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All Journal Jurnal Sarjana Teknik Informatika JUITA : Jurnal Informatika ILKOM Jurnal Ilmiah Bulletin of Computer Science Research
Nurhadi Nurhadi
Institut Teknologi Dan Bisnis Riau Pesisir
Computer Science & IT

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Stacking-Based Hybrid Ensemble Learning for Security Personnel Attendance Prediction and Performance Classification Nurhadi Nurhadi; Khairul Azmi Azmi; Mustazzihim Suhaidi Suhaidi; Sandi Fadilah Fadilah
Jurnal Sarjana Teknik Informatika Vol. 14 No. 2 (2026): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i2.32090

Abstract

Monitoring the attendance and performance of campus security personnel is essential to ensure operational effectiveness and service reliability. However, conventional monitoring approaches are often manual, fragmented, and prone to inaccuracies, limiting timely decision-making. This study proposes a stacking-based hybrid ensemble machine learning model for predicting security personnel attendance and classifying performance within the SIMSATPAM campus monitoring system at Institut Teknologi dan Bisnis Riau Pesisir. The proposed method integrates Random Forest, Support Vector Machine (SVM), and XGBoost as base learners, while Logistic Regression is employed as the meta-learner to improve predictive capability and classification robustness. To prevent overfitting and data leakage, the stacking architecture utilizes K-Fold Cross-Validation and Out-of-Fold (OOF) prediction mechanisms during meta-learner training. Experimental results demonstrate that the proposed stacking hybrid ensemble model outperforms individual classifiers across all evaluation metrics. The model achieved an accuracy of 94.27%, precision of 93.81%, recall of 93.45%, and F1-score of 93.62%, improving accuracy by 4.13% compared to Random Forest, 5.51% compared to SVM, and 2.24% compared to XGBoost. Furthermore, the proposed model produced stable multi-class classification performance for attendance prediction and personnel performance evaluation. Analysis indicates that attendance duration, shift schedule, and lateness frequency are the most influential variables affecting prediction outcomes. These findings confirm that the proposed stacking-based hybrid ensemble approach provides an effective intelligent decision-support system for automated campus security monitoring and personnel management in smart campus environments.
Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus Nurhadi Nurhadi; Sarjon Defit; Gunadi Widi Nurcahyo
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.754

Abstract

Dengue Hemorrhagic Fever (DHF) and Typhus/Typhoid are two infectious diseases often found in tropical areas. In Indonesia, data shows that cases of DHF and typhoid are quite high, so a system is needed that can help doctors make faster and more accurate decisions based on blood test results. Based on the previous explanation, this study aims to apply the Deep Learning Multilayer Perceptron (MLP) method to be able to identify dengue fever and typhus. This study uses a Deep Learning-based Multilayer Perceptron approach for accurate classification of Dengue Fever, Typhoid Fever, and Normal cases using clinical blood parameters and selected symptoms. This methodology consists of several stages: dataset acquisition, preprocessing, model architecture design, training, and evaluation. The dataset was taken from Dumai City Hospital medical record data from 2023 to 2024, totaling 379 patient data used to identify Dengue Fever and Typhus using 7 clinical parameters as the main input obtained from laboratory examination results and patient clinical symptoms: Hemoglobin, Leukocyte, Platelet count, Hematocrit level, Headache, Abdominal pain, and diarrhea. Based on the results obtained, the application showed the best performance in classifying Dengue Fever, which is shown through the achievement of the model evaluation metrics as follows. The test results indicate that an increase in the amount of test data is directly proportional to the percentage of classification success achieved by the system. Based on the test results with 10% validation data, 70 % training data, and 20 % test data, the system showed very good performance with an overall accuracy of: 98.68% (Accuracy = 0.9868), which indicates a high level of success in classifying for the three classes, namely Normal, Dengue Fever, and Typhus.
Edge AI-Based Multimodal Biometric Smart Reader Using YOLOv8 for Integrated Academic Attendance Systems Nurhadi Nurhadi; Emil Naf'an; Desyanti Desyanti; Mustazzihim Suhaidi
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30527

Abstract

Attendance systems in vocational education institutions face challenges related to accuracy, security, and susceptibility to manipulation due to the use of single-modality authentication methods. RFID-based systems are vulnerable to card sharing, fingerprint systems suffer from latency during peak usage, and face recognition systems are sensitive to illumination and pose variations. This study proposes an Edge AI-based multimodal biometric smart reader integrating RFID, fingerprint, and YOLOv8-based face recognition for an academic attendance system at SMK Negeri 1 Dumai. The system is implemented on NVIDIA Jetson Nano as an edge computing device and integrated with an academic information system through an IoT-based architecture for real-time attendance monitoring. A decision-level fusion approach using majority voting is applied, where authentication is accepted if at least two of three modalities match. The system is evaluated using accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and response time. Experimental results show that the proposed multimodal system achieves an accuracy of 98.72%, outperforming RFID (89.34%), fingerprint (92.15%), and YOLOv8 face recognition (95.63%). The system also reduces FAR to 0.82% and FRR to 0.91%, with an average response time of 1.47 seconds, making it suitable for real-time deployment. Overall, the proposed Edge AI-based multimodal biometric system demonstrates high accuracy, improved security, and efficient real-time performance, providing a scalable solution for intelligent attendance systems in vocational education environments.
Design and Quantitative Evaluation of a Keycloak-Based Single Sign-On Architecture for Integrated Institutional Information Systems Nurhadi Nurhadi; Mustazzihim Suhaidi; Muhammad Athariq
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3368.237-254

Abstract

The increasing integration of digital services in higher education institutions requires a secure and scalable authentication mechanism to ensure consistent access across multiple systems. However, fragmented authentication approaches often result in repeated login processes, inconsistent security policies, and inefficient identity management. This study proposes a modular Single Sign-On (SSO) architecture based on Keycloak, integrated with OAuth 2.0, OpenID Connect, and JSON Web Tokens (JWT), to support unified authentication in institutional information systems. A quantitative experimental approach is employed to evaluate system performance in a real academic environment involving 100 user accounts. The evaluation focuses on authentication efficiency, scalability, reliability, and user productivity. The results show a 62% reduction in average login time, an 85% increase in authentication throughput, and a 100% authentication success rate. Scalability testing indicates stable system performance under concurrent workloads, while token validation overhead remains minimal, ensuring that security enhancements do not degrade system responsiveness. In addition, task completion time decreases by 51%, accompanied by a significant improvement in user productivity. These findings demonstrate that the proposed Keycloak-based SSO architecture provides measurable improvements in performance, scalability, security governance, and usability. The study contributes to software systems engineering by presenting a validated architectural model and a comprehensive quantitative evaluation framework for identity management in higher education environments.
Co-Authors Desyanti Desyanti Emil Naf'an Gunadi Widi Nurcahyo Khairul Azmi Azmi Muhammad Athariq Mustazzihim Suhaidi Mustazzihim Suhaidi Mustazzihim Suhaidi Suhaidi Sandi Fadilah Fadilah Sarjon Defit