STUDI LITERATUR PENINGKATAN KINERJA BKK SEKOLAH DENGAN SISTEM INFORMASI BURSA KERJA

Penelitian ini ditujukan untuk BKK yang ada di Sekolah Menengah Kejuruan atau SMK. Saat ini pihak BKK harus lebih mengoptimalkan kinerja dan peran sebagai penyedia informasi industri kepada alumni dan siswa agar mempunyai pengetahuan yang cukup untuk bekal mencari pekerjaan. Penyampaian segala bentuk informasi oleh pihak BKK kini masih menggunakan poster dan brosur yang ditempel di majalah dinding sehingga siswa maupun alumni kurang tertarik dan sering tertinggal informasi, dikarenakan informasi industri tertumpuk dengan informasi sekolah lainnya. Pada lowongan pekerjaan terdapat informasi kebutuhan industri dan syarat-syarat yang dibutuhkan untuk melamar pekerjaan tersebut. Bursa Kerja Khusus atau yang disingkat BKK adalah sebuah lembaga yang terdapat pada SMK yang memiliki tugas penting seputar dunia industri. Terdapat hambatan dalam segi penyebaran informasi secara luas yang dimiliki BKK. Sehingga diperlukan media sistem informasi  untuk mempermudah BKK untuk menyampaikan dan menyebarluaskan informasi industri maupun lowongan pekerjaan kepada siswa dan alumni. Metode penelitian yang digunakan adalah metode SLR (Systematic, Literature, Review). Penelitian ini bertujuan untuk mengetahui : (1) Adanya perbedaan kinerja BKK sekolah sebelum dan sesudah menggunakan sistem informasi bursa kerja; (2) Pengaruh dari sistem informasi bursa kerja terhadap kinerja BKK sekolah. Hasil penelitian dari beberapa jurnal menunjukkan bahwa terdapat perbedaan sebelum dan sesudah  penggunaan sistem informasi bursa kerja. Dengan menggunakan sistem informasi bursa kerja oleh BKK dalam upaya penyampaian informasi kepada siswa dan alumni dapat meningkatkan kinerja BKK. Jadi dapat disimpulkan bahwa sistem informasi bursa kerja lebih baik dan efektif dalam meningkatkan kinerja BKK

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

THE USE OF RECOMMENDER SYSTEMS IN WEB APPLICATIONS – THE TROI CASE

Avoiding digital marketing, surveys, reviews and online users behavior approaches on digital age are the key elements for a powerful businesses to fail, there are some systems that should preceded some artificial intelligence techniques. In this direction, the use of data mining for recommending relevant items as a new state of the art technique is increasing user satisfaction as well as the business revenues. And other related information gathering approaches in order to our systems thing and acts like humans. To do so there is a Recommender System that will be elaborated in this thesis. How people interact, how to calculate accurately and identify what people like or dislike based on their online previous behaviors. The thesis includes also the methodologies recommender system uses, how math equations helps Recommender Systems to calculate user’s behavior and similarities. The filters are important on Recommender System, explaining if similar users like the same product or item, which is the probability of neighbor user to like also. Here comes collaborative filters, neighborhood filters, hybrid recommender system with the use of various algorithms the Recommender Systems has the ability to predict whether a particular user would prefer an item or not, based on the user’s profile and their activities. The use of Recommender Systems are beneficial to both service providers and users. Thesis cover also the strength and weaknesses of Recommender Systems and how involving Ontology can improve it. Ontology-based methods can be used to reduce problems that content-based recommender systems are known to suffer from. Based on Kosovar’s GDP and youngsters job perspectives are desirable for improvements, the demand is greater than the offer. I thought of building an intelligence system that will be making easier for Kosovars to find the appropriate job that suits their profile, skills, knowledge, character and locations. And that system is called TROI Search engine that indexes and merge all local operating job seeking websites in one platform with intelligence features. Thesis will present the design, implementation, testing and evaluation of a TROI search engine. Testing is done by getting user experiments while using running environment of TROI search engine. Results show that the functionality of the recommender system is satisfactory and helpful

