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All Journal International Journal of Electrical and Computer Engineering Jurnal INTECH Teknik Industri Universitas Serang Raya
Othman, Zulaiha Ali
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Deep Learning Based Recommendation System for Employee Retention Using Bipartite Link Prediction Siregar, Ivan Michael; Othman, Zulaiha Ali; Bakar, Azuraliza Abu
Jurnal INTECH Teknik Industri Universitas Serang Raya Vol. 11 No. 1 (2025): Juni
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/intech.v11i1.10069

Abstract

The Human Resources (HR) department faces significant challenges in employee retention. Traditional methods, such as performance evaluations and career development using regression, association, and clustering, have been widely used and have yielded positive results. However, these approaches are limited in predicting changes in employee behaviour and capturing complex relationships between variables. In this study, we leverage AI advancements to enhance predictive analysis by utilizing deep learning’s ability to identify patterns and complex relationships while continuously adapting to employee behavior changes. Specifically, we integrate Graph Convolutional Network (GCN) deep learning-based and bipartite graph-based approaches to construct a robust link prediction model. The bipartite employee-training network serves as input to the GCN, where each convolutional layer aggregates information from neighboring nodes, leveraging observed link information at each hidden layer. During the evaluation phase, the model iteratively aggregates information until an optimal state is reached, uncovering hidden relationship patterns that facilitate employee skill development. Empirical results on a benchmark dataset demonstrate significant performance improvements, with precision, recall, and AUC metrics exceeding 80%, highlighting the model's effectiveness in enhancing employee retention.
Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents Mohemad, Rosmayati; Mohd Muhait, Nazratul Naziah; Mohamad Noor, Noor Maizura; Othman, Zulaiha Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5014-5026

Abstract

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method.
Co-Authors Bakar, Azuraliza Abu Ivan Michael Siregar, Ivan Michael Mohamad Noor, Noor Maizura Mohd Muhait, Nazratul Naziah Mohemad, Rosmayati