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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Embedded Systems, Security and Intelligent Systems
Hendra Kurniawan
Jurusan Teknik Informatika, Universitas Maritim Raja Ali Haji
Computer Science & IT Control & Systems Engineering

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Hospital Nurse Scheduling Optimization Using Simulated Annealing and Probabilistic Cooling Scheme Ferdi Chahyadi; Azhari SN; Hendra Kurniawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.23056

Abstract

Nurse’s scheduling in hospitals becomes a complex problem, and it takes time in its making process. There are a lot of limitation and rules that have to be considered in the making process of nurse’s schedule making, so it can fulfill the need of nurse’s preference that can increase the quality of the service. The existence variety of different factors that are causing the nurse scheduling problem is so vast and different in every case. The study is aimed to develop a system used as an equipment to arrange nurse’s schedule. The working schedule obtained will be checked based on the constraints that have been required. Value check of the constraint falsification used Simulated Annealing (SA) combined with cooling method of Probabilistic Cooling Scheme (PCS). Transitional rules used cost matrix that is employed to produce a new and more efficient state. The obtained  results showed that PCS cooling methods combined with the transition rules of the cost matrix generating objective function value of  new solutions better and faster in processing time than the cooling method exponential and logarithmic. Work schedule generated by the application also has a better quality than the schedules created manually by the head of the room.
A Hybrid VAE-CNN Framework for Unsupervised URL-Based Phishing Detection Hendra Kurniawan; Yoga Syahputra; Novrizal Fattah Fahmitra
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.12237

Abstract

Purpose – URL-based phishing remains a persistent cybersecurity threat because attackers continuously generate deceptive links that evade blacklist-based and supervised detection systems. This study aims to develop and evaluate an unsupervised phishing detection framework that learns legitimate URL patterns without requiring labeled phishing samples during training. Design/methods/approach – A hybrid Variational Autoencoder–Convolutional Neural Network (VAE-CNN) model was proposed and compared with a Convolutional Autoencoder (CAE) baseline. Both models were trained only on legitimate URLs from the PhiUSIIL dataset and evaluated using repeated holdout validation across five random seeds. The experiments included four latent dimensions, five percentile-based anomaly thresholds, and McNemar’s exact test. In addition, external validation was conducted using PhishTank phishing URLs combined with PhiUSIIL legitimate holdout samples. Findings – In the main PhiUSIIL benchmark, the best VAE-CNN configuration achieved an accuracy of 0.9458, precision of 0.9091, recall of 0.9907, and F1-score of 0.9482, outperforming the best CAE baseline with an F1-score of 0.8920. In external validation, VAE-CNN achieved an F1-score of 0.9494, compared with 0.8470 for CAE. McNemar’s test confirmed statistically significant paired-prediction differences across all evaluated comparisons. Research implications/limitations – The findings indicate that probabilistic latent modeling improves unsupervised phishing detection, although the study remains limited to character-level URL representations and benchmark-based evaluation. Originality/value – The proposed VAE-CNN provides a statistically validated unsupervised framework for URL-based phishing detection and demonstrates promising cross-source performance under the evaluated PhiUSIIL–PhishTank setting, particularly when labeled malicious URLs are limited, delayed, or rapidly outdated.
Co-Authors Azhari SN Chahyadi, Ferdi Novrizal Fattah Fahmitra Yoga Syahputra