Workplace Preference Analytics Among Graduates
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Abstract
Graduates often find themselves difficult to secure a job after completing their education at universities or colleges. In this light, researchers have proposed various solutions to address this challenge. However, most of the work has largely focused on academic profile and personality traits; very few have highlighted the importance of workplace location characteristics. To address this challenge, this study has employed feature selection and machine learning approach to help graduates identify desired company type and sector based on their preferences and preferred location. The data used in this study was obtained from the Ministry of Higher Education Graduates Tracer Study's data, specifically for 2382 Multimedia University (MMU) students' employment situation upon graduating. Additional analytical datasets focusing on company and graduate locations were developed in order to extract further features relevant for this analysis. Feature selection was used to identify top-10 predictors that influence the selection of jobs in graduates' desired sectors. Various analytics methods such as Decision Tree Analysis, Random Forest Model selection, Naive Bayes Classification Method, Support Vector Machines and K-Nearest Neighbor Algorithms were employed for comparative evaluations within the workplace analytics scope. Notably so, results from this study demonstrate that using Random Forest Algorithm resulted in better performance in predicting employment status with an accuracy rate of 99.40%, predicting company type with 66.60% and lastly predicting company sector with 30.80% when compared to other predictive models utilized during our research work's project lifecycle phase.
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References
R. Megasari, E. Piantari, R. Nugraha, “Graduates profile mapping based on job vacancy information clustering”, 2020 6th international conference on science in information technology (icsitech), pp. 35-39), 2020.
B. D. Puspasari, L. L. Damayanti, A. Pramono, A. K. Darmawan, “Implementation k-means clustering method in job recommendation system”, 2021 7th international conference onelectrical, electronics and information engineering (iceeie), pp. 1-6, 2021.
A. Olowolayemo, K. Harun, T. Mantoro, “University based job recommender alumni system”, 2018 international conference on computing, engineering, and design (icced), pp. 212-217, 2018.
L. Jie, S. Zheng, W. Qi, C. Xiya, “Analysis of employment status and countermeasures of biology graduates in local normal universities based on big data technology—take the graduates of guangxi normal university from 20l6 to 2020 as an example”, 2021 2nd international conference on artificial intelligence and education (icaie), pp. 572-578, 2021.
Q. Zhou, F. Liao, L. Ge, J. Sun, “Personalized preference collaborative filtering: Job recommendation for graduates”, 2019 ieee smartworld, ubiquitous intelligence computing, advanced trusted computing, scalable computing communications, cloud big data computing, internet of people and smart city innovation (smartworld/scalcom/uic/atc/cbdcom/iop/sci), pp. 1055-1062, 2019.
O. Awujoola, P. O. Odion, M. E. Irhebhude, & H. Aminu, “Performance evaluation of machine learning predictive analytical model for determining the job applicants employment status”, vol. 6, pp. 67-79, 2021.
M. Nie, L. Yang, J. Sun, H. Su, H. Xia, D. Lian, K. Yan, “Advanced forecasting of career choices for college students based on campus big data”, Frontiers of Computer Science, vol. 12, 2017.
S. Li, Y. Chuancheng, W. Hongguo, D. Yanhui, “An employment recommendation algorithm based on historical information of college graduates”, 2018 9th international conference on information technology in medicine and education (itme), pp. 708-711, 2018.
H. Yu, Z.-q. Zhang, “The application of data mining technology in employment analysis of university graduates”, 2018 ieee/acis 17th international conference on computer and information science (icis), pp. 846-849, 2018.
D. Chakraborty, M. S. Hossain, M. S. Arefin, “Demand analysis of cse graduates of different universities in job markets”, 2019 international conference on electrical, computer and communication engineering (ecce), pp. 1-6, 2019.
Z. Chen, W. Liang, X. Gao, Z. Zhou, M. Wu, “Research on the accurate recommendation management system for employment of college graduates on Hadoop”, 2019 5th international conference on big data and information analytics (bigdia), pp. 19-22, 2019.
M. Jiang, Y. Fang, H. Xie, J. Chong, M. Meng, “User click prediction for personalized job recommendation”, World wide web 22, pp. 325-345, 2019.
M. Kahn, T. Gamedze, J. Oghenetega, “Mobility of sub-saharan africa doctoral graduates from south african universities—a tracer study”, International Journal of Educational Development, vol. 68, pp. 9-14, 2019.
K. Kusnawi, J. Ipmawati, D. Kusumandaru, “Decision support system employee recommendation using fuzzy sugeno method as a job search service”, 2019 international conference on information and communications technology (icoiact), pp. 539-542, 2019.
A. Nigam, A. Roy, H. Singh, H. Waila, “Job recommendation through progression of job selection”, 2019 ieee 6th international conference on cloud computing and intelligence systems (ccis), pp. 212-216, 2019.
L. G. Rodriguez, E. P. Chavez, “Feature selection for job matching application using profile matching model”, 2019 ieee 4th international conference on computer and communication systems (icccs), pp. 263-266, 2019.
Z. Yang, S. Cao, “Job information crawling, visualization and clustering of job search websites”, 2019 ieee 4th advanced information technology, electronic and automation control conference (iaeac), vol. 1, pp. 637-641, 2019.
