Pemerintah Indonesia secara berkelanjutan menjalankan berbagai program bantuan di sektor peternakan sebagai bagian dari upaya meningkatkan ketahanan pangan dan kesejahteraan peternak. Namun, implementasi program tersebut masih menghadapi sejumlah tantangan, seperti kurangnya integrasi data, ketidaktepatan sasaran, dan terbatasnya alat bantu pengambilan keputusan berbasis data. Penelitian ini bertujuan untuk mengintegrasikan data peternakan ke dalam sebuah Sistem Pendukung Keputusan (Decision Support System) yang dapat digunakan oleh pemerintah dalam perencanaan dan pelaksanaan program bantuan secara lebih efektif. Metode penelitian yang digunakan adalah Mixed Method dengan pendekatan Exploratory Sequential Design, yang dimulai dengan pengumpulan data kualitatif melalui wawancara dan studi kasus untuk menggali kebutuhan serta tantangan yang dihadapi oleh pemangku kepentingan di sektor peternakan. Hasil dari tahap eksploratif ini digunakan sebagai dasar dalam pengembangan sistem berbasis data kuantitatif yang mengintegrasikan informasi peternakan dari berbagai sumber. Hasil penelitian ini diharapkan dapat memberikan kontribusi nyata dalam optimalisasi penyaluran bantuan pemerintah di bidang peternakan, sekaligus mendukung transformasi digital dalam pengelolaan data sektor pertanian. Implikasi dari penelitian ini juga membuka peluang untuk pengembangan sistem serupa di sektor lain yang membutuhkan pengambilan keputusan berbasis data dan spasial.

The implementation of Artificial Intelligence-based Decision Support Systems (AI-DSS) in recruitment has significantly enhanced efficiency; however, concerns regarding algorithmic bias persist. Existing AI-DSS models primarily emphasize explicit data, often neglecting psychological and behavioral factors essential for fair recruitment. This study integrates Person-Job Fit and Person-Organization Fit theories into AI-DSS while employing adaptive learning techniques to mitigate bias. Using a mixed- methods approach with an explanatory sequential design, this research combines quantitative analysis (statistical comparisons of AI-DSS and traditional hiring methods, bias evaluation using fairness metrics) with qualitative insights (interviews with HR professionals and candidates). The findings indicate that AI-DSS improves selection efficiency and candidate performance yet remains susceptible to biases derived from historical data. Adaptive learning enhances fairness; however, ethical concerns about transparency and accountability persist. This research strengthens the AI recruitment debate by suggesting a comprehensive model that balances the operational efficiency, ethical needs, and fair practices. Explaining AI paradigms requires additional research to establish trust and flexibility for AI recruitment systems.

The development of Artificial Intelligence (AI) has brought a significant impact in various fields, including pricing strategies. Value-Based Pricing as an approach that focuses on the perception of customer value is now increasingly optimized through the integration of AI technology. The study aims to explore how AI can support value-based decision-making through complex data processing and predictive analytics, as well as how critical thinking skills remain a key competency in interpreting results and establishing sustainable pricing strategies. This approach emphasizes the importance of synergy between technological sophistication and human cognition, in order to produce decisions that are not only efficient but also ethical and value-oriented in the long term. The results of this study show that the application of AI in Value-Based Pricing can improve the accuracy and speed of analysis, but still requires an active role of critical thinking in the context of interpretation and strategic decision-making.

Social media has increasingly become a driving force for community innovation in cyberspace. Platforms such as Facebook, WhatsApp, Instagram, and TikTok have empowered individuals and groups worldwide to collaborate, share ideas, and innovate collectively. However, despite these opportunities, social media also presents significant challenges, including misinformation, algorithmic bias, and issues surrounding trust and privacy. Utilizing a mixed- methods research approach, this study combines quantitative surveys and qualitative interviews to assess the impact of social media on community innovation. Data will be analyzed using descriptive statistics and thematic analysis to understand engagement, collaboration patterns, and the challenges faced by users. The study aims to contribute to existing research by providing insights into how social media platforms can be optimized for innovation and what strategies can be employed to overcome the digital divide and algorithmic biases. This study finds social media significantly enhances community innovation, with surveys indicating 95% faster collaboration, 100% idea generation efficiency, and 90% solution implementation post- adoption. Persistent challenges include misinformation (84% agreement), algorithmic bias limiting diversity (90%), and access gaps excluding marginalized groups (87%). Mixed- methods data reveal a paradox: platforms democratize innovation yet perpetuate inequities. Opportunities emerge in algorithmic transparency (80% agreement) and policy-driven digital inclusion (78%). Reforms prioritizing ethical governance, transparent AI, and inclusive infrastructure are critical to equitably harness social media’s potential as a global innovation catalyst.

Entrepreneurship has been an important predictor of resolving the problem of unemployment in Indonesia and other countries through the creation of new jobs. Academics, scholars, and policy researchers especially promote entrepreneurial strategies because of their important role in expanding innovation, enhancing economic growth, and supporting economic development. The primary purpose of this study is to systematically identify and evaluate entrepreneurial intuition research. This study involved data from the Scopus database from 2004 to 2024 using preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. For visualization, this study involved VOSviewer software to determine the linkage of variables. The findings indicate that entrepreneurial intuition is often linked with several factors: sustainability, technology usage, and cognitive process. The findings also conclude that there is a robust relationship between entrepreneurial intuition and entrepreneurial intention. This paper also highlights the trend of entrepreneurial ideas for student education and the key role of entrepreneurial learning in enhancing entrepreneurial intuition. The findings of this paper are to allocate avenues for future research on the theme of entrepreneurial intuition.

The development of microcontroller technology that is rapidly making applications to be developed as a work in the field of technology that can help and facilitate human activities. Researchers see that current technological developments there are many uses of loadcell sensors to detect weight on objects. The number of factors that affect the weight difference between digital scales and scales very significant conventional. Therefore researchers analyzed the use of loadcell sensors for calculate solid and liquid objects and analyze the work system and the accuracy of the accuracy on the loadcell sensor in calculating the weight of solid objects and liquid objects.

Abstract The more developed human needs about technology, the more research will be conducted on increasingly rapid technological developments, one example is IoT or Internet of things. It cannot be denied that IoT is a future technology that will be widely used by humans, because IoT has a concept that aims to expand the benefits of internet connectivity that is continuously connected. Allows humans to easily communicate with all equipment connected to the internet network. For this reason, the researcher developed a research on the concept of data communication between devices and interfaces. In this study the website uses wireless which is connected to the internet network. This wireless device will use the ESP8266 wireless module that has been embedded in the Wemos D1 R1 Mini.