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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 154 Documents
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A machine learning approach for early prediction of mental health crises Chigagure, Hassan; Sakala, Lucy Charity
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p335-345

Abstract

The global mental health crisis, intensified by the COVID-19 pandemic, placed unprecedented strain on healthcare systems and highlighted the urgent need for proactive crisis prevention strategies. This study investigated the effectiveness of various machine learning (ML) models in predicting mental health crises within 28 days post-hospitalization, leveraging an eight-year longitudinal dataset. Multiple data preprocessing techniques, including feature selection (EFSA, RFECV), imputation, and class imbalance handling (SMOTE, Tomek links), were systematically applied to enhance model performance. Six traditional classifiers—logistic regression, support vector machine, k-nearest neighbors, naive Bayes, XGBoost, and AdaBoost—were evaluated alongside ensemble learning (EL) methods (bagging, boosting, stacking). Performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used for comprehensive assessment. Results demonstrated that ensemble methods, particularly boosting and bagging, consistently achieved high predictive accuracy (up to 93%), with XGBoost and AdaBoost emerging as top performers. Feature selection and class imbalance techniques further improved model robustness and generalizability. The findings underscored the potential of ML-driven approaches for early identification of at-risk patients, enabling more effective resource allocation and timely interventions in mental health care. Recommendations for integrating these predictive tools into clinical workflows were discussed to support data-driven decision-making.
Characteristics ransomware stop/djvu remk and erqw variants with static-dinamic analysis Nugrahadi, Dodon Turianto; Abadi, Friska; Herteno, Rudy; Muliadi, Muliadi; Alkaff, Muhammad; Alfando, Muhammad Alvin
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p283-293

Abstract

Ransomware has developed into various new variants every year. One type of ransomware is STOP/DJVU, containing more than 240+ variants. This research to determine changes in differences characteristics and impact between ransomware variants STOP/DJVU remk, which is a variant from 2020, and the erqw variant from 2023, through a mixed-method research approach. Observation, simulation using mixing static and dynamic malware analysis methods. Both variants are from the Malware Bazaar site. The total characteristics based on dynamic analysis, the remk variant has 177, and the erqw variant has 190, which increased by 1.8%. The total characteristics based on static analysis, the remk variants have 586, and the erqw variants have 736, which increased by 5.7%. All characteristics from remk to erqw increasing in dynamic analysis, except the number of payloads that decreased about 20%. In static analysis, all characteristics from remk to erqw increase except the number of sections decreased about 1.5%. It can be the affected CPU performance, because the remk variant affects performance by increasing CPU work by 3.74%, while the erqw variant affects performance by reducing CPU work by 1.18%, both compared with normal CPU. which will affect the ransomware's destructive work and require changes in its handling.
Predictive model for high-risk healthcare clients and claims frequency Zhou, Lenias; Mutandavari, Mainford; Matondora, Lucia
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p346-354

Abstract

Global healthcare spending surged to approximately USD 9.8 trillion in the aftermath of the COVID-19 pandemic, intensifying the need for effective risk management strategies in healthcare insurance. This study proposes a predictive model designed to identify high-risk clients for timely targeted interventions and to forecast claims frequency for optimized resource allocation. A real-world claims dataset from a healthcare insurance provider was utilized. Bayesian optimization was employed to enhance data labelling. A deep learning (DL) model with sigmoid activation was used to classify high-risk clients, while a regression model forecasted claims frequency. The model was trained and validated, and gave an accuracy of 97%, a precision of 95.2%, a recall of 98.1% and an F1-score of 96.6%. The results confirmed the model’s accuracy in identifying high-risk clients and its ability to provide reliable forecasting of future claims frequency. Importantly, the model also provided the reason behind its classification decision, enhancing transparency and trust. This research provides valuable data-driven insights to both the healthcare insurers and clients, giving them the power to stay ahead in managing key risks, which ultimately reduces the cost of healthcare insurance. This work contributed a scalable and interpretable solution for risk prediction in healthcare insurance.
Cloud computing needs to explore into sky computing Ullah, Arif; Remmach, Hassnae; Aznaoui, Hanane; Şahin, Canan Batur; Mrhari, Amine
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p294-306

Abstract

This paper evaluates key issues in cloud computing and introduces a novel model, known as sky computing, to address these challenges. Cloud computing, a transformative technology, has played a critical role in reshaping modern operations—especially following the COVID-19 pandemic, when many human activities shifted to technology-driven platforms. It offers multiple service models, including Software as a Service, Hardware as a Service, Desktop as a Service, Backup as a Service, and Network as a Service, each tailored to user requirements. However, the rapid expansion of cloud-based technologies and interconnected systems has intensified infrastructure and scalability challenges. Sky computing, or the “cloud of clouds,” emerges as an advanced layer above traditional cloud models, enabling dynamically provisioned, distributed domains built over multiple serial clouds. Its core capability lies in offering variable computing capacity and storage resources with dynamic, real-time support, providing a robust and unified platform by integrating diverse cloud resources. This paper reviews related technologies, summarizes prior research on sky computing, and discusses its structural design. Furthermore, it examines the limitations of current cloud computing frameworks and highlights how sky computing could overcome these barriers, positioning it as a pivotal architecture for the future of distributed computing.

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