JUTI: Jurnal Ilmiah Teknologi Informasi (Scientific Journal of Information technology) is focusing on publishing original research, communication, and notes as a result of original and high quality research. The journal also invites well known researchers to write a review paper. JUTI accepts high quality papers in the field of engineering, science, and technology. The scope of JUTI encompasses but not limited to the following: Information Technology Computer Science Information System Computer Network Software Engineering Computer Vision JUTI: Jurnal Ilmiah Teknologi Informasi (Scientific Journal of Information Technology) is a scientific journal published by the Department of Informatics, Institut Teknologi Sepuluh Nopember. The journal is published biannually (January and July) and is available in an open-access electronic version. The paper template can be downloaded here. We now also provide a Typst template to facilitate authors in preparing their manuscripts with a structured and professional format. You can start a new Typst project using the template here. If you prepare your manuscript using the Typst template, please submit both the PDF file and the Typst source file. Publication Fees JUTI is fully open-access and free of charge, ensuring that authors can publish their research without any submission or publication fees. We are committed to supporting the academic community by providing a platform for high-quality research dissemination without financial barriers. All accepted papers will be freely accessible to readers worldwide, promoting knowledge sharing and scholarly impact. Accreditation JUTI has been re-accredited by the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia with the decree No. 21/E/KTP/2016, dated July 9, 2016 as Sinta-3 started from Volume 14 Number 1 (2016). The accreditation has been renewed through decree No. 164/E/KPT/2021, dated December 27, 2021. Indexing & Abstract JUTI has been indexed by DOAJ, Cross Ref, Google Scholar, Sinta, Garuda (Garda Rujukan Digital), Dimensions, and Scilit.
Department of Informatics, Institut Teknologi Sepuluh Nopember
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in JUTI unless they receive approval for doing so from the Editor-in-Chief. JUTI open access articles are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
Decision Support System for Determining Strategic Warehouse Locations Using a Combination of the WENSLO Weighting and RAWEC Method
Determining the location of a strategic warehouse is a crucial decision in supply chain management as it directly affects distribution efficiency, logistics costs, and service levels. This problem is multi-criteria and complex, requiring an approach that can accommodate differences in the importance of criteria as well as variations in performance among alternatives objectively. This study aims to develop a Decision Support System to determine a strategic warehouse location by combining the Weights by Envelope and Slope (WENSLO) weighting method and the Ranking of Alternatives with Weights of Criterion (RAWEC) ranking method. The WENSLO method is used to generate criteria weights based on the nonlinear strength of each criterion, while the RAWEC method is applied to calculate the final values and determine the ranking of warehouse location alternatives. A case study was conducted on eleven alternative locations with the main criteria including location cost, accessibility, safety, distribution travel time, and proximity to suppliers. The study results showed that Location TR obtained the highest final score of 0.9673 and was designated as the top priority warehouse location, followed by Location RD with a score of 0.6235 and Location HO with a score of 0.338, while Location QC had the lowest score of −0.975. These findings demonstrate that the combination of the WENSLO and RAWEC methods can produce rankings that are objective, consistent, and easy to interpret, making them a reliable decision-support tool for determining strategic warehouse locations and potentially applicable to other logistics and distribution problems.
Copyright (c) 2026 Junhai Wang, Setiawansyah Setiawansyah, Temi Ardiansah, Faruk Ulum, Sumanto Sumanto https://creativecommons.org/licenses/by-sa/4.0
Multi-task Temporal Deep Learning Model for Real Time Intrusion Detection System
The rapid expansion of Internet of Things (IoT) ecosystems has enabled large-scale interconnected smart environments while simultaneously exposing IoT devices to increasingly sophisticated cyber threats. To address these challenges, machine learning and deep learning–based intrusion detection systems (IDS) have been widely adopted; however, many existing approaches suffer from limited generalization, insufficient temporal modeling, and poor performance under extreme class imbalance. In this study, we investigate a multi-task stacked Long Short-Term Memory (LSTM) architecture for IoT intrusion detection, where binary anomaly detection and multi-class attack classification are jointly learned within a unified temporal framework. The proposed model examines different inter-path knowledge transfer mechanisms, including additive, gated, and attention-based aggregation, to enhance discriminative attack representation learning. A topology-constrained shuffling strategy is further introduced to preserve intra-flow temporal dependencies while reducing reliance on fixed traffic ordering. Experimental results on the Edge-IIoTset dataset show that all models achieve high binary detection performance (F1-score above 97%), while attention-based aggregation consistently outperforms static fusion strategies for multi-class classification, yielding superior macro F1-score and AUC-PR under severe class imbalance. These findings emphasize the importance of context-aware information sharing and temporal structure preservation for robust and adaptive IoT intrusion detection systems.
