Selection and Recommendation Scholarships Using AHP-SVM-TOPSIS

Abstract. Gerakan Nasional Orang Tua Asuh Scholarship offers a number of scholarship packages. As there are a number of applicants, a system for selection and recommendation is required. we used 3 methods to solve the problem, the methods are AHP for feature selection, SVM for classification from 3 classes to 2 classes, and then TOPSIS give a rank recommendation who is entitled to receive a scholarship from 2 classes. In testing threshold for AHP method the best accuracy 0.01, AHP selected 33 from 50 subcriteria. SVM has highest accuracy in this research is 89.94% with Sequential Training parameter are λ =0.5, constant of γ =0.01 , ε = 0.0001, and C = 1. Keywords: Selection, Recommendation, Scholarships, AHP-SVM-TOPSI

A data-driven MADM model for personnel selection and improvement

Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts’ judgements as input for the derivation of solutions. However, the subjectivity of the experts’ experience can have a negative influence on this type of decision-making process. With the arrival of today’s data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future. First published online 15 May 202