performance, particularly in shadow quality and lighting effects, offering a balanced alternative between realism and efficiency. Based on the findings, Blender remains a flexible and effective platform. The choice of rendering engine should depend on whether speed, graphic quality, or memory optimization is prioritized.

This study examines the optimization of Ramadan fasting education through worship simulation to instill patience values in third-grade students at SDN 04 Birugo, Bukittinggi. Using a qualitative descriptive approach, data were collected via classroom observations, interviews with Islamic education teachers, and documentation of learning activities. The findings reveal that worship simulations, designed to mimic real fasting practices, significantly enhanced students' enthusiasm, understanding of Ramadan’s spiritual significance, and behavioral changes reflecting patience. Students demonstrated improved self-control, discipline, and social cooperation during and after the simulations. Teachers reported that experiential learning through simulations facilitated deeper engagement compared to conventional methods, enabling students to internalize abstract values through concrete experiences. Key recommendations include integrating simulations into Islamic education curricula, providing teacher training, and fostering parental involvement to reinforce learned values. This study highlights the effectiveness of simulation-based learning in fostering holistic religious and moral development, advocating its adoption as an innovative pedagogical tool in primary Islamic education.

This article discusses efforts to optimize the production process at the motorcycle carburetor company "Mantri Karbu" through the application of transformation methods. In the automotive industry, production efficiency is key to enhancing competitiveness and meeting the ever-growing market demand. This study identifies various challenges faced by Mantri Karbu, including long production times, resource waste, and inconsistent product quality. By implementing transformation methods, which include workflow analysis, waste reduction, and employee skill enhancement, the company can improve productivity and operational efficiency. The results of this implementation show a significant increase in production cycle time, a reduction in operational costs, and an improvement in customer satisfaction. This article aims to provide insights for other companies in similar industries to adopt effective strategies for optimizing their production processes.

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.

This study aims to enhance the competitiveness and market reach of Micro, Small, and Medium Enterprises (MSMEs) in Kedungjambe Village through the optimization of social media marketing and e-commerce platforms. The primary challenge faced by MSMEs is limited consumer visibility due to ineffective promotional and marketing strategies. The proposed solution includes training on digital marketing strategies utilizing social media platforms like WhatsApp Business for interactive communication with customers and leveraging the features of the Shopee e-commerce platform to expand market reach. MSME participants were trained to create engaging promotional content, utilize WhatsApp Business broadcast features to foster customer engagement, and effectively manage online stores on Shopee, including crafting informative product descriptions, managing inventory, and analyzing sales performance. A simulation of digital marketing through Shopee was conducted to provide hands-on experience for the business owners. The results of the program demonstrated a significant improvement in MSME competitiveness, brand awareness, customer engagement through social media, and sales volume on Shopee.