Publication Search

his study examines the business development of seaweed in Indonesia during the period of 2019-2024 through a comprehensive literature review. Findings indicate substantial growth in the seaweed industry during this period, with promising increases in production and exports contributing significantly to the national economy. However, challenges such as production issues, global competition, and regulatory obstacles are also identified. Strategic measures are needed to optimize the potential of this industry, including improving production quality, market expansion, government collaboration, and human resource development. Close cooperation with relevant stakeholders, including the government and academic institutions, can strengthen the seaweed industry. Supportive policies, investment in research and development, and training programs to enhance workforce skills are key to overcoming the challenges faced by this sector. The findings of this study are expected to provide valuable insights for stakeholders in the seaweed industry and make a meaningful contribution to academic literature on this topic. With the right steps, the seaweed industry in Indonesia has the potential to continue growing sustainably and significantly benefit economic growth and societal welfare.

In this era of globalization, it is crucial to optimize state revenue through zakat and taxes based on the principles of maqâsid al-syarî’ah. This article presents challenges and opportunities in optimizing both instruments in Indonesia. Through an analysis of state income and zakat potential, a literature review method is employed to formulate an optimization model. The model encompasses increasing public awareness, institutional strengthening, developing supportive regulations, enhancing synergy, and building public trust. The implementation of this model requires commitment from all parties and must be based on the principles of maqâsid al-syarî’ah. Thus, it is hoped that state revenue through zakat and taxes can be optimized to achieve the fair and prosperous goals of national development.

This research aims to investigate theoretical and practical approaches in optimizing the potential of human resources and employees. The research method used is a literature review with a qualitative approach to explore the conceptual framework supporting effective HR management practices. The literature review includes an in-depth review of relevant academic literature, covering key concepts in HR management such as motivation, leadership, career development, and performance measurement. Critical analysis of various theories and models helps in understanding the complexity and dynamics of HR management. The findings of this literature review indicate that theoretical approaches provide a solid conceptual foundation for understanding the basic principles in HR management, while practical approaches enable the implementation of strategies appropriate to the organizational context. The integration of theory and practice is key to developing effective and sustainable HR management strategies. The findings of this literature review provide valuable insights for practitioners in designing and implementing successful HR management practices. By understanding the theoretical framework and applying it contextually, organizations can optimize the potential of their human resources and employees, thereby enhancing organizational performance and competitiveness in the dynamic global market.

This research investigates the prevalent challenges and identifies strategic approaches for effective resource utilization in secondary schools. Through a mixed-methods study involving quantitative analysis and qualitative interviews, the research highlights significant problems such as budget constraints, outdated educational materials, and disparities in resource distribution between urban and rural schools. The study reveals that 75% of surveyed schools encounter difficulties in resource allocation, with urban schools having better access to technology and trained personnel compared to their rural counterparts. Qualitative findings underscore the importance of several strategies to optimize resource usage. Needs-based allocation is identified as a critical approach, with schools that tailor resources to specific needs reporting improvements in student engagement and achievement. The integration of technology, combined with comprehensive teacher training on digital tools, is pivotal in enhancing learning experiences. Moreover, collaboration with the community and local businesses emerges as a valuable source of additional resources and real-world learning opportunities. The establishment of resource-sharing networks among schools is also noted as an effective measure to mitigate the impact of budget constraints and promote a more equitable distribution of materials and expertise. This study contributes to the discourse on educational equity and efficiency, offering actionable insights for policymakers, educators, and administrators. By emphasizing the need for localized solutions, technological equity, and community engagement, the research points towards a more equitable and efficient educational landscape. Future research directions include exploring the longitudinal impacts of these strategies on educational outcomes and the role of policy reforms in supporting resource optimization efforts.    

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.