R. J. Atela, L. Othuon, & J. Agak, “Relationship between types of intelligence and career choice among undergraduate students of maseno university, kenya”, 2020.
H. Fan, “A prediction model of college students’ employment based on data mining”, 2020 13th international conference on intelligent computation technology and automation (icicta), pp. 549-552, 2020.
D. Hooshyar, M. Pedaste, Y. Yang, “Mining educational data to predict students’ performance through procrastination behavior”, Entropy, vol. 22(1), 2020.
R. S. Kumar, N. Prakash, S. Anbuchelian, “Prediction of job openings in it sector using long short -term memory model”, 2020 fourth international conference on i-smac (iot in social, mobile, analytics and cloud) (i-smac), pp. 945-953, 2020.
N. Gunarathne, S. Senaratne, R. Herath, “Addressing the expectation–performance gap of soft skills in management education: An integrated skill-development approach for accounting students”, The International Journal of Management Education, vol. 19(3), p. 100564, doi: https://doi.org/10.1016/j.ijme.2021.100564, 2021.
B. Heriyadi, “Tracer study analysis for the reconstruction of the mining vocational curriculum in the era of industrial revolution 4.0”, Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, pp. 3013-3019, 2021.
N. Premalatha, & S. Sujatha, “An effective ensemble model to predict employment status of graduates in higher educational institutions”, 2021 fourth international conference on electrical, computer and communication technologies (icecct), pp. 1-4, 2021.
M. Rahman, M. Asadujjaman, “Multi-criteria decision making for job selection”, 2021 international conference on decision aid sciences and application (dasa), pp. 152-156, 2021.
S. R. Nudin, B. Warsito, A. Wibowo, “Impact of soft skills competencies to predict graduates getting jobs using random forest algorithm”, 2022 1st international conference on information system information technology (icisit), pp. 49-54, 2022.
Y. Bharambe, N. Mored, M. Mulchandani, R. Shankarmani, S. G. Shinde, “Assessing employability of students using data mining techniques”, 2017 international conference on advances in computing, communications and informatics (icacci), pp. 2110-2114, 2017.
M. M. Almutairi, M. H. A. Hasanat, "Predicting the suitability of is students’ skills for the recruitment in saudi arabian industry", 2018 21st saudi computer society national computer conference (ncc), pp. 1-6, 2018.
M. Y. Arafath, M. Saifuzzaman, S. Ahmed, S. A. Hossain, “Predicting career using data mining”, 2018 international conference on computing, power and communication technologies (gucon), pp. 889-894, 2018.
J. Martinez-Gil, B. Freudenthaler, T. Natschläger, “Recommendation of job offers using random forests and support vector machines”, 2018.
A. Nachev, T. Teodosiev, “Analysis of employment data using support vector machines”, International journal of applied engineering research issn, vol. 13, pp. 13525-13535. 2018.
R. Amalia, A. Wibowo, “Prediction of the waiting time period for getting a job using the naive bayes algorithm”, International research journal of advanced engineering and science, vol. 5, pp. 225-229, 2020.
A. Daharwal, Prof. S. Gore., A. Bhagwat., S. Dethe., S. Chavan., “Career guidance system using machine learning for engineering students (cs/it)”, International research journal of engineering and technology (irjet), vol. 7, pp. 3417-3420, 2020.
K. Jamal, R. Kurniawan, I. Husti, Zailani, M. Z. A. Nazri, J. Arifin, “Predicting career decisions among graduates of tafseer and hadith”, 2020 2nd international conference on computer and information sciences (iccis), pp. 1-4, 2020.
T. V. Huynh, K. V. Nguyen, N. L.-T. Nguyen, A. G.-T. Nguyen, “Job prediction: From deep neural network models to applications”, 2020 rivf international conference on computing and communication technologies (rivf), pp. 1-6, 2020.
R. G. Angesti, A. Kurniawati, H. D. Anggana, “Prediction of the telkom university’s undergraduates waiting period for getting a job using the cart algorithm”, 2021 4th international conference of computer and informatics engineering (ic2ie), pp. 135-140, 2021.
B. M. D. E. Bannaka, D. M. H. S. G. Dhanasekara, M. K. Sheena, A. Karunasena, N. Pemadasa, “Machine learning approach for predicting career suitability, career progression and attrition of it graduates”, 2021 21st international conference on advances in ict for emerging regions (icter), pp. 42-48, 2021.
M. Sharma, S. Joshi, S. Sharma, A. Singh, R. Gupta, “Data mining classification techniques to assign individual personality type and predict job profile”, 2021 9th international conference on reliability, infocom technologies and optimization (trends and future directions) (icrito), pp. 1-5, 2021.
J. Zhu, G. Viaud, C. Hudelot, “Improving next-application prediction with deep personalized-attention neural network”, 2021 20th ieee international conference on machine learning and applications (icmla), pp. 1615-1622, 2021.
S. Vassef, R. Toosi, M. A. Akhaee, “Job title prediction from tweets using word embedding and deep neural networks”, 2022 30th international conference on electrical engineering (icee), pp. 577-581, 2022.