Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
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This Author published in this journals
All Journal Applied Information System and Management Indonesian Journal of Machine Learning and Intelligent Systems
Abdullah Said Alkalbani
University of Buraimi
Languange, Linguistic, Communication & Media

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Improving spam detection techniques using machine learning algorithms Abdullah Said Alkalbani; Iin Lesmana Putra; Dini Oktarina Dwi Handayani
Indonesian Journal of Machine Learning and Intelligent Systems Vol. 1 No. 1 (2026)
Publisher : Indonesian Artificial Neural Network Society

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Abstract

Spam detection has become a critical challenge in maintaining the integrity and security of digital communication systems. This study focuses on enhancing spam detection precision by applying the XGBoost algorithm, compared to other machine learning models such as Support Vector Machines (SVM) and Random Forests. Traditional rule-based methods, while straightforward, need to address the increasing sophistication of spam tactics, leading to high rates of false positives and negatives. Machine learning, with its data-driven adaptability, provides a powerful alternative for improving spam detection accuracy. This research evaluates the effectiveness of XGBoost in handling large and complex datasets, leveraging its ability to build robust predictive models by combining weak learners. Performance metrics, including accuracy, precision, recall, and F1-score, are used to assess its efficacy. Additionally, a comparative analysis highlights XGBoost's advantages over SVM, known for its precision in high-dimensional spaces, and Random Forest, valued for its resilience against overfitting. The study emphasizes the importance of parameter optimization and feature engineering in maximizing the performance of spam detection models. This research contributes to the development of more reliable and scalable spam detection frameworks by addressing the diverse attributes of spam messages. The findings offer valuable insights for researchers and practitioners in cybersecurity, underscoring the transformative potential of XGBoost in combating the evolving threat of spam.
Human-AI Work Redistribution Across the Software Development Life Cycle: A Socio-Technical Study of Indonesian Developers Arisantoso Arisantoso; Achmad Benny Mutiara; Abdullah Said Alkalbani; Saipul Anwar; Muhamad Femy Mulya
Applied Information System and Management (AISM) Vol. 9 No. 2 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i2.53455

Abstract

AI Coding Assistants are becoming increasingly integrated into software development, changing not only how developers perform their tasks but also how humans and AI share responsibilities. However, how this redistribution of work occurs across the Software Development Life Cycle (SDLC) remains insufficiently understood. This study investigates how responsibilities are distributed between developers and AI across SDLC activities, how these changes affect software engineering workflows and developer roles, and which socio-technical factors influence this process. Semi-structured interviews with seven software professionals in Indonesia were analyzed using reflexive thematic analysis. The results identify three patterns of human-AI work redistribution: AI-delegated, AI-augmented, and human-controlled work. AI contributes more substantially to structured, repetitive, and relatively verifiable activities, particularly code generation, debugging, testing, and documentation. In contrast, requirements analysis, architectural decisions, security assessment, and release approval remain largely under human control because they require contextual understanding, professional judgment, and accountability. The findings also indicate a shift in developer responsibilities. This redistribution depends on developer experience, AI literacy, trust, model capability, task complexity, security requirements, team practices, and organizational policies. Drawing on socio-technical systems theory and activity theory, the study develops an empirically grounded Human-AI Work Redistribution Model that conceptualizes AI-assisted software engineering as a selective redistribution of responsibilities rather than uniform automation across the SDLC.
Co-Authors Achmad Benny Mutiara Arisantoso Arisantoso Dini Oktarina Dwi Handayani Iin Lesmana Putra Mulya, Muhamad Femy Saipul Anwar