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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Junadhi
Universitas Sains dan Teknologi Indonesia, Pekanbaru, Indonesia
Computer Science & IT

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A Stacking Ensemble Learning Framework for Analyzing SkillsMismatch in IT Graduate Employability Rahmaddeni; Junadhi; Sukri Adrianto; Suandi Daulay; Syarfi Azis; Deshinta Arrova Dewi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 3 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i3.5991

Abstract

The increasing gap between academic outcomes and labor market demands has led to a significant skills mismatch among Information Technology graduates. The purpose of this research is to develop a stacking ensemble-based decision-support framework for analyzing and predicting employability outcomes in a multidimensional skill context. The method used is a stacking ensemble learning approach, in which multiple base learners are combined and optimized with XGBoost as the meta-learner. The study uses a synthetic dataset of 2,000 records with 31 variables designed to represent realistic employability factors, including academic performance, technical skills, soft skills, certifications, and career preferences. To enhance interpretability, SHAP (Shapley Additive exPlanations) is employed to identify the contribution of each feature to the prediction outcomes. The result of this study isthat the proposed stacking framework achieves superior performance compared to individual models, demonstrating improved predictive accuracy and robustness. The analysis further reveals that GPA, technical competencies, soft skills, and professional certifications strongly influence employability. In conclusion, the proposed framework not only improves prediction performance but also provides interpretable insights that support decision-making. These findings offer practical implications for higher education institutions and policymakers in designing curriculum strategies and targeted training programs to reduce skills mismatch and enhance IT graduate employability.
Optimizing Content Recommendations Using a Hybrid Filtering Algorithm to Enhance User Relevance and Engagement Lusiana Efrizoni; Junadhi; Agustin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i2.5988

Abstract

Recommender systems play an important role in helping users discover relevant content in environments characterized by information overload. However, existing approaches often struggle to balance recommendation relevance and user engagement. Collaborative filtering is constrained by data sparsity and the cold-start problem, whereas content-based methods that rely on textual features may not fully capture dynamic user preferences. This study aims to develop a hybrid deep learning-based recommendation model that improves both recommendation relevance and user engagement. The proposed method integrates collaborative filtering via Neural Matrix Factorization (NeuMF) with content-based filtering via a Long Short-Term Memory (LSTM) text encoder, employing an early-fusion strategy. An experimental research method was applied using synthetic user–item interaction data. Model performance was evaluated using ranking metrics (Precision@10, Recall@10, and NDCG@10) and engagement metrics (Click-Through Rate and Average Completion Ratio). The results show that the hybrid model outperforms the baseline models. It achieves Precision@10 of 0.143, Recall@10 of 0.112, and NDCG@10 of 0.139, which exceed those of both the NeuMF-only and LSTM-only models. In terms of engagement, the hybrid model also records the best performance with a CTR of 0.0017 and an ACR of 0.0090. These findings indicate that integrating user–item interaction patterns with semantic content representations can significantly enhance recommendation quality and user engagement, providing a more effective solution for content-rich digital platforms.
Co-Authors Agustin Daulay, Suandi Deshinta Arrova Dewi Lusiana Efrizoni Rahmaddeni Sukri Adrianto Syarfi Azis