Best lecturer decision support system using method Analytical Hierarchy Process (AHP) and simple adaptive weighting (SAW)

• Main Authors: ,, Dwi Pebrianti, ,, Nurhayati, Bayuaji, Luhur, ,, Mohammad Syafrullah,
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: T10.5 Communication of technical information
• Online Access: http://irep.iium.edu.my/101591/1/Best_Lecturer_Decision_Support_System_Using_Method_Analytical_Hierarchy_Process_AHP_and_Simple_Adaptive_Weighting_SAW.pdf; http://irep.iium.edu.my/101591/7/Scopus%20-%20Document%20details%20-%20Best%20Lecturer%20Decision%20Support%20System%20Using%20Method_to%20update.pdf; http://irep.iium.edu.my/101591/; https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9946532
• Institution: Universiti Islam Antarabangsa Malaysia

Data processing as one of information technology area has wide range of applications including the selection of best lecturer in universities. Best Lecturer Assessment is required by the university to identify qualified human resources and in the future this process will give motivations to the lectures to be more productive and increase the quality of doing their jobs. In Buana Perjuangan University, Karawang, the selection of best lecturer is still conducted manually, where giving result to be subjective and questionable. Additionally, decision support system method has not been applied in determining the best lecturer. In this study, we proposed hybrid method which combines Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to determine the best lecturer by using five criteria, namely attendance at lecture meetings, academic rank, number of publications, additional assignments, and education levels. The data sample used were obtained using a random sampling technique on lecturer data, provided by the University Data and Information Center. The study involves two phases. Phase 1 involves in developing a questionnaire that contains a list of criteria that a best lecturer must have. In Phase 2, an AHP decision matrix is developed, and the candidates will be ranked based on the selected criteria obtained from Phase 1 and their weight values. The result will then be ranked by using SAW to obtain even more accurate decision of best lecturer. The results obtained indicate that the value of the Consistency Index (CI) for all respondents is consistent, 0.8 in average. Then, the CI result is used to determine the Consistency Ratio (CR), where the result is also consistent for all of the respondents, -0.7 in average. These result shows that the proposed method has the same output with the expert (respondents) decisions.