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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 191 Documents
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Implementation and design of GPS tracker monitoring system on car rental vehicles based on internet of things using Nodemcu ESP-32 Indah Purnama Sari; Al-Khowarizmi Al-Khowarizmi; Asrar Aspia Manurung
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p214-223

Abstract

Internet of things (IoT) based vehicle tracking system is an effective solution to overcome various problems in the vehicle rental industry, such as asset loss, route misuse, and late returns. This study aims to design and implement a real-time vehicle position monitoring system using the NodeMCU ESP-32 module integrated with the NEO-6M GPS module and Wi-Fi connectivity to send data to a cloud-based server. This system is designed to display the vehicle position directly through a web-based digital map interface, which can be accessed by vehicle owners anytime and anywhere. The methodology used includes hardware and software design, location accuracy testing, and data integration with a web-based visualization platform using a map API. The test results show that the system is capable of sending vehicle location data with a position accuracy level of up to ±5 meters and data updates every 10 seconds under stable network conditions. In addition, the system has good power efficiency, with an average current consumption of 80–100 mA when active. All data was successfully stored and visualized in real-time using the Google Maps API, and the system was able to operate stably for 24 hours of non-stop testing. Based on these results, the IoT-based GPS tracker system with NodeMCU ESP-32 can be effectively implemented on rental vehicles as a modern monitoring solution that is cost-effective, flexible, and easily accessible. This system provides added value in fleet monitoring and supports faster and data-based decision making.
A comparative study of classical, bagging, and hybrid methods for optimizing loan default prediction Ismail Idowu Akuji; Ahmed Babajide Olanrewaju; Taofik Abiodun Ahmed; Ayodeji Jubril Alabi; Idris Babatunde Adeyemi
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p179-195

Abstract

This study optimized loan default prediction by comparing k-nearest neighbor (KNN), random forest (RF), and hybrid methods. The dataset used was preprocessed using simple imputer, label encoder, synthetic minority oversampling technique (SMOTE), and correlation-based feature selection on top seven features while grid search cross-validation (GSCV) and random search cross-validation (RSCV) were employed to optimize models. Before tuning, RF achieved perfect performance (100% accuracy, 99.8% precision, 100% recall, 99.9% F1, 1.000 area under curve (AUC)), outperforming untuned KNN (99.2% accuracy, 96.2% precision, 99.8% recall, 98.0% F1, 0.997 AUC) and hybrid (99.8% accuracy, 99.1% precision, 99.9% recall, 99.5% F1). After tuning, RF maintained same results, confirmed by 10× nested CV stability (F1=0.9997±0.0002) and McNemar tests showing equivalence to RF_RSCV (p=1.0000). KNN improved marginally in precision (96.2%→99.8%) but declined in recall, while hybrid dropped slightly across metrics. Partial dependence plots confirm RF’s dominance stems from three key features (lump_sum_payment, property_value, co-applicant_credit_type), validated by business impact analysis showing minimal errors against KNN/hybrid. RF_GSCV’s perfection reflects true generalization, not overfitting, establishing it as the production-ready gold standard. Future work can address static dataset limitation by incorporating dynamic time-series data with online learning, concept drift detection, and real-time macroeconomic features to enhance real-world generalizability.
Fuzzy logic–based consensus protocol for educational blockchain networks Igor Ivanov; Svetlana Zhdanova
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p131-140

Abstract

This paper addresses the growing challenge of ensuring trust, authenticity, and transparency in the management and verification of educational credentials within modern, digitally oriented learning ecosystems. Rapid expansion of e-learning, lifelong learning, and global mobility has intensified document fraud, revealing the limitations of traditional verification mechanisms. To respond to these systemic risks, the study proposes a socially oriented block-validation protocol integrated into a distributed blockchain environment designed specifically for educational data security. The protocol forms the core of the EduBLOCK system, developed by the authors, and introduces an innovative consensus mechanism that incorporates human-centered reputation assessments rather than computational or financial power. The approach employs fuzzy-set theory to evaluate user activity, institutional credibility, and delegate reputation, enabling a more nuanced and context-sensitive model of trust. Delegates responsible for validating blocks are selected through a dynamic, reputation-driven procedure that excludes financial contributions and subjective parameter tuning. The proposed algorithm combines cryptographic guarantees, peer-to-peer (P2P) communication, and soft-computing methods to ensure fairness, prevent manipulation, and maintain stable system functioning. Block validity is determined through open voting, requiring approval by more than two-thirds of elected delegates.
Complexity of finite state Turing machine with other domain Rajesh Kumar; Anju Jain; Rakesh Kumar
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p196-202

