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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Nuansa Informatika Journal of Elektronik Sistem InformasI
PARAMA YOGA, TITAN
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Audit Keamanan Aplikasi Presensi Human Resource Management System Menggunakan Framework Cobit 5 Acep Saepuloh, Acep Saepuloh; PARAMA YOGA, TITAN; Zamani, Fadli Emsa
JESII: Journal of Elektronik Sistem InformasI Vol 3 No 1 (2025): JOURNAL ELEKTRONIK SISTEM INFORMASI (JUNE)
Publisher : Departement Information Systems Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/jesii.v3i1.4041

Abstract

Information system security is a crucial aspect in the operation of a company, especially in managing employee data. To support the company's operational processes, PT Dekatama Centra uses a face verification-based attendance application to record employee attendance. However, like any other system, this application has potential security risks that need to be evaluated to ensure optimal data protection. This research aims to analyze the security level of the HRMS attendance application using the COBIT 5 framework, specifically in the APO13 (Manage Security) and DSS05 (Manage Security Service) domains. The research process was conducted through interviews, observations, and questionnaires, which were designed to measure the maturity level of the application security system based on the COBIT 5 assessment model. The audit results show that the maturity level of system security is still at the “Performed” level (Level 1), which means that the security process has been implemented but not well documented. Some aspects that still require improvement include user access management that must be strengthened with double authentication and access rights restrictions based on roles, protection against malware by improving network security systems and endpoint protection, and regular security monitoring by implementing a logging and monitoring system based on Securtiy Information and Event Management (SIEM). To overcome these problems, this research recommends implementing an Information Security Management System (ISMS) in accordance with the ISO/IEC 27001 standard, improving data encryption mechanisms, and increasing cybersecurity awareness for employees through continuous training programs. By implementing these recommendations, PT Dekatama Centra is expected to increase system resilience to cyber threats, maintain the confidentiality of employee data, and ensure the continuity of safer and more efficient company operations.
Emoji-Based Sentiment Classification Using Ensemble Learning with Cross-Validation: A Lightweight Approach for Social Media Analysis: Klasifikasi Sentimen Berbasis Emoji Menggunakan Ensemble Learning dengan Validasi Silang: Pendekatan Ringan untuk Analisis Media Sosial Alamsyah, Nur; Bayu Wibisono, Gunthur; Parama Yoga, Titan; Budiman; Hendra, Acep
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.396

Abstract

The increasing use of emojis in online communication reflects emotional expression that is often more immediate and intuitive than text. This study proposes a lightweight sentiment classification approach that utilizes only emoji features extracted from social media posts, without relying on textual content. The importance of this research lies in its relevance to short-form digital content, where textual sentiment cues are minimal or absent. To address the classification problem, we implement and compare multiple machine learning models including Random Forest (RF), Support Vector Machine, and an ensemble Voting Classifier combining both. Emoji tokens were vectorized using character-level count vectorization, and performance was evaluated using 5-fold cross-validation to ensure robustness and generalizability. Results show that the ensemble model achieved the highest average accuracy of 93.6%, outperforming the individual classifiers. These findings confirm that emojis alone can serve as reliable indicators of sentiment and support the deployment of fast, interpretable, and scalable models for social media sentiment analysis.
OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING Jennifer Kaunang, Valencia Claudia; Alamsyah, Nur; Parama Yoga, Titan; Hendra, Acep; Budiman, Budiman
Jurnal Techno Nusa Mandiri Vol. 20 No. 2 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.6912

Abstract

The increasing complexity of 6G network slicing introduces new challenges in identifying abnormal behavior within highly virtualized and dynamic network infrastructures. This study aims to address the anomaly detection problem in 6G slicing environments by comparing the performance of three models: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques. The optimized autoencoder is trained to reconstruct normal data patterns, with anomaly detection performed using a threshold- based reconstruction error approach. Reconstruction errors are evaluated across different percentile thresholds to determine the optimal boundary for classifying abnormal behavior. All models are tested on a publicly available 6G Network Slicing Security dataset. Results show that the optimized autoencoder outperforms both the baseline autoencoder and the random forest in terms of anomaly sensitivity. Specifically, the optimized model achieves an F1- score of 0.1782, a recall of 0.2095, and an accuracy of 0.714. These results indicate that introducing regularization and dropout significantly improves the ability of autoencoders to generalize and isolate anomalies, even in highly imbalanced datasets. This approach provides a lightweight and effective solution for unsupervised anomaly detection in next- generation network environments.
Co-Authors Acep Hendra Acep Saepuloh, Acep Saepuloh Bayu Wibisono, Gunthur Budiman Jennifer Kaunang, Valencia Claudia Nur Alamsyah Zamani, Fadli Emsa