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Journal of Technology and System Information
Published by Indonesian Journal Publisher
ISSN : -     EISSN : 30322081     DOI : https://doi.org/10.47134/jtsi
Core Subject : Science,
Computer Science & IT
The Journal of Technology and System Information is dedicated to publishing cutting-edge research and advancements in the broad and dynamic intersection of technology and information systems. The focus of the journal is to facilitate the exchange of knowledge and ideas in these interconnected domains, fostering a deeper understanding of the role of technology in shaping information systems and vice versa. The journal welcomes contributions that span theoretical, empirical, and practical aspects, with an emphasis on the transformative impact of technology on information systems and vice versa. The scope of JTSI is a Information Technology and Systems, Data Management and Analytics, Emerging Technologies, System Design and Optimization, Cybersecurity and Privacy, Networks and Communication Systems, Artificial Intelligence and Machine Learning, Human-Computer Interaction.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 4 (2025): October" : 5 Documents clear
Enhancing Data Integrity in Wireless Sensor Networks Using a Base-Station Controlled Clustering Protocol Khalid Khalis Ibrahim
Journal of Technology and System Information Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v2i4.4891

Abstract

Wireless Sensor Networks (WSNs) are increasingly used in applications involving environmental monitoring, military applications, and automation in industries. Nonetheless, the networks continue to experience challenges in providing data integrity and network lifetime in situations of resource constraint and security attack. In this study, a new protocol is proposed using BaseStation Controlled Dynamic Clustering Protocol (BCDCP) with Identity-Based Aggregate Signatures (IBAS). The protocol helps the Base Station (BS) choose the best Cluster Heads (CHs) and assign signature aggregation responsibilities to the Deputy Cluster Heads (DCHs), hence balancing the consumption of energy and reducing the communication overhead. The model has been tested using Network Simulator 2 (NS-2) and compared with the typical BCDCP and the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols. From the simulation results, the proposed scheme is found to reduce authentication overhead by a factor of 25%, improve the Packet Delivery Ratio (PDR) by a factor of up to 30%, and improve the entire network lifetime by a factor of up to 20%. These results illustrate the superiority of the proposed model in the reduction of security and improvement in the efficiency of WSNs in terms of energy consumption
Perancangan UI/UX Aplikasi Debt Note dengan Metode Design Thinking Islamy, Muhammad Khairul Faizi; Afada, Faris; Adristiawan, Ranu Arva; Saputro, Indrawan Ady
Journal of Technology and System Information Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v2i4.4905

Abstract

The rapid development of technology has encouraged digitalization in various aspects of life, including in the case of financial records. One of the problems that is often faced is debt recording which is still traditionally carried out using paper media, which is vulnerable to damage, loss, and manipulation of data. To answer this challenge, the Debt Note application is designed to make it easier to record debts digitally. The design of this application uses a method called Design Thinking which includes five stages, namely Empathize, Define, Ideate, Prototype, and Testing. This research aims to develop an easy-to-use application, especially for store owners, in order to manage debt data better and efficiently. The results of the feasibility test using the System Usability Scale (SUS) method show an average score of 90, which is included in the Excellent category, reflecting a high positive response from users to the application. The Debt Note application provides various features such as debt recording, repayment, and mobile-based data management with an intuitive and efficient interface design. The designer hopes that this application can be implemented in various areas of the store and can reduce the level of loss and damage in a debt recording data that causes quarrels between store owners and buyers.
Multi-classification for Predicting Alzheimer's Disease Using 1.5T1-weighted MRI Imaging and Deep Learning 2D CNN Shahad Haitham Ali, Shahad
Journal of Technology and System Information Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v2i4.4987

Abstract

Medical imaging holds the pivotal role in clinical diagnosis, education, research work, and treatment of medicine. Medical professionals are tasked with analyzing and interpreting high-level medical data, which is extremely difficult in nature due to the intricate nature of medical images. Deep learning techniques are emerging as strong tools, yielding promising and correct results in the analysis of medical data. Alzheimer's disease, which affects one in every ten individuals aged 65 and above, is a central focus where such advancements are aimed. Artificial intelligence has proved capable of distinguishing between healthy brains and Alzheimer's disease-affected brains. The etiology of the disease lies in abnormal proteins accumulating inside and outside neuronal cells, causing irreparable loss of memory. Alzheimer's disease is the most prevalent form of dementia, and "Mild Cognitive Impairment" (MCI) typically presents as an early indicator, identifying patients who are at higher risk of Alzheimer's disease. However, not all MCI patients go on to develop Alzheimer's, and this highlights the importance of effective interventions. While some patients with MCI (MCI-nc) remain stable, others will progress to Alzheimer's disease. Here, a CNN model was designed and trained first with four classifiers and later retrained with five classifiers. The accuracy rate of the four-classifier model was 98%, while that of the five-classifier model was slightly higher with an accuracy of 98.67%.
Detection of DDOS Attacks in Software-Based Systems in Cyberspace Using Machine Learning Dolmaz, Zeynep; Cinar, Ilkay
Journal of Technology and System Information Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v2i4.5033

Abstract

Distributed Denial of Service (DDoS) attacks have emerged as one of the most critical threats to contemporary network security. Rapid and accurate detection of such attacks is major for ensuring service continuity in large-scale networks. This study proposes an integrated approach that combines feature engineering with machine learning algorithms for the detection of DDoS attacks. In the initial phase, ANOVA and Chi-Square tests were applied to the dataset to identify statistically significant features; attributes such as dt, switch, dur, bytecount, and pktcount, which contributed minimally to classification performance or contained redundant information, were excluded. The optimized feature set was then evaluated using several machine learning algorithms, namely Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR). Quantitatively, feature selection improved SVM accuracy from 74.88% to 95.05%, increased Decision Tree accuracy to nearly 99.94%, slightly reduced KNN performance while maintaining its overall strength, and decreased LR accuracy from 77.15% to 74.87%. The experimental findings demonstrate that the proposed approach not only enhances classification performance but also reduces model runtime. Accordingly, the study presents an effective solution that simultaneously delivers high accuracy and computational efficiency in DDoS detection.
A Lightweight Spiking Neural Network Model for Real-Time Brain Signal Classification Using Open EEG Datasets Saleh, Worud Mahdi; Hawi, Ibtesam Jomaa; Hasan, Marwa Falah; Abd Ali, Samar Khalil Ibrahim; Fadhel Hussein, Marwa Ibrahim
Journal of Technology and System Information Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v2i4.5110

Abstract

To classify EEG signals in real time, a lightweight SNN was built and evaluated. The work showed that it is possible to use energy-efficient, bio-inspired neural computer models on BCI devices using open-source EEG data. The preliminary results indicate that the proposed system's accuracy and speed are promising for implementation on a portable, low-power device. Due to their event-based computing paradigm and temporal coding feature, spiking neural networks (SNNs) have been gaining popularity in brain signal processing. A biologically plausible and efficient implementation of an SNN model was presented for the classification of EEG signals with an application to motor imagery tasks. The model proposed utilized the hybrid coding and attention mechanism to extract the spatiotemporal features in the EEG data and select the relevant features. High classification accuracy, low inference latency, and satisfactory cross-subject generalization performance were achieved by the model in large-scale experiments using publicly available EEG datasets. The results achieved validate the potential of SNNs as a promising alternative to conventional NNs for BCI applications. This result is a significant advancement in low-power, real-time neural decoding systems and opens the door for future generations of neuromorphic computing applications in the biomedical domain.

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