Job Information Research Articles

Penerapan Model Klasifikasi K-Nearest Neighbor Dalam Pencarian Kesesuaian Pekerjaan

The application of job suitability is very important for companies/institutions in seeing the advantages of job applicants in accordance with their fields. then mobile-based information technology in finding job vacancies for applicants is very much needed for the required vacancies information and the suitability of the required formations. so that applicants can easily see the jobs that applicants need. The focus of this research problem is the existence of a mobile application in classifying job vacancies and the existence of a job information search menu according to the skills possessed and in accordance with the needs of mobile application-based jobs using the k-nearest neighbor (KNN) model. The purpose of this research is to apply a freelance application in aceh to help job search information according to the skills they have that match the needs of the job. The criteria available in the mobile application consist of a list of jobs, self-registration, a list of self-data weights, criteria and weights for recent education, work experience, marital status. The study was conducted with test data used 25 samples and applicants who registered consisted of 30 participants. the test results for the first registrant with the results of age 1, gender 0.5, GPA, 0.8, work experience 0.25, marital status 1 and passed with the value of the KNN calculation result is 0.406, the second applicant passed with a value of 0, 89 and the third did not pass with a value of 0.392. Job vacancies are obtained by taking 11 training data pieces that have the closest Euclidean distance to the questionnaire results. Furthermore, to test the accuracy using 30 samples with 24 accurate results and 6 inaccurate results.

PENYULUHAN HUKUM JAMINAN KEHILANGAN PEKERJAAN KEPADA MASYARAKAT PANCORAN, JAKARTA SELATAN

Community Service (PkM) is carried out by the Faculty of Law Universitas Kristen Indonesia by providing legal counseling about job loss guarantees by the government for workers/labourers who have been laid off due to the Covid-19 pandemic. PkM will be held on 18 December 2021 at Pancoran Buntu II, Pancoran Village, Pancoran District, South Jakarta. The method used is to provide counseling/lectures and provide motivation, then followed by question and answer to obtain results and solutions as a form of solving problems and obstacles faced. At the time of providing counseling/lectures, special tables are also provided to receive personal legal consultations for the community. The results of PkM with legal counseling are very effective considering that there are still many people who do not get information on job loss guarantees provided by the Government through PP No. 37 of 2021 concerning the Implementation of the Job Loss Guarantee Program. With this legal counseling, the public becomes aware of this program and what benefits can be obtained, such as cash benefits, access to job information, and job training. Thus, it is hoped that the people of Pancoran who have lost their jobs / have been laid off are always motivated to maintain a decent standard of living by wanting to work again or try to be independent.

Graph-Community-Enabled Personalized Course-Job Recommendations with Cross-Domain Data Integration

With millions of students/employees browsing course information and job postings every day, the need for accurate, effective, meaningful, and transparent course and job recommender systems is more evident than ever. The current recommendation research has attracted wide attention in the academic and industrial areas. However, existing studies primarily focus on content analysis and user feature extraction of courses or jobs and fail to investigate the problem of cross-domain data integration between career and education. At the same time, it also fails to fully utilize the relations between courses, skills, and jobs, which helps to improve the accuracy of the recommendation. Therefore, this study aims to propose a novel cross-domain recommendation model that can help students/employees search for suitable courses and jobs. Employing a heterogeneous graph and community detection algorithm, this study presents the Graph-Community-Enabled (GCE) model that merges course profiles and recruiting information data. Specifically, to address the skill difference between occupation and curriculum, the skill community calculated by the community detection algorithm is used to connect curriculum and job information. Then, the innovative heterogeneous graph approach and the random walk algorithm enable cross-domain information recommendation. The proposed model is evaluated on real job datasets from recruitment websites and the course datasets from MOOCs and higher education. Experiments show that the model is obviously superior to the classical baselines. The approach described can be replicated in a variety of education/career situations.

