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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Noureddine Falih
University of Sultan Moulay Slimane

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HR analytics a roadmap for decision making: case study Brahim Jabir; Noureddine Falih; Khalid Rahmani
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 2: August 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i2.pp979-990

Abstract

In the socio-economic world, the human resources are in the most top phase of the enterprise evolution. This evolution began when the arithmetic, statistics are applicable over a vast of opportunities and used to identify problems and support decision. However, analytics has been emerged to provide predictions and understand the people performance based on available data.In light of this vast amount of information, human resources services need to deploy a predictive management model and operating system of analytics that can be an efficient and an instead solution that can respond to the gaps of the traditional existing ones and facilitate the decision making. In this paper, we present a literature review of this HR analytics concept and a case study concerning the impact of interventions using an analytics solution. 
Density-based classification with the DENCLUE algorithm Mouhcine El Hassani; Noureddine Falih; Belaid Bouikhalene
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp269-278

Abstract

Classification of information is a vague and difficult to explore area of research, hence the emergence of grouping techniques, often referred to Clustering. It is necessary to differentiate between an unsupervised and a supervised classification. Clustering methods are numerous. Data partitioning and hierarchization push to use them in parametric form or not. Also, their use is influenced by algorithms of a probabilistic nature during the partitioning of data. The choice of a method depends on the result of the Clustering that we want to have. This work focuses on classification using the density-based spatial clustering of applications with noise (DBSCAN) and DENsity-based CLUstEring (DENCLUE) algorithm through an application made in csharp. Through the use of three databases which are the IRIS database, breast cancer wisconsin (diagnostic) data set and bank marketing data set, we show experimentally that the choice of the initial data parameters is important to accelerate the processing and can minimize the number of iterations to reduce the execution time of the application.
Co-Authors Belaid Bouikhalene Brahim Jabir Khalid Rahmani Mouhcine El Hassani