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Scientific Journal of Computer Science
Published by PT. Teknologi Futuristik Indonesia
ISSN : -     EISSN : 31103170     DOI : https://doi.org/10.64539/sjcs
Core Subject : Science,
Computer Science & IT
The Scientific Journal of Computer Science (SJCS) (e-ISSN: 3110-3170) is a peer-reviewed and open-access scientific journal, managed and published by PT. Teknologi Futuristik Indonesia in collaboration with Universitas Qamarul Huda Badaruddin Bagu and Peneliti Teknologi Teknik Indonesia. The SJCS dedicated to publishing high-quality research across all areas of computer science, with a particular focus on emerging technologies that are shaping the future of computing. SJCS invites original research, review papers, and studies that involve practical applications, simulations, and theoretical advancements. The journal scope includes, but is not limited to: Artificial Intelligence and Machine Learning Data Science and Big Data Cybersecurity and Cryptography Cloud Computing and Distributed Systems Software Engineering Human-Computer Interaction Computer Vision and Natural Language Processing Internet of Things (IoT) Blockchain Technologies Robotics and Automation Computational Biology and Bioinformatics All fields related to computer science SJCS aims to advance the development of innovative computing systems that contribute to technological progress across industries.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 24 Documents
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An Intelligent Conversational Agent for Flood Risk Communication in a Flood-Prone Region of Nigeria Ebipamobonumugha, Willie; Onwudebelu, Ugochukwu; Kokogbiya, Efe Darel; Ogoja, Justina
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.411

Abstract

Flooding remains one of the most devastating natural hazards in developing countries, with significant impacts on human lives, infrastructure, and livelihoods. In Nigeria, particularly in Bayelsa State, recurrent flooding events highlight the need for effective and accessible flood risk communication systems. However, existing approaches largely rely on static and non-interactive dissemination channels, limiting timely public engagement and response. This study addresses this gap by designing and implementing a conversational agent capable of providing real-time responses to frequently asked flood-related questions. The proposed system adopts a rule-based conversational framework supported by natural language preprocessing techniques, including tokenization and normalization, for query interpretation. A structured knowledge base containing flood preparedness and response information was developed for the study area. The system was evaluated using a set of 120 representative flood-related queries derived from domain-specific scenarios. Experimental results show that the chatbot achieved a response accuracy of 87.5% and a successful query handling rate of 90.8%. These findings demonstrate the feasibility of conversational agents as effective tools for enhancing flood risk communication and public awareness. The study contributes to the integration of artificial intelligence-driven solutions into disaster risk management and highlights the potential of chatbot systems in improving access to critical environmental information in resource-constrained settings.
Machine Learning-Based Diabetes Classification Using Vital Signs and Clinical Information from the MIMIC-IV Dataset Huynh, Huy; Cao, Thanh; Tran, Hai
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.439

Abstract

Diagnosing diabetes based on clinical data is very important because the number of people with diabetes is growing around the world. The main focus of this study is on using machine learning models to figure out what kind of sickness someone has from a variety of clinical data. The MIMIC-IV dataset was used, which has both structured and unstructured data. The structured data includes vital signs, demographics, and lab tests. The unstructured data includes medical notes, major complaints, and a list of medications. Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine, and XGBoost were some of the models that were tested. Accuracy, Precision, Recall, F1-score, and AUC-ROC were used to measure how well the models worked. When random text data was added to the experiments, the results showed a big improvement in performance: the accuracy increased from approximately 68% to up to 87% across models. The best-performing models achieved AUC-ROC values above 0.95, with Random Forest and XGBoost showing the strongest performance. This shows that semantic mining from clinical notes is a key part of making medical decision support systems more reliable.
Predictive Analytics Model for Adaptive Teaching in Open and Distance Learning Institutions: Machine Learning Approach Adayilo, Danladi Moses; Oyefolahan, Ishaq Oyebisi; Ndunagu, Juliana Ngozi; Anekwe, Nwando; Malcalm, Ebenezer; Twabu, Khanyisile
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.451

Abstract

The study investigates the application of predictive analytics model in adaptive teaching within Open and Distance Learning (ODL) institutions. The aim of the study lies in addressing the ongoing challenges of high dropout rates and low student engagement, particularly in developing countries. The research gap is the underutilisation of predictive analytics to personalise interventions and enhance learning outcomes in ODL environments. The study employs mixed-method research design including machine learning algorithms with Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost, in predicting students at risk of academic failure and providing personalised interventions. A dataset of 5,000 students from the National Open University of Nigeria was used to trained and test the model. Model validation metrics used includes: accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC. More so, (n=1050) participants took part in the experimental and control group including semi-interview, enabling real world application of predictive model. Key findings indicated that Random Forest had the highest ROC-AUC (98.38%), followed by XGBoost (97.76%). Nevertheless, Logistic Regression and SVM outperformed the others in accuracy (97.43%), precision (97.65%), recall (95.95%), and F1-score (96.79%). These results show that adaptive teaching, supported by predictive analytics, is associated with improved student engagement and contributes to reducing dropout rates. The challenges such as data quality, privacy, trust and algorithms bias should be addressed. The study suggest that predictive analytics is capable of transforming teaching methods in ODL institutions, improve personalised and effective learning. Future study should focus on model optimisation and integration with other educational technologies.
Hybrid Neuro-Fuzzy Deep Learning with Genetic Optimization for Explainable Stock Price Forecasting in Emerging Markets Olumide Sunday Adewale; Emmanuel Onwuka Ibam; Johnson Bisi Oluwagbemi
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.377

Abstract

Precise stock price forecasting is vital for economic stability and capital allocation, yet it remains a tenacious challenge in emerging economies due to the inherent uncertainty and non-linearity of financial time series. Despite advances in deep learning, existing models often lack linguistic interpretability, fail to adapt to rapid market shifts, or exhibit look-ahead bias due to static validation splits. Moreover, empirical research focused on African financial systems, such as the Nigerian market, remains sparse, limiting the practical utility of conventional black-box architectures. This study proposes a Hybrid Neuro-Fuzzy and Deep Learning (HNFDL) framework that integrates fuzzy inference systems with Long Short-Term Memory (LSTM) networks and Genetic Algorithms (GA). The objective is to unify semantic reasoning with temporal learning to improve forecasting accuracy while maintaining high model transparency through explainable AI (XAI). Empirical validation using data from the Nigerian Exchange Group (NGX) (Dangote Cement, Zenith Bank, and the NSE All-Share Index) shows that the HNFDL model achieved a directional accuracy of 68.4% and a Mean Absolute Percentage Error (MAPE) as low as 4.36%. An ablation study confirmed that GA-driven optimization reduced the Root Mean Square Error (RMSE) by 8.4%, while the Diebold-Mariano test () statistically confirmed the model's superiority over standalone LSTM and fuzzy baselines. These results demonstrate that combining explainable fuzzy reasoning with adaptive deep neural architectures significantly enhances decision-making confidence. The framework provides a robust, statistically validated decision-support tool for investors and policy makers operating within volatile, information-asymmetric financial environments.

Page 3 of 3 | Total Record : 24

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