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master's thesis
Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža.
2017. urn:nbn:hr:145:617707
Lukačević, Maja
Josip Juraj Strossmayer University of Osijek
Faculty of Economics and Business in Osijek
Chair of Quantitative Methods and Informatics

Cite this document

Lukačević, M. (2017). Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža. (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:145:617707

Lukačević, Maja. "Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža.." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:145:617707

Lukačević, Maja. "Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža.." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, 2017. https://urn.nsk.hr/urn:nbn:hr:145:617707

Lukačević, M. (2017). 'Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža.', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, accessed 02 April 2025, https://urn.nsk.hr/urn:nbn:hr:145:617707

Lukačević M. Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža. [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek; 2017 [cited 2025 April 02] Available at: https://urn.nsk.hr/urn:nbn:hr:145:617707

M. Lukačević, "Klasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža.", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Economics and Business in Osijek, Osijek, 2017. Available at: https://urn.nsk.hr/urn:nbn:hr:145:617707

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Metadata
TitleKlasifikacija zaposlenika s pomoću stabla odlučivanja i neuronskih mreža.
Title (english)CLASSIFICATION OF EMPLOYEES WITH THE DECISION TREE AND NEURAL NETWORKS
AuthorMaja Lukačević
MentorMarijana Zekić Sušac (mentor)
Committee memberMarijana Zekić Sušac (predsjednik povjerenstva)
Committee memberDavor Dujak (član povjerenstva)
Committee memberNataša Šarlija (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Economics and Business in Osijek
(Chair of Quantitative Methods and Informatics)
Osijek
Defense date and country2017-04-12, Croatia
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Economics
Business Informatics
Abstract
Predmet istraživanja u ovom radu je odlazak zaposlenika iz poduzeća kroz gradnju modela neuronskih mreža i stabla odlučivanja napravljenom u softverskom paketu Statistica. Ovaj rad pokušava razumjeti zašto zaposlenici napuštaju poduzeće, koji je glavni razlog, zašto zaposlenici donose takvu odluku i kako da se spriječi odljev talentiranih zaposlenika. Zaposlenici su podijeljeni u dvije grupe. Klasa 0 predstavlja zaposlenike u poduzeću, a klasa 1 predstavlja zaposlenika koji je napustio... More poduzeće. Modeli neuronske mreže koristili su dvije funkcije, logističku aktivacijsku funkciju i tangens aktivacijsku funkciju. Rezultati su pokazali da je logistička (94,07%) imala bolju stopu točnosti nego tangens (93,21%) aktivacijska funkcija. Stablo odlučivanja ima najbolju stopu 95,47%, zbog bolje stope taj model pokazao se kao uspješniji. Najvažnija varijabla zaposlenikova napuštanja u stablu odlučivanja je stupanj zadovoljstva, a najmanju važnost ima promaknuće u zadnjih 5 godina. Less
Abstract (english)
The subject of research in this study is modeling employees turnover by using neural networks and decision tree methods in software package Statistica. This study is trying to understand why employees leave, i.e. what are the main factors why people make that decision and how to prevent the drain of employee talent. Employees are classified in two classes. Class 0 represent employees who stayed in company and class 1 represent employees who have already left the company. Models of... More neural networks in this study use two functions: logistic activation function and tanh activation function. Results showed that the logistic (94,07%) have better rate then tanh (93,21%) activation function. Decision tree model produces the highest rate of 95,47% and is the the most successful model. The most important variable on employee turnover in decision tree model was the satisfaction level, while the lowest importance has promotion in last 5 years. Less
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:145:617707
Study programmeTitle: Business economy; specializations in: Business Informatics
Course: Business Informatics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra ekonomije (magistar/magistra ekonomije)
Type of resourceText
File originBorn digital
Access conditionsAccess restricted to students and staff of home institution
Terms of useLicence In copyright icon
Created on2017-04-27 07:03:08
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