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All Journal The IJICS (International Journal of Informatics and Computer Science)
Frans Nicko Apriansyah
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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A Generative AI-Driven Approach to Integrating the SECI Model for Knowledge-Based Systems Frans Nicko Apriansyah; Badia Inaya Sazrade; Cahyo Adi Nugraha; Tri Mutiara Illahi; Ken Ditha Tania; Zaqqi Yamani A
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9717

Abstract

This study examines the potential role of Generative Artificial Intelligence (Gen-AI) in extending the Socialization, Externalization, Combination, and Internalization (SECI) model within knowledge management. Using a Systematic Literature Review (SLR) of studies published between 2024 and 2026, it maps the functions of technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Logic Augmented Generation (LAG) into Nonaka and Takeuchi’s knowledge spiral. The findings indicate that Gen-AI can support knowledge processes such as retrieval, interpretation, structuring, and integration, which may contribute to more continuous and digitally mediated knowledge flows. Some reviewed studies report improvements in handling tacit and explicit knowledge; for example, a simulation-based study reports a tacit knowledge recall rate of up to 94.9%, although this result is derived from a specific experimental context and should not be generalized. The review also identifies challenges, including AI-generated inaccuracies, overreliance on automated systems, and data security concerns, highlighting the continued importance of human oversight. This study contributes a conceptual mapping of a Generative AI-based Knowledge Framework (GRAI) as an extension of the SECI model; however, this contribution remains theoretical and requires further empirical validation across organizational contexts.
Comparative Study of Naive Bayes and SVM for E-Commerce Sentiment Classification on Shopee Yoga Fradana; Cahyo Adi Nugraha; Frans Nicko Apriansyah; Tri Mutiara Illahi; Ken Ditha Tania; Allsela Meiriza
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9740

Abstract

This study compares the performance of Naive Bayes and Support Vector Machine (SVM) for sentiment classification of men’s shirt product reviews on Shopee. A dataset of 500 reviews was collected via web scraping and processed through case folding, tokenizing, stopword removal, and stemming, followed by TF-IDF feature extraction. The data was split at an 80:20 ratio and evaluated using accuracy, precision, recall, and F1-score. The main contribution of this study is demonstrating that despite both algorithms achieving equal overall accuracy of 93%, SVM outperforms Naive Bayes in detecting negative sentiment on a class-imbalanced dataset, with SVM attaining a negative class recall of 0.87 and F1-score of 0.88 compared to 0.80 and 0.87 for Naive Bayes. These findings provide practical guidance for selecting an appropriate classifier in imbalanced e-commerce review classification tasks.
Co-Authors Allsela Meiriza, Allsela Badia Inaya Sazrade Cahyo Adi Nugraha Ken Ditha Tania Tri Mutiara Illahi Yoga Fradana Zaqqi Yamani A