Corporate Website Job Recruitment Platform: A Moderated Mediation Model of Organisational Image

Corporate companies’ websites recruitment in advanced countries is well established. However, lesser is known about the Malaysian students and employed jobseekers’ intention to apply for jobs on the corporate companies’ websites. Therefore, this study aims to assess the subjective perceptions of these two groups’ intentions to apply for jobs on the websites. This study also aims to analyse the mediating effect of organisational image on perceived person-job fit (P-J fit) and perceived website usability (PWSU) which may be moderated by these two groups of jobseekers’ intentions. The conceptual model was first analysed using two-level confirmatory factor analysis. Adopting Hayes’ Model 7 template, AMOS software was used to differentiate the two groups’ perceptions. The results indicate that the perceived P-J fit and PWSU are the main factors influencing jobseekers’ intentions to apply for jobs on corporate websites. The moderated mediation technique has identified an indirect effect between perceived P-J fit and the intention to apply for jobs indicating the importance of established organisational image that it arouses applicants’ pursuit intention. Thus, the corporate human resource managers need to ensure the sites are designed for easy navigation and job advertisements that contain updated job information and realistic job preview. Using this platform, companies can reduce costs and time for the recruitment of talents. This study provides the subjective perceptions of jobseekers’ intention study and it contributes to the first component of Planning Theory of Intention. Lastly, this research also contributed to an alternate research method of analysing the moderated mediation using AMOS.

Human Resource Matching Support System Based on Deep Learning

Aiming at the problem of reasonable recommendation and accurate matching of human resources, a hybrid human resources matching recommendation algorithm based on GBT-CNN is proposed in this article. The advantages of traditional GBT and CNN algorithms are combined and can give full play to the high-level feature abstraction ability of convolution processing. The gradient lifting tree is used to transform the features, complete the feature screening and coding, and then input the hybrid convolution neural network to obtain the high-dimensional feature abstraction by using the hybrid convolution operation, to improve the quality of human resources recommendation. In this article, GBT and CNN algorithms are first described, and then the basic framework and specific implementation of GBT-CNN algorithm are introduced. Finally, the effectiveness of the algorithm is verified by simulation experiments. The results show that the deeper correlation between job seeker information and job information can be effectively captured by the algorithm. Besides, reasonable recommendation and accurate matching of human resources can be realized.

Altered Mental Distress Among Employees From Different Occupational Groups and Industries During the COVID-19 Pandemic in Germany.

Mental distress of employees from the financial, public transport, public service, and industrial sector was examined in a cross-sectional study during the second COVID-19 (coronavirus disease 2019) wave in Germany and retrospectively at its beginning. Mental distress in terms of anxiety and depression symptoms was assessed with the Patient Health Questionnaire-4. High and potential occupational SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection risk (OSIR) was defined based on job information from 1545 non-health care workers. The risks for more severe mental distress symptoms increased threefold and twofold, respectively, among employees with high and potential OSIR compared with employees without OSIR. Mental distress severity differed by the extent of work-privacy conflicts, perceived job protection, interactions with colleagues, and overcommitment. Reducing COVID-19 exposure through workplace protective measures, strengthening interactions among colleagues, and supporting employees with work-privacy conflicts could help better protect employees' mental health.

EFEKTIVITAS ANALISIS JABATAN PADA KOPERASI PEGAWAI REPUBLIK INDONESIA (KPRI) SAPTAWA POVINSI LAMPUNG

Abstract Based on Article 117 paragraph (1) of Law Number 43 of 1999 concerning Amendments to Law Number 8 of 1874 concerning the Principles of Personnel, it is determined that civil servants are appointed in positions and ranks. To ensure objectivity, transparency, and conformity between the job demands and the employee who will occupy the position, it is necessary to carry out a job analysis. The results of the job analysis in the form of job information can be used for institutional, management, and staffing purposes. In this study, researchers conducted research on the effectiveness of Job Analysis in the Indonesian Civil Servant Cooperative (KPRI) SAPTAWA, Lampung Province. The method used in this research is a qualitative research method with a descriptive research design. The results of this study are from various indicators used to measure the achievement of goals, acquisition of resources, internal processes and satisfaction of strategic constituencies. From the points of achievement of the effectiveness indicators, the researcher concludes that the Job Analysis at the SAPTAWA Province Employee Cooperative of the Republic of Indonesia (KPRI) in Lampung Province has been effective and has made the quality of service provided to customers and the internal working atmosphere well done. The supporting factor in the implementation of this job analysis is a clear legal basis, then the inhibiting factor in the job analysis at the Indonesian Civil Service Cooperative (KPRI) SAPTAWA Lampung Province is the ideological difference between employees. Keywords: Effectiveness, Job Analysis, Cooperatives

