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All Journal Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Manexia
Galih
Universitas Islam Nusantara
Computer Science & IT Economics, Econometrics & Finance

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Human–AI Collaboration Reshaping Creative Labor and Professional Identity Dynamics Galih
Manexia: Journal of Business, Management, and Creative Economy Vol. 2 No. 2 (2026): Human–AI Value Systems
Publisher : UDEX Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66203/manexia.02204

Abstract

Creativity is increasingly no longer an exclusively human endeavor, as artificial intelligence becomes deeply embedded in processes of ideation, production, and evaluation. This shift challenges established assumptions about authorship, originality, and professional identity within the creative economy. Despite growing research on digital labor, artificial intelligence, and identity work, these domains remain theoretically fragmented, limiting understanding of how human–AI collaboration reshapes both creative processes and identity construction. This study addresses this gap by developing an integrative conceptual framework that bridges creative labor theory, identity work, and socio-technical perspectives. Using a mechanism-based analytical approach, the study conceptualizes creative labor as a hybrid co-creative system characterized by generative expansion, iterative co-creation, algorithmic mediation, and human curation. It further explains how these processes trigger identity transformation through recursive stages of destabilization, experimentation, negotiation, and reconstruction. The study contributes by reframing creativity as a distributed process, extending identity theory to incorporate AI as an active participant, and introducing the concept of hybrid intelligent labor, offering a foundation for future empirical inquiry.
Benchmarking Machine Learning Models for Intrusion Detection in Higher Education Networks Using CIC-IDS2017 Moch Irwan Hermanto Irwan; Galih; Rudhi Wahyudi Febrianto; Siti Nur
Infoman's : Jurnal Ilmu-ilmu Informatika dan Manajemen Vol. 20 No. 1 (2026): Infoman's
Publisher : LPPM & Fakultas Teknologi Informasi UNSAP

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Abstract

The security of university networks faces an increase in attack surfaces due to the high dependence on digital services. Machine learning-based Intrusion Detection System (IDS) is one of the approaches to recognize benign and malicious traffic patterns. This study analyzes the benchmark machine learning model in the CIC-IDS2017 dataset through a literature study approach and comparative analysis. The models compared include Decision Tree, Random Forest, Support Vector Machine, Naive Bayes, Artificial Neural Network, as well as additional benchmarks XGBoost, KNN, and Logistic Regression. Evaluation parameters include accuracy, precision, recall, F1-score, false positive, false negative, and class distribution. The benchmark results showed that Random Forest achieved an accuracy of 99.67% with precision, recall, and malicious class F1-scores of 99.90%, 99.67%, and 99.67%, respectively in one of the published configurations. Another comparative study reported that XGBoost and Random Forest achieved 99.96% accuracy in their experimental configurations. Analysis of the class distribution showed a strong imbalance so that accuracy was not sufficient to assess the effectiveness of the IDS. The study recommends macro-F1 evaluation, per-class recall, false negative, and testing on more representative data before the model is used in a college production environment.
Co-Authors Moch Irwan Hermanto Irwan Rudhi Wahyudi Febrianto Siti Nur