Abstract

In this paper, the authors investigate and discussed the non-deterministic state complexity of certain operations on finite state Turing machine on other domain which includes partial function and natural function over an alphabet set Σ∗. It is found that in some boolean operations on said domains, the state complexity reaches up to upper bound O( √ n!). This result is complement for the operation on Kleene star-free unary and recursive languages accepted by the finite state Turing machine.
A dynamic geofencing and dwell-time validation system for secure attendance tracking in higher education: methodological proposal Michael Favour Edafeajiroke; Amanda Eromosele Ekata
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p159-166

Abstract

Accurate attendance tracking is vital for student engagement and academic integrity, yet traditional methods are prone to error and proxy attendance. While technological solutions like biometrics and QR codes exist, they often suffer from high costs, privacy concerns, and an inability to verify continuous presence. This study proposes a dynamic geolocation-based attendance system to address these gaps. Developed with Flutter and Node.js, the system employs lecturer-defined geofences and a dwell-time validation rule, confirming attendance only if a student remains within the designated area for at least 80% of the class duration. It features cross-platform accessibility, role-based dashboards, real-time notifications, and exportable reports. The methodology followed an Agile approach, focusing on user-centered design and robust backend development. The resulting system offers a cost-effective, scalable solution that enhances accuracy, prevents proxy attendance, and supports the digital transformation of higher education administration.
Tracking a person and determining the location by using convolutional neural network technology Zinah Shiker Makki; Ahmet Zengin
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p203-213

Abstract

Tracking individuals in real-world environments requires robust, non-intrusive methods that overcome the limitations of device-based systems. This study proposes a convolutional neural network (CNN)-driven person-tracking framework that identifies targeted individuals directly from camera feeds, eliminating the need for wearable or global positioning system (GPS) devices and addressing a major drawback of traditional tracking technologies. The system utilizes a TensorFlow-trained CNN model that can detect, recognize, and locate persons of interest in real-time, even under varying illumination conditions. Unlike conventional approaches, our method integrates facial feature extraction with encrypted identity management, enabling secure multi-person detection and rapid location reporting. Experimental results demonstrate a 92% accuracy in low-light settings and 100% accuracy under normal lighting, confirming the system’s effectiveness for security-oriented applications. The findings highlight the novelty of combining lightweight CNN architecture, real-time facial recognition, and hash-based identity protection within a unified tracking pipeline.
Performance evaluation of the deep learning system for weed recognization Abd Abrahim Mosslah; Reyadh Hazim Mahdi; Hassan Kassim Albahadily
Computer Science and Information Technologies Vol 7, No 2: July 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i2.p167-178

Abstract

Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilization of deep neural networks (DNNs) to differentiate between various weed types in actual events scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design the robust deep learning system. To simplify such process and conserve resources, researchers have explored a fresh method known as automated deep learning our technology’s recognization of weeds through the use of machine learning was evaluated using plant seedlings and weed collections from plants dataset to address a issue of weed recognization. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training with datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align together with automated machine learning (AutoML-linked) studies, while fall short of manually fine-tuned deep-learning-based systems created through human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.
Convolutional neural network and long short-term memory forecasting and variational autoencoder anomaly detection in 4G cellular networks Ruvarashe C. Hove; Eng Mainford Mutandavari
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p256-270

