Open AccessDetermination of Work Schedule Based on Employee Data Classification Using the Decision Tree Algorithm C4.5 Method
Author(s) -
Mas’ud Effendi,
AUTHOR_ID,
Risna Dyah Ariani,
Retno Astuti,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
industria
Language(s) - English
Resource type - Journals
eISSN - 2549-3892
pISSN - 2252-7877
DOI - 10.21776/ub.industria.2021.010.03.6
Subject(s) - work shift , decision tree , computer science , schedule , scheduling (production processes) , mean shift , data mining , decision tree learning , shift work , algorithm , software , machine learning , artificial intelligence , database , operations management , engineering , pattern recognition (psychology) , neuroscience , biology , programming language , operating system
This study aims to create a work shift scheduling system based on data classification, as well as to determine its level of accuracy and provide schedule recommendations. The method used was the Decision Tree Algorithm C4.5 which functions as a classification system to form a work shift schedule. The study included 128 employees, and a total of 43 training data were obtained from a 1/3 split of the dataset, then processed using RapidMiner 5.3 data mining software. Furthermore, the rule of decision tree calculation results was used to classify employee and shift formation on the web system based on PHP and MySQL. The attributes of the decision-maker consist of gender, health records, age, and work unit. The classification found 96 employees who occupy the afternoon shift and 32 for the night shift. System testing was carried out using K-fold cross-validation, which produced an average accuracy of 93.39%, with the highest found in the six-fold cross-validation of 95.35%. Two-shift scheduling was proposed on the web system with a work shift rotation in the form of a metropolitan plan (2-2-2 rota).