To find out and analyze the use of forensic hypnosis as a method of examining witnesses in proving criminal acts in other countries. To find out and analyze the use of forensic hypnosis as a method of examining witnesses in the perspective of proof of criminal acts at the investigative level in IndonesiaThe type of research used is normative juridical. The focus of the normative approach is to find, analyze and then formulate norms for the use of hypnosis. While the focus of the juridical approach is to explain the use of forensic hypnosis in several countries by referring to legal concepts or expert opinions, legal principles or principles and legal sources that apply universally.Whereas forensic hypnosis has been used as a method of examining witnesses in the investigative process in the United States, Canada and Hong Kong where information from the results of forensic hypnosis is used by investigators to search for, collect evidence and strengthen evidence. Meanwhile, de jure court decisions in each country that recognize the use of forensic hypnosis is a source of jurisprudence and legal legitimacy for applying forensic hypnosis as a method of examining witnesses in proving criminal offenses at the investigative level in Indonesia. Whereas the use of forensic hypnosis as a method of examining witnesses in the perspective of proving a crime at the level of investigation in Indonesia is no different from the method of examining witnesses in the investigation process in general. As a complementary method, it consists of 4 (four) stages namely pre-induction, induction, suggestion and termination where implicitly the use of forensic hypnosis as a method of examining witnesses in the investigation process is a manifestation of the investigator's enthusiasm in carrying out other actions according to law.In order to further strengthen and optimize the existence of the use of forensic hypnosis as a method of examining witnesses in the investigative process in Indonesia, it is hoped that the legislature will provide confirmation on the use of forensic hypnosis which must be contained in statutory regulations or in standard procedures.

The Allisa Cake business was founded by Mrs. Nora in 2016, a housewife located in Krapyak, West Semarang, Semarang City, Central Java. The products produced are cakes or bread which are marketed offline and online. However, online marketing has not met the expected targets, so appropriate promotional media is needed, in order to introduce the product to the public and to optimize promotional media. This research designs Instagram content that makes it easier to understand products and designs Instagram content that follows trends.The method used in this research is ADDIE developed by Dick and Carry, the development design with the ADDIE design aims to develop and validate the product. Meanwhile, the software used includes Corel Draw and Adobe Photoshop.Data collection was carried out by interviewing business owners, making observations and studying literature. After getting the data, create a product design and then apply it to the Instagram Feed design concept. The design stages are in the form of Thumbnail visualization, Tightissue and Final Design. The research produces Instagram content that contains information to make it easier to understand the product and the content design follows trends as the main promotional media.

This research aims to describe: 1) forms of teacher creativity in the Indonesian language learning process, and 2) inhibiting and supporting factors in efforts to increase teacher creativity at SMA Negeri 1 Sinjai Borong, Sinjai Regency. This type of research is descriptive qualitative. Data collection techniques include: observation, interviews, and documentation. Data analysis techniques by: data reduction, data presentation and verification. The research results found were that teacher creativity in the Indonesian language learning process was generally able to make learning plans, carry out learning using various methods, and carry out assessments appropriately. Inhibiting factors (limited time allocation, lack of supporting facilities and infrastructure, lack of student awareness of the importance of Indonesian language material, and lack of teacher awareness in understanding the meaning and creative learning models in the classroom learning process). Supporting factors (teacher's ability to optimize facilities and infrastructure well, and cooperation between teachers is quite good). Efforts made by teachers to increase creativity (using assignment methods, understanding the importance of creative teachers, carrying out internal training, and activating MGMP forums. It is hoped that the implications of the results of this research can become reference material for the development of further learning strategies (development of designs, methods and class design), so that it is not focused on one learning model, but is varied, and does not bore students in class.

Ecotourism in Baubau City has not developed optimally despite having great natural resource potential. Weak coordination between institutions is one of the obstacles to the implementation of policies related to ecotourism development. The purpose of this study was to analyze the effectiveness of the role of ecotourism development institutions in supporting ecotourism policies in Baubau City. This research uses qualitative methods with data collection techniques of observation and in-depth interviews with related agencies and local ecotourism actors. The results showed that coordination between ecotourism development institutions is still weak so that the implementation of ecotourism policies is not optimal. Recommendations include the need to improve cross-sectoral coordination and program synchronization, optimize the function of ecotourism institutions, and increase human resource capacity. This research is useful as an evaluation for policy makers related to ecotourism institutional reform in Baubau City.

The emergence of remote work as a prevalent mode of employment has brought about significant shifts in team dynamics within organizations. This qualitative analysis delves into the multifaceted effects of remote work on team interactions, communication, cohesion, and productivity. Through in-depth interviews and thematic analysis, this study explores the experiences and perceptions of team members and leaders regarding the challenges and opportunities presented by remote work. Findings reveal nuanced changes in collaboration patterns, the importance of technology in facilitating virtual teamwork, and the necessity for deliberate efforts to maintain social bonds and trust among team members. Furthermore, this research highlights the pivotal role of leadership in fostering an inclusive and supportive remote work environment. Understanding these dynamics is essential for organizations to adapt effectively to remote work paradigms and optimize team performance in the contemporary digital landscape.