Company, Job, and Perceived Advantage Influence on Job Hunting: Evaluating Glassdoor Job Hunting platform

The current research aims to investigate how features for Job Application Portals (JAP) such as company information, job information, and relative advantage affect job hunting. A cross-sectional field survey of 480 new graduates with IT and applied scientific degrees was conducted in the intended research environment for comparative purposes, which included tech businesses in the EU, US, and Asia samples of job application portal users. Using the Smart PLS software 3.0, partial least square structural equation modeling (PLS-SEM) results revealed that company information, job information, and relative advantage all have a favorable significant impact on job hunting. This research is novel to propose and test a unique theoretical framework in a unique data context to bring key policy directions as well as theoretical and practical ramifications in the field.

Model for Selecting Optimal Dispatching Rules Based Real-time Optimize Job Shop Scheduling Problem

Owing to the randomness of the job release time, it is not possible to obtain all job information in real time during the operation of a manufacturing system. Generating a suitable scheduling strategy at the correct moment is the focus of addressing this disturbance. In this study, the flow time of the job in the manufacturing system was used as a criterion for evaluating the performance of the scheduling strategy. Subsequently, a model was constructed for selecting the optimal dispatching rule (DR) to actively change the scheduling strategies during the production process. The constructed model for selecting the optimal DR included an initial model for selecting the optimal DR and an evaluation model. The initial model for selecting the optimal DR outputs a DR with better performance based on the attributes of the job to be scheduled in the manufacturing system. Meanwhile, the evaluation model is responsible for evaluating the DR output of the initial model for selecting the optimal DR and determining whether the DR needs to be updated; the update process is realized based on simulation technology. Following experimental verification, the constructed model could generate scheduling strategies with superior performance in real-time and realize the update of the historical database. The results of this study will be of reference significance for solving the disturbance problem encountered by the manufacturing system in real time.

The Structural Relationship between Core Competencies, Career Adaptability, and Career Barriers of College Students

The purpose of this study is to analyze the relationship between college students' SUPER core competencies, career adaptability, and career barriers, so that college students can recognize the linkage between core competencies for career development and education in the future. For this purpose, SUPER core competencies, career adaptability, and career barriers were measured for 309 university students at J University. The measured results were analyzed with a structural equation model using the SPSS ver 25.0 and AMOS ver 25.0 programs. The analysis results are as follows: First, some core competencies were higher among female students than male students, high in medical science colleges, and low in business colleges. The age problem of career barriers was higher in male students and third year students, and the lack of job information, lack of interest and future anxiety were higher in medical science colleges and lower in culture and tourism colleges. Second, the ‘skewness’ and ‘kurtosis’ criteria were satisfied by verifying whether each variable satisfied the normal distribution. Also, the correlation of each variable showed that core competency and career adaptability had a positive relationship, core competency had a negative relationship with career barriers, and career adaptability and career barriers had a negative relationship. Third, as a result of analyzing the estimability and fit of the structural regression model for the verification of the research model, a good fit was shown, and the suitability of each variable was confirmed through confirmatory factor analysis. Fourth, the core competency for career adaptability was statistically significant and had an explanatory power of 49.8%. Core competencies for career barriers were not statistically significant. Career adaptability to career barriers was statistically significant and had an explanatory power of 37.8%. This means that the higher the level of core competencies, the higher the career adaptability, and the higher the career adaptability, the lower the career barriers. Based on the results of this study, the conclusions are summarized as follows. First, there are differences in some factors of SUPER core competency, career adaptability, and career barriers according to gender and major. Thus, an approach to core competency and career barriers by gender and college is required. Second, universities should provide opportunities to college students through subjects and non-course curriculum so that they can achieve the core competencies required in the society of the 4<sup>th</sup> Industrial Revolution. Third, in order to strengthen and improve the core competencies of college students, it is also necessary for instructors to inform college students of a system that can recognize what core competencies are lacking, as well as to enlighten them on specific methods and guidelines for their cultivation.

Analysis on Influencing Factors of Knowledge-Based Attitude and Behavior Change of Cardiovascular Disease Nurses.

