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All Journal International Journal of Advances in Intelligent Informatics Jurnal INTECH Teknik Industri Universitas Serang Raya
Bakar, Azuraliza Abu
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Computer Science & IT Decision Sciences, Operations Research & Management 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.
Community preserving sparsification based on K-core for enhanced community detection in attributed networks Setiadi, Tedy; Yaakub, Mohd Ridzwan; Bakar, Azuraliza Abu
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2209

Abstract

Community detection is an important aspect of complex network analysis, especially in attribute networks where topological structure and attribute information both play a role in community formation. Traditional structure-based methods tend to result in topologically dense but semantically inconsistent communities, while attribute-based approaches can improve semantic coherence but face scalability constraints and high computational costs. On the other hand, graph sparsification techniques have been used to reduce the size of the network, but most focus on structural aspects alone and rarely consider attributes, so the quality of the resulting community is often degraded. This study proposes CPSK (Community Preserving Sparsification based on K-core), a sparsification framework that combines k-core decomposition with attribute-based side weighting. This approach is designed specifically for attribute networks, with the aim of maintaining a balance between structural reduction and community semantic consistency, while improving the efficiency of the detection process. Evaluation of the six datasets showed that CPSK consistently generates more stable and meaningful communities than existing attribute-based community detection methods, while maintaining an edge in computing efficiency on large-scale and heterogeneous networks.
Co-Authors Ivan Michael Siregar, Ivan Michael Othman, Zulaiha Ali Putra, Muhammad Reza Tedy Setiadi Yaakub, Mohd Ridzwan