Abstract

Spectrum monitoring in cellular networks with limited resources remains predominantly reactive because capacity planning and failure detection occur after network congestion, while spectrum monitoring data are rarely available for reproducible research. Existing studies address short-term traffic forecasting and anomaly detection as separate tasks and largely depend on densely sampled operator telemetry data that are inaccessible to academia and regulators in emerging markets. This study integrates both tasks into a unified deep-learning pipeline for a 4G cellular environment. The Zimbabwe spectrum dataset contains 315,247 hourly measurements collected from 13 cell sites, three operators, and five frequency bands, annotated with four operator-defined anomaly classes representing 2.03% of all measurements. A hybrid one-dimensional (1-D) convolutional neural network-long short-term memory (CNN–LSTM) model uses a 72-hour traffic window to forecast the next six hours of aggregate network traffic, while a variational autoencoder (VAE) trained exclusively on normal records detects anomalies when reconstruction error exceeds the 99th-percentile validation threshold. On the held-out test set, the model achieved a mean absolute error (MAE) of 158.97 GB, root mean square error (RMSE) of 230.41 GB, and mean absolute percentage error (MAPE) of 52.93%, outperforming a seasonal-naive baseline (MAE=216.43 GB, MAPE=66.77%, p0.001, Diebold-Mariano test). The study contributes a publicly available localized synthetic dataset, a reproducible end-to-end forecasting and anomaly detection baseline, and a per-class anomaly analysis.
Hybrid geostatistical and machine learning for gold grade estimation Tanyaradzwa Miriam Mtetwa; Monika Gondo
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p394-403

Abstract

The evaluation of ore grade is a basic part of the process of mine planning, production scheduling and resource evaluation. The nugget effect and irregular mineralisation that occurs in some greenstone belt deposits on the Zimbabwean Archean causes problems in estimating accurately. The traditional geostatistical models like Ordinary Kriging (OK) produce smoothed estimates which have systematic underestimation in high-grade areas and overestimation in low-grade areas, thus affecting the deposit selectivity. In addition, machine learning (ML) techniques can capture the more complex nonlinear grade relationships, but they are unable to model spatial continuity and can be unrealistic in predicting grades. The hybrid OK-Gradient Boosting (GB) model was designed and evaluated with the drill-hole data of a gold mine in Zimbabwe. The spatial baseline was produced using OK and GB was trained to correct the systematic residual errors. The hybrid model was found to have the maximum overall explanatory power (R²=0.8795), competitive prediction accuracy and increased spatial realism. A spatial prediction map and extraction priority zone classification was created to aid operational mine planning.
Risk-integrated contractor allocation in Zimbabwe’s timber value chain Tavengwa Norman; Brian Mupini
Computer Science and Information Technologies Vol 7, No 3: November 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i3.p337-345

Abstract

In Zimbabwe, the commercial forestry industry is reliant on a large proportion of outsourced harvest and milling contractors whereas existing enterprise resource planning (ERP) systems record completed transactions instead of predicting contractor failure before assigning activities. We introduce the dynamic resource allocation framework (DRAF), a risk-integrated decision-support framework that supplements the probability of contractor failure derived from XGBoost to a non-dominated sorting genetic algorithm II (NSGA-II) multi-objective optimizer. It divides contractor-block-mill combinations, minimizes cost and expected delay, and maximizes risk-adjusted timber recovery and operational reliability. We created the solution on an 828,789-record virtual ERP dataset and calibrated it to Manicaland forestry conditions and tested with statistical, heuristic and risk-free optimization baselines. Extreme gradient boosting (XGBoost) obtained a holdout receiver operating characteristic area under the curve (ROC-AUC) of 0.965, recall of 0.999, and F1 of 0.867, showing an improvement of 0.365 over logistic regression. The optimizer developed 64 complete Pareto solutions to balanced and high-recovery scenarios and uncovered a constraint-feasibility boundary for a more conservative low-risk scenario. The satisfaction score of the 30-practitioner stakeholder assessment was 4.19 out of 5.0. The results demonstrate that embedding predictive risk into an optimization objective can optimize forestry allocation decisions and suggest that some real ERP validation is required before such measures will be broadly implemented.

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