The study explores the influencing factors of knowledge, attitude, and behavior of cardiovascular disease nurses. A total of 500 CVD nurses from 10 hospitals in the province from January 2020 to 2021 are selected as the survey subjects. After reviewing the literature, a questionnaire is developed to investigate the knowledge, belief, and practice level of cardiovascular disease nurses. The demographic information, job information and knowledge, attitude, and behavior of cardiovascular disease nurses are investigated in the form of questionnaires. Univariate analysis is made on the knowledge, attitude, and behavior scores of the nurses with cardiovascular diseases in demographic characteristics and job characteristics, and multivariate stepwise analysis is made on the factors affecting the knowledge, attitude, and behavior of the nurses with cardiovascular diseases. Cardiovascular disease nurses' knowledge, attitude, and behavior are the medium level, should be regular training activities and psychological counseling, and cultivate high title, long working life, older nursing staff, and practice level.

Higher Employee Engagement through Social Intelligence: A Perspective of Indian Scenario

Recognizing that high levels of social intelligence are required for effective engagement, the authors set out to find the association between employee engagement and social intelligence. Specifically, the goal of this study was to find the explanatory value of social intelligence constructs for employee engagement in a sample of employees by conducting a statistical analysis. The final research included 150 male and 50 female professionals who were selected from FMCG sectors. A questionnaire was used to gather socio-demographic evidence; the Utrecht engagement scale and the Tromso social intelligence scale in the Indian cultural context were used to obtain professional and job information. The findings revealed that employees with high levels of social intelligence scores performed well on engagement measures, with social skills being the most significant predictor of engagement. The findings of this study have substantial practical significance for the development of training and intervention activities targeted at improving employees' performance on the job, among other things.

Implementation of Analytic Network Process Algorithm in E-Lowker System

Job vacancies information system at this time that there are still many companies that use the manual method to get the best employees and the number of job applicants who have not used the internet as a medium of information, but in this case the research aims to facilitate companies in getting the best employees, namely using the ANP Method in a web-based job information system and facilitate job applicants in utilizing internet media as a source of information that has been provided by the company. The ANP method is to find the weighted sum of the performance ratings for each alternative on all attributes, in this study the programming languages ​​used are PHP, MySQL database, CSS and Java Script. The criteria used in this study are education, experience, age and expertise. In this study shows that the Implementation of the ANP Method in the Web-Based Job Vacancy Information System is able to produce the best prospective workers for the company in accordance with the criteria

INDEKS KEPUASAN MAHASISWA TERHADAP LAYANAN AKADEMIK DAN NON AKADEMIK UIN WALISONGO SEMARANG

Higher education as one of the places where education is held is expected to always maintain its quality and quality. To see the quality or quality of higher education management can be assessed from two indicators, namely the fulfillment of quality standards and the achievement of customer satisfaction. Universities must be able to manage their service quality system and administration because if not, the trust of higher education users will decrease as well as the quality of students as one of the university's users, so that student satisfaction measurements need to be carried out regularly to be input and improvement for the university. Student satisfaction survey was conducted on aspects of academic and non-academic services at UIN Walisongo Semarang. Academic and academic service aspects are each measured by 8 indicators. This study aims to determine the level of student satisfaction with service quality after participating in the learning process at UIN Walisongo Semarang. This study uses a quantitative approach and survey methods. The population of this survey is all students of UIN Walisongo Semarang. The results of the assessment of the student satisfaction index on academic services are as follows: the highest satisfaction is on the online guardianship indicator with an index of 2.99 and the lowest satisfaction is on the online lecture system (E-learning) indicator with an index of 2.27. Meanwhile, the results of the satisfaction index assessment on non-academic services are as follows: the highest indicators are; Religious guidance services for students (3.01), while the lowest index is; Career guidance and job information services (Walisongo Career Center) (2.69). All satisfaction results show high satisfaction criteria.