In summary, the comprehensive analysis of students' responses to 11 distinct questions about the utilization of TikTok as a learning medium in English-speaking classes yields valuable insights. The unanimous agreement among the interviewed students underscores the overall advantage of TikTok as an effective and versatile learning tool. Firstly, TikTok proves to be a valuable supplementary resource, providing diverse content that aids in better understanding study materials. Students emphasize that the platform's content enhances their comprehension, offering additional learning material that is both engaging and accessible. Secondly, TikTok's interactive features, such as stitch videos and duets, emerge as pivotal components that inject an engaging and enjoyable dimension into the learning process. Students express a strong preference for these features, highlighting their positive impact on the learning experience. Thirdly, TikTok significantly contributes to bolstering students' confidence in speaking English. The students' responses emphasize that the platform acts as a transformative space where they overcome initial apprehensions about fluency. Through consistent speaking practice, students report feeling more motivated and self-assured when communicating in English. Additionally, the analysis reflects a positive correlation between TikTok usage and increased motivation to learn English. The platform's dynamic and interactive nature contributes to a more stimulating learning environment, encouraging students to actively engage with the language. Furthermore, students note that TikTok facilitates a deeper understanding of English materials. The visual and auditory elements in TikTok videos enhance their comprehension and retention of vocabulary and pronunciation. Despite these positive aspects, it's essential to acknowledge potential drawbacks. Some students express concerns about distractions on TikTok, which may divert their focus from the intended educational content. This highlights the need for effective time management and self-discipline when using TikTok as a learning tool. In conclusion, the data-driven analysis reveals a multifaceted perspective on the advantages and disadvantages of using TikTok as an English learning tool. While it significantly contributes to supplementary learning, interactivity, and confidence-building, students need to navigate potential distractions. Ultimately, TikTok emerges as a valuable and effective tool in language education, requiring a balanced approach to optimize its benefits for English learners.    

This study analyzes the transformation of economic education towards educational investment as a means of strengthening human resources (HR) through a bibliometric approach. By utilizing Scopus basic data and the VOSviewer analysis tool, the study examines the trends in publications over the past ten years, focusing on key areas such as educational economics, educational investment, and HR development. The findings highlight that innovations in educational economics, including the integration of digital technology, project-based learning, and collaborations between educational institutions, government, and industry, are crucial in enhancing the effectiveness of educational investments. Additionally, the study reveals that the proper allocation of educational funds for infrastructure development, improving teaching quality, and implementing skills-based training programs significantly contributes to increasing workforce competitiveness and promoting sustainable economic growth. The research emphasizes that educational investment strategies must be aligned with the needs of industry and global challenges to foster adaptive and innovative human resources. Moreover, it stresses the importance of designing educational policies that are comprehensive, addressing the aspirations of individuals and the relevance of education to the labor market. Ultimately, the study offers valuable academic insights and practical recommendations for policymakers, educational institutions, and other stakeholders. It also provides guidance for developing effective strategies that optimize educational investment and support the cultivation of superior HR capable of driving future economic progress.

This study investigates the efficacy of dynamic assignment strategies in enhancing English language proficiency, engagement, and motivation among learners compared to traditional assignment methods. Through a mixed-methods approach, involving quantitative analyses of language proficiency scores and engagement levels, as well as qualitative insights from student perspectives and teacher observations, the research highlights the significant advantages of integrating dynamic assignments in English language learning. Results indicate that students participating in the dynamic assignment group showed statistically significant improvements in overall language proficiency, particularly in speaking and writing skills (p < 0.05). Additionally, these students reported higher levels of cognitive, emotional, and behavioral engagement (p < 0.05), which were positively correlated with their language proficiency improvements (r = 0.62, p < 0.01). This suggests that the more engaged students are in their learning process, the better their language learning outcomes. From the qualitative data, students expressed a greater motivation and interest in learning English, attributing this to the real-world relevance and applicability of the dynamic assignments. Teachers corroborated these findings, noting enhanced active participation, collaboration, and improvements in students’ communication skills and confidence. However, both students and teachers identified challenges, including increased preparation time and the need for clearer guidelines to maximize the benefits of dynamic assignments. The study concludes that dynamic assignment strategies significantly contribute to improving language proficiency and engagement among English language learners. It suggests that addressing the identified challenges could further optimize the effectiveness of these strategies. This research advocates for a shift towards more interactive, relevant, and engaging teaching methodologies in language education to better prepare learners for real-world communication.

