cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
-
Phone
-
Journal Mail Official
ij.aptikom@gmail.com
Editorial Address
9th Floor, 4th UAD Campus Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Ahmad Dahlan
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 10 Documents
 1 
Search results for , issue "vol 7, no 2: july 2026" : 10 Documents clear
Optimizing water distribution in Harare, Zimbabwe using IoT and cloud computing Angeline Tsatsa; Tinashe Butsa; Yolanda Chibaya
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.p231-240

Abstract

Rapid urbanization in Harare, Zimbabwe, has intensified inefficiencies in water distribution, resulting in high non-revenue water (NRW) and inequitable supply. This paper presents a novel data-driven framework that integrates internet of things (IoT) sensors, machine learning (ML), and cloud computing to optimize urban water distribution. Historical and real-time data including water flow, pressure, and consumption are collected via IoT sensors and analyzed using a random forest model for accurate demand forecasting and anomaly detection, such as leaks. The model is deployed on a secure cloud-based ASP.NET platform, enabling real-time monitoring and automated valve control through ultrasonic sensors over Wi-Fi. Evaluation demonstrates superior performance with R²=0.89 for demand forecasting and anomaly detection metrics of 94% accuracy, 91% precision, 92% recall, and 91% F1-score, outperforming baseline methods. This integrated system reduces water loss, improves supply equity, and provides a scalable and cost-effective approach for smart water management in resource-constrained urban settings. The framework offers practical insights for policymakers and utilities seeking to implement sustainable, technology-driven water management solutions in developing cities.
Improving Botnet host prediction with encryption and GRU for enhanced network security Omega Joel Patria Moata; Irwansyah Saputra
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.p141-158

Abstract

This paper examines the challenges of reliably and securely predicting Botnet hosts, a crucial aspect of network security. Existing Botnet detection systems often fail to address data privacy concerns and struggle with evolving attack methods. This study proposes an innovative approach to improve the security and accuracy of Botnet host prediction by integrating deep learning with encryption. The proposed method employs encryption techniques such as data encryption standard (DES) and blum-blum-shub (BBS) to protect sensitive data in a text data set of 2,100 IP addresses, consisting of Botnet hosts and benign hosts. Several pre-processing techniques, including moving average and missing value handling, are implemented to optimize the model performance. The effectiveness of the system is evaluated using performance metrics such as F1-score, recall, accuracy, and precision. Experimental results show that the proposed approach significantly outperforms existing methods in accuracy, which have not achieved the maximum accuracy per IP Host within a given time frame, while providing enhanced security through encryption on text data. The study concludes that combining deep learning with encryption on text data offers a promising solution for reliable and secure Botnet host prediction data. Future research will focus on testing larger and more diverse data sets, as well as analyzing the impact of different encryption techniques on the overall accuracy and security of the system.
A novel approach for real-time traffic sign recognition framework Kshatrapal Singh
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.p224-230

Abstract

Traffic sign recognition plays a critical role in enhancing road safety and enabling autonomous driving systems. This paper presents a comprehensive approach to real-time traffic sign recognition using advanced computer vision techniques and machine learning models. The proposed system employs convolutional neural networks (CNNs) for accurate detection and classification of traffic signs under diverse environmental conditions, including varying lighting, weather, and occlusions. Real-time processing is achieved through the integration of optimized algorithms and hardware acceleration techniques, ensuring minimal latency and high throughput. Experimental results demonstrate that the system achieves state-of-the-art performance on benchmark datasets, with an accuracy of over 95% and a recognition speed suitable for real-world applications. The findings underscore the potential of the system to improve driver assistance systems and pave the way for safer autonomous vehicles.
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.

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 7, No 3: November 2026 Vol 7, No 2: July 2026 Vol 7, No 1: March 2026 Vol 6, No 3: November 2025 Vol 6, No 2: July 2025 Vol 6, No 1: March 2025 Vol 5, No 3: November 2024 Vol 5, No 2: July 2024 Vol 5, No 1: March 2024 Vol 4, No 3: November 2023 Vol 4, No 2: July 2023 Vol 4, No 1: March 2023 Vol 3, No 3: November 2022 Vol 3, No 2: July 2022 Vol 3, No 1: March 2022 Vol 2, No 3: November 2021 Vol 2, No 2: July 2021 Vol 2, No 1: March 2021 Vol 1, No 3: November 2020 Vol 1, No 2: July 2020 Vol 1, No 1: May 2020 List of Accepted Papers (with minor revisions) More Issue