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All Journal The IJICS (International Journal of Informatics and Computer Science) Jurnal Sistem Komputer dan Informatika (JSON)
Badia Inaya Sazrade
Universitas Sriwijaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Comparative Evaluation of Machine Learning Algorithms for Diabetes Prediction with SMOTE and Principal Component Analysis Badia Inaya Sazrade; Ken Ditha Tania; Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9903

Abstract

Diabetes mellitus is a chronic disease that requires early detection to reduce the risk of severe complications. However, machine learning-based diabetes prediction is often affected by class imbalance and high-dimensional data. This study investigates the effectiveness of integrating Synthetic Minority Over-sampling Technique (SMOTE) and Principal Component Analysis (PCA) for diabetes prediction. A total of 80,437 records from a Kaggle diabetes dataset were processed using the Knowledge Discovery in Databases (KDD) framework. Six machine learning algorithms, namely Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, and Neural Network, were evaluated using train-test split ratios of 70:30, 80:20, and 90:10. Performance was measured using accuracy, precision, recall, and F1-score. Without oversampling, XGBoost consistently achieved the highest accuracy across all split ratios, peaking at 94.04% at the 80:20 ratio; however, recall for the minority (diabetic) class remained substantially lower than for the majority class, indicating that high overall accuracy masked weaker detection of actual diabetes cases. After applying SMOTE, overall accuracy declined across all models (e.g., XGBoost fell to 87.52% at 80:20), but minority-class recall improved markedly, indicating a more balanced classification between classes at the cost of overall accuracy. Notably, at the 80:20 split, the Neural Network achieved a marginally higher accuracy (87.67%) than XGBoost under SMOTE, although XGBoost remained the top performer at the 70:30 and 90:10 ratios, suggesting that its advantage under class-balanced conditions is not uniform across split ratios. PCA was applied to reduce data dimensionality and did not substantially affect predictive performance; however, the present results do not include quantitative evidence, such as the change in feature count or computation time, needed to substantiate claims about its contribution to efficiency. These findings suggest that XGBoost with an 80:20 split is the most effective configuration when class imbalance is not addressed, while the application of SMOTE narrows the performance gap between models and shifts the trade-off toward more balanced, rather than purely accuracy-maximizing, classification.
The Impact of Dominant Color in the Shopee Application's User Interface Design on User Focus Levels Fathimah Fadiyah Salimah; Septi Mulia Putri; Badia Inaya Sazrade; Fathoni
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.v10i2.9686

Abstract

The design of mobile application interfaces plays a crucial role in shaping user experience, where color influences not only aesthetics but also user cognition and focus. This study aims to analyze the effect of the dominant orange color in the Shopee application interface on user focus levels. A quantitative correlational approach was employed, involving 32 respondents who completed a Likert-scale questionnaire, with data analyzed using SPSS. The results show a strong and statistically significant correlation between color perception and user focus (r = 0.681; p < 0.05), indicating that better evaluation of the dominant color is associated with higher focus levels. Furthermore, the coefficient of determination (R² = 0.464) reveals that nearly half of the variation in user focus is explained by color alone. These findings confirm that the dominant orange color enhances attention, supports sustained concentration, and improves navigation efficiency. This study highlights color as a key functional factor in optimizing user focus in mobile commerce applications.
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.
Co-Authors Cahyo Adi Nugraha Fathimah Fadiyah Salimah Fathoni Ferdiansyah Frans Nicko Apriansyah Ken Ditha Tania Septi Mulia Putri Tri Mutiara Illahi Zaqqi Yamani A