Copyright (c) 2026 Christian Budhi Sabdana, Noriandini Dewi Salyasari, Izra Noor Zahara Aliya, Ary Mazharuddin Shiddiqi https://creativecommons.org/licenses/by-sa/4.0
Performance Evaluation of DNA-Based Cryptographic Algorithms on Constrained IoT Devices
DNA-based cryptographic techniques have attracted significant interest due to their intrinsic parallelism, high algorithmic complexity, and bio-inspired randomness. However, their practical applicability in resource-constrained Internet of Things (IoT) environments remains insufficiently explored. This study presents a comprehensive performance evaluation of six representative DNA-based encryption schemes—DNA-XOR-Mutation, DNA-Substitution-Shift, Hybrid DNA-Logical Encoding, DNA-Crossover-Encode, DNA-Logical-Shift, and DNA-Hybrid-Crypt—implemented and experimentally measured on embedded platforms typical of IoT devices. These schemes were benchmarked against established lightweight cryptographic algorithms, including PRESENT-80, ASCON-128, SPECK-64, TWINE-80, HIGHT, SIMON-64/128, and LED-64, using an experimental measurement environment configured to reflect the specifications of widely deployed microcontrollers such as ATmega328P, STM32F0, ESP32, nRF52840, PIC24FJ64GA, and MSP430. Performance metrics encompassed execution time, ROM/RAM memory footprint, and energy consumption. The results indicate that while DNA-based algorithms generally demand greater memory resources and exhibit higher latency than hardware-optimized lightweight ciphers, they demonstrate superior diffusion properties and enhanced resistance against classical differential cryptanalysis. These findings highlight the promise of DNA-inspired cryptography as a complementary security mechanism for next-generation IoT systems, particularly in scenarios requiring polymorphic or non-deterministic encryption approaches. Finally, we discuss optimization strategies and hardware integration considerations, offering a performance-driven foundation for further research into DNA-based cryptographic primitives within IoT security frameworks.
Copyright (c) 2026 Mircea Ţălu https://creativecommons.org/licenses/by-sa/4.0
Enhancing the Quality of Merged Process Models by Addressing Invisible Task
Model merging is a key approach for integrating multiple process model variants into a unified representation. Existing automated merging methods face challenges in handling invisible tasks, which are intentionally inserted in the process model to depict certain conditions, including stacked branching relationships. The inability to handle invisible tasks reduces the quality of the merged process models. A proposed graph-merging method explicitly addresses sequence, branching relationships, and invisible tasks. The proposed method first identifies common activities across model variants. Furthermore, the method applies the proposed graph rules grounded in behavioral and structural aspects to combine those common activities as well as their related relationships and generate the graph-based merged process model. Behavioral rules govern the integration of sequence and branching relationships, while structural rules handle branching and invisible tasks. An evaluation against two existing approaches by Derguech and Yohanes demonstrates that the proposed graph-merging method achieves higher precision. The graph-merging method substantially improves the quality of merged process models.