Working in Australia's digital games industry

The Working in Australia’s Digital Games Industry: A Consolidation Report is the outcome of a comprehensive study on the games industry in Australia by Dr Sandra Haukka from the ARC Centre of Excellence for Creative Industries and Innovation (CCI) based at Queensland University of Technology in Brisbane. The study responds to concerns that Australia’s games industry would not reach its full potential due to a lack of local, highly skilled staff, and a lack of appropriately trained graduates with the necessary knowledge and skills. This is the first of two reports produced with the support of the Games Developers’ Association of Australia. Over coming months researchers will develop a future skills strategy report for the industry

Impact of artificial intelligence on education for employment: (learning and employability Framework)

Sustainable development has been a global goal and one of the key enablers to achieve the sustainable development goals is by securing decent jobs. However, decent jobs rely on the quality of education an individual has got, which value the importance of studying new education for employment frameworks that work. With the evolution of artificial intelligence that is influencing every industry and field in the world, there is a need to understand the impact of such technology on the education for employment process. The purpose of this study is to evaluate and assess how AI can foster the education for employment process? And what is the harm that such technology can brings on the social, economical and environmental levels? The study follows a mapping methodology using secondary data to identify and analyze AI powered startups and companies that addressed the learning and employability gaps. The study revealed twelve different AI applications that contribute to 3 main pillars of education for employment; career exploration and choice, skills building, and job hunting. 94% of those applications were innovated by startups. The review of literature and study results showed that AI can bring new level of guidance for individuals to choose their university or career, personalized learning capabilities that adapt to the learner\u27s circumstance, and new whole level of job search and matchmaking

Graphical Database Architecture For Clinical Trials

The general area of the research is Health Informatics. The research focuses on creating an innovative and novel solution to manage and analyze clinical trials data. It constructs a Graphical Database Architecture (GDA) for Clinical Trials (CT) using New Technology for Java (Neo4j) as a robust, a scalable and a high-performance database. The purpose of the research project is to develop concepts and techniques based on architecture to accelerate the processing time of clinical data navigation at lower cost. The research design uses a positivist approach to empirical research. The research is significant because it proposes a new approach of clinical trials through graph theory and designs a responsive structure of clinical data that can be deployed across all the health informatics landscape. It uniquely contributes to scholarly literature of the phenomena of Not only SQL (NoSQL) graph databases, mainly Neo4j in CT, for future research of clinical informatics. A prototype is created and examined to validate the concepts, taking advantage of Neo4j’s high availability, scalability, and powerful graph query language (Cypher). This research study finds that integration of search methodologies and information retrieval with the graphical database provides a solid starting point to manage, query, and analyze the clinical trials data, furthermore the design and the development of a prototype demonstrate the conceptual model of this study. Likewise the proposed clinical trials ontology (CTO) incorporates all data elements of a standard clinical study which facilitate a heuristic overview of treatments, interventions, and outcome results of these studies

The Changing Economics of Attaining Post-Secondary Education in the U.S.: An Analysis by Stakeholder: Employer, Student, and Government

This paper has as its focus the identification of economic returns to stakeholders of investment in human capital as it pertains to attainment of post-secondary education in the US. The centerpiece of this study pertains to returns to prospective employers in a 21st century environment, which demands of the labor market rapid adaptation to technology and it’s applications. With dynamic demands from employers as a backdrop, this paper seeks to determine if the benefit of post-secondary education is becoming more or less relevant from the perspective of the employer. A qualitative approach comprised of in-depth interviews of employers has been conducted. In particular learnings from those employers regarding their views of the importance of technology and what impacts if any this has on expectations of post-secondary institutional curriculums. The second stakeholder, the student, has been considered via a cost benefit analysis based upon expected earning differentials for the student group who has chosen to pursue a post-secondary education versus those who have not. Earnings have been quantified and extrapolated over the lifetime of defined student groups and compared to the actual cost of college with considerations for occupational differentials, in order to determine the net value of a college education to a student. This information has provided the basis for understanding the value of post-secondary education to the third stakeholder, the government. Projected income taxes for selected occupational groups have been calculated and compared based on the net present value of these lifetime earnings. The differential revenues that accrue to federal agencies via these taxes has been compared to the costs associated with attending post-secondary education. With this information in hand, conclusions have been made regarding policy implications for federal subsidies of post-secondary education