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.

This study presents the development of a smart grid system designed to efficiently integrate renewable energy sources into the existing electrical grid. The proposed system employs advanced communication technologies and real-time data analytics to optimize energy distribution and consumption. A simulation model was created to evaluate the system's performance under various scenarios, demonstrating significant improvements in energy efficiency and reliability. The findings indicate that the smart grid system can enhance the stability of the electrical network while promoting the use of sustainable energy sources.

This study explores the use of graphene-based nanofluids in enhancing the performance of solar-powered desalination systems. A laboratory-scale desalination system was developed to simulate the evaporation process, powered by solar energy, with the integration of graphene-based nanofluids to improve thermal efficiency. The experimental setup measured evaporation rates, energy consumption, and temperature profiles under varying solar radiation conditions (400–800 W/m²). Results revealed that the system with nanofluids demonstrated up to a 35% increase in evaporation rates compared to the baseline system without nanofluids, indicating enhanced heat transfer properties. Moreover, energy consumption was reduced by up to 20%, highlighting the improved energy efficiency of the system with nanofluids. The system with nanofluids exhibited higher temperatures in the evaporator, confirming more effective thermal utilization. Statistical analyses, including t-tests and regression analysis, confirmed the significant impact of nanofluids on both evaporation rates and energy consumption. This study demonstrates that graphene-based nanofluids offer a sustainable and energy-efficient solution for solar-powered desalination, particularly in areas with abundant solar radiation. The integration of nanofluids not only enhances the efficiency of the desalination process but also reduces operational costs, making it a promising alternative for addressing water scarcity in a sustainable manner. Further research is needed to optimize nanofluid formulations and assess their long-term feasibility for large-scale applications.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

The advent of 5G technology has marked a significant shift in the telecommunications industry, offering transformative improvements in service speed, latency, and network reliability. This study explores the impact of 5G on operational efficiency and service innovation in telecom companies. By examining the operational performance of three leading telecom companies that have implemented 5G networks, the research identifies key improvements in speed, cost reduction, and resource optimization. The findings highlight that 5G has enabled companies to achieve up to 100 times faster data transfer speeds compared to previous generations, drastically reducing latency and enhancing network reliability. These improvements contribute to increased customer satisfaction, faster response times, and reduced operational costs. Additionally, the integration of artificial intelligence (AI) for network management has optimized resource allocation and further enhanced the efficiency of telecom operations. The research also demonstrates how 5G has driven innovation in service offerings, such as enabling smart cities, IoT integrations, autonomous vehicles, and real-time patient monitoring in healthcare. While the deployment of 5G offers numerous benefits, the study acknowledges challenges such as high infrastructure costs, digital inequality, and regulatory hurdles. Telecom companies must invest significantly in infrastructure and navigate complex regulatory environments to fully realize the potential of 5G. The study concludes that 5G technology has the potential to reshape the telecom sector, fostering greater competitiveness, service quality, and innovation. Future research should focus on the long-term impact of 5G on customer loyalty, its expanded applications, and its role in advancing future technologies such as 6G.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

The rapid evolution of smart cities, driven by the integration of technologies such as the Internet of Things (IoT) and blockchain, has brought about significant advancements in urban infrastructure and services. However, these developments also introduce new cybersecurity challenges. Introduction: Smart cities are increasingly vulnerable to cyber threats due to the extensive use of interconnected devices and systems. A key security concern is the management of digital identities, which is essential for maintaining the integrity and reliability of city services. Literature Review: Traditional centralized identity management systems face significant security issues, including a single point of failure, data breaches, and limited user control over personal information. In contrast, decentralized solutions, particularly blockchain-based systems, offer enhanced security through their distributed nature, eliminating vulnerabilities associated with centralized models. Materials and Method: This research focuses on blockchain technology’s application in smart city identity management. A decentralized framework is proposed, leveraging cryptographic techniques, consensus mechanisms, and smart contracts to ensure data security, integrity, and privacy. Results and Discussion: The implementation of blockchain for identity management significantly improves attack tolerance, data integrity, and transparency. The decentralized approach mitigates the risks associated with central authorities, ensuring that user data remains secure and verifiable. However, scalability, interoperability, and regulatory compliance challenges remain. Blockchain solutions must be optimized for large-scale smart city applications and aligned with legal standards to achieve widespread adoption. Future research should focus on overcoming these challenges to create a more secure and resilient smart city infrastructure.