Copyright (c) 2026 Kelly Rossa Sungkono, Riyanarto Sarno, I Gusti Agung Chintya Prema Dewi, Muhammad Suzuri Hitam https://creativecommons.org/licenses/by-sa/4.0
The Role of Advanced Penetration Testing Techniques in Enhancing Cybersecurity: A Survey on Web Application Security
The Internet continues to grow as an exciting platform for numerous web applications, providing crucial services to various industries and user communities. However, this expansion comes with an increase in cybersecurity threats, as web applications continue to be a primary target for malicious entities. Despite the creation of numerous security frameworks, yearly reports and the OWASP (Open Web Application Security Project) Top 10 consistently highlight the ongoing presence of severe vulnerabilities in contemporary web platforms. This paper examines the significance of advanced penetration testing methods in enhancing cybersecurity, particularly in the context of web application security. Utilizing a combination of manual and automated testing approaches, incorporating tools such as BURP SUITE and METASPLOIT, this study examines how sophisticated penetration testing can uncover, exploit, and address vulnerabilities, including SQL injections, cross-site scripting (XSS), and weak authentication methods. The results highlight that even small vulnerabilities can have significant practical impacts, emphasizing the importance of ongoing, intelligent testing approaches.
Copyright (c) 2026 Emmanuel Bugingo, Voltaire Ishimwe, Ghislaine Uwase Simbi, Adeline Dusenge, Jean Baptiste Nizeyimana https://creativecommons.org/licenses/by-sa/4.0
A Comparative Study Evaluation of Kafka and RabbitMQ: Performance, Scalability and Stress Test in Distributed Messaging Systems
The two most widely used Message-Oriented Middleware (MOM) technologies are Apache Kafka and RabbitMQ, both of which have fundamental differences in terms of architecture and performance characteristics. Kafka is designed for high-throughput and good scalability data stream processing, while RabbitMQ excels in message routing flexibility, delivery reliability, and complex queue management. This study presents a comprehensive comparative analysis of two leading message brokers, namely Apache Kafka and RabbitMQ to evaluate performance, scalability, and behaviour under stress test, for the selection of the most suitable message broker in modern distributed system architectures. The experimental testing process was carried out in four different scenarios: message size variation of 1 KB, 10 KB and 100 KB aimed at measuring performance based on payload size, message volume variation of 10,000, 50,000 and 100,000 messages to see throughput limits and resource usage, consumer number variation of 1, 5 and 10 Measuring the scalability of the consumer system, then a high-intensity pressure test of 100,000 messages in 10 seconds to evaluate the stability and latency of the overload. Key performance metrics, such as throughput, latency, CPU usage, and RAM consumption are carefully evaluated. The overall results of the experiment were more suitable for systems that affect the speed and volume of messages, while Kafka was more appropriate for extreme workloads with high durability requirements. This experiment provided empirical data concluding that RabbitMQ is highly effective for applications that require sending high-volume, low-latency individual messages, while Kafka's strength lies in handling specific data stream sizes and maintaining stability under intense and sustained loads.
Copyright (c) 2026 Muhammad Rias Ramadan, Ach Muhyil Umam, Anani Asmani, Royyana Muslim Ijtihadie https://creativecommons.org/licenses/by-sa/4.0
Exploring The Effectiveness of In-Context Methods in Human-Aligned Large Language Models Across Languages
Most of past studies about in-context methods like in-context learning (ICL), cross-lingual ICL (X-ICL), and in-context alignment (ICA) come from older, unaligned large language models (LLMs). However, modern human-aligned LLMs are different; they come with chat-style prompt templates, are extensively human-aligned, and cover many more languages. We re-examined these in-context techniques using two recent, human-aligned multilingual LLMs. Our study covered 20 languages from seven different language families, representing high, mid, and low-resource levels. We tested how well these methods generalized using two tasks: topic classification (SIB-200) and machine reading comprehension (Belebele). We found that utilizing prompt templates significantly improves the performance of both ICL and X-ICL. Furthermore, ICA proves particularly effective for mid- and low-resource languages, boosting their f1-score by up to 6.1%. For X-ICL, choosing a source language that is linguistically similar to the target language, rather than defaulting to English, can lead to substantial gains, with improvements reaching up to 21.98%. Semantically similar ICL examples continue to be highly relevant for human-aligned LLMs, providing up to a 31.42% advantage over static examples. However, this gain decreases when using machine translation model to translate query from target language. These results collectively suggest that while modern human-aligned LLMs definitely benefit from in-context information, the extent of these gains is highly dependent on careful prompt design, the language's resource level, language pairing, and the overall complexity of the task.