Minimizing the Late Work of the Flow Shop Scheduling Problem with a Deep Reinforcement Learning Based Approach

In the field of industrial manufacturing, assembly line production is the most common production process that can be modeled as a permutation flow shop scheduling problem (PFSP). Minimizing the late work criteria (tasks remaining after due dates arrive) of production planning can effectively reduce production costs and allow for faster product delivery. In this article, a novel learning-based approach is proposed to minimize the late work of the PFSP using deep reinforcement learning (DRL) and graph isomorphism network (GIN), which is an innovative combination of the field of combinatorial optimization and deep learning. The PFSPs are the well-known permutation flow shop problem and each job comes with a release date constraint. In this work, the PFSP is defined as a Markov decision process (MDP) that can be solved by reinforcement learning (RL). A complete graph is introduced for describing the PFSP instance. The proposed policy network combines the graph representation of PFSP and the sequence information of jobs to predict the distribution of candidate jobs. The policy network will be invoked multiple times until a complete sequence is obtained. In order to further improve the quality of the solution obtained by reinforcement learning, an improved iterative greedy (IG) algorithm is proposed to search the solution locally. The experimental results show that the proposed RL and the combined method of RL+IG can obtain better solutions than other excellent heuristic and meta-heuristic algorithms in a short time.

Career Recommendation for College Students Based on Deep Learning and Machine Learning

With the popularization of higher education, China’s higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students.

Impact of artificial intelligence-enabled job characteristics and perceived substitution crisis on innovative work behavior of employees from high-tech firms

The importance of artificial intelligence (AI)-enabled systems has been at the forefront of innovation research for the past ten years. The literature has reported the use of AI-enabled systems (AIS) in firms, but there has been a paucity of empirical research on AI-enabled job characteristics of employees of high-tech firms in regard to innovative work behavior (IWB). To address this gap, drawing on job design theory, we proposed a model that describes the roles that AI-enabled task and knowledge characteristics play in employees’ IWB. Furthermore, the effects of AIS on human workforce replacement have been a highly debated topic. Drawing on prospect theory, we tested the moderating role of perceived substitution crisis (PSC) triggered by AIS on IWB. We used the partial least square (PLS) technique to test the hypotheses using data from 486 responses collected from an online survey completed by high-tech professionals. The results indicated that AI-enabled task characteristics (job autonomy and skill variety) and knowledge characteristics (job complexity, specialization, and information processing) impact IWB and that AI-enabled job characteristics are strongly associated with IWB under differential effects of PSC. The implications of this study could be used by academicians and practitioners to design AI-enabled job characteristics.

A Comprehensive Assessment of Cultivation Environment of Top Innovative High-Level Talents Based on Deep Learning Algorithm.

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real-time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top-level talents, will become more and more crucial. Growing in importance is China's economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation.

A Fresh Perspective on Job Analysis: Extracting Job Information from Job Vacancy Announcements

A Fresh Perspective on Job Analysis: Extracting Job Information from Job Vacancy Announcements

Investigating the Prevalence of Contact Dermatitis and its Related Factors Among Hospital Staff During the Outbreak of the COVID-19 Epidemic: A Cross-Sectional Study.

Background:Frequent hand washing and prolonged mask use during the Coronavirus disease (COVID-19) pandemic may cause contact dermatitis in hospital staffs. This study was conducted to investigate the prevalence of contact dermatitis and its related factors among hospital staff during the COVID-19 pandemic.Materials and Methods:The present cross-sectional study was conducted among the hospital personnel of Aligoudarz Shahid Valian Hospital, Lorestan, Iran, from March to June 2020. Sampling was performed using the complete enumeration method and 142 hospital staff (physicians, nurses, service personnel, nurses’ aides, secretaries, and laboratory and radiology experts) participated in the study. Data were collected using a 3-part questionnaire containing a demographic characteristics form, job information form, and the Nordic Occupational Skin Questionnaire (NOSQ). The obtained data were analyzed using descriptive statistics, Chi-square, and Spearman's correlation coefficient in SPSS.Results:The prevalence of hand dermatitis in participants was 88.02%, and facial dermatitis with the surgical mask was 71.83% and with n95 mask was 81.69%. The most-reported symptom was dryness of the hand. The most common symptom observed after prolonged use of surgical (55 Samples; 38.73%) and n95 masks (79 Samples; 55.63%) was redness. The frequency of hand washing (F = 8; p = 0.017) and the use of liquid soap (F = 4; p = 0.047) had a significant interrelation with the severity of dermatitis.Conclusions:Planning for preventive actions and management of the current situation should be a priority among the policies of hospital managers given the high prevalence of hand and face dermatitis among hospital staff.