Copyright (c) 2025 Ubaidillah Ariq Prathama, Ayu Purwarianti, Samuel Cahyawijaya https://creativecommons.org/licenses/by-sa/4.0
Handling Ambiguity in App Review-Based Software Requirement Classification Using Multi-Label BERT Transfer Learning
User-generated reviews on mobile applications represent a valuable yet ambiguous resource for classifying software requirements, particularly when multiple aspects—such as bugs, feature requests, and user experiences—are embedded within a single review. Although prior studies have shown the potential of transformer-based and multi-label models in improving text classification accuracy and efficiency, explicit handling of semantic ambiguity in multi-aspect reviews has not been addressed. This study proposes a multi-label classification approach using BERT-based transfer learning to manage ambiguity in app reviews. Each review is manually annotated with one or more relevant requirement categories. Preprocessing involves text cleaning, normalization, and BERT tokenization to convert reviews into structured representations. The classification model categorizes reviews into four classes: bug reports, feature requests, user experiences, and ratings. Evaluation results demonstrate strong performance, with F1-scores of 0.96 for bug reports, 0.95 for feature requests, 0.97 for ratings, and 0.80 for user experiences, confirming the model’s capability in capturing overlapping labels in ambiguous reviews. This approach offers a scalable and automated solution for extracting software requirements, enabling developers to better identify, categorize, and prioritize user needs from unstructured review data.
Copyright (c) 2026 Stefani Tasya Hallatu, Muhammad Jerino Gorter, Andrea Bemantoro Jati, Diana Purwitasari, Chastine Fatichah, Hilya Tsaniya https://creativecommons.org/licenses/by-sa/4.0
Explainable BERT Embeddings for Veracity Assessment in Criminal Investigations
The binary classification of truth and lies is often a detriment in criminal investigations as statements are intentionally not entirely true nor entirely false. This ambiguity in the veracity of their claims demands more extensive methods such as explainable models. Explainable models, particularly SHapley Additive exPlanations (SHAP), can help dissect statements and narrow down information for a more thorough investigation. Data from the Miami University Deception Database, comprising of various statements and their veracity, was analyzed for its linguistic features. This research utilizes Bidirectional Encoder Representations from Transformers (BERT) Embeddings to provide contextual understanding of statements and Sentiment Lexicons to provide domain specific knowledge. Results show that the R² (coefficient of determination) of the 2-Gram embedding performed the best at 0.39 by being able to capture more context than the 1-Gram embedding while being more general than the 3-Gram and 4-Gram embeddings. Each variant of the BERT Embedding was proven to be much more effective than general word embedding such as GloVe, Word2Vec and FastText. SHAP values were able to capture key points of interest in a statement by narrowing down pivotal and decision-making points. These results highlight potential indicators of either deceptive or truthful language such as the word ‘something’ and ‘our’. These points of interest can help humans focus on key points of investigation and intervention.
Copyright (c) 2026 Thoha Ikhwanul Haq, Chastine Fatichah, Anny Yuniarti https://creativecommons.org/licenses/by-sa/4.0
SentiBERT and Enhanced Bi-GRU for Weather-related Text Classification Using Lexical Features
The growing volume of weather related content on social media platforms, especially Twitter, has highlighted the need for robust classification models that can handle noisy, ambiguous, and emotionally subtle language. However, existing models machine learning such as Support Vector Machines (SVM) often fail to effectively capture implicit sentiment and sequential context in short, real time texts. This study addresses the challenge of weather related text classification by proposing a hybrid architecture that combines SentiBERT, a sentiment aware transformer model, with an Enhanced BiGRU network equipped with Self Attention and LeakyReLU activation. Experiments were conducted using a five class(sunny, cloudy, rainy, extreme, other) dataset of weather related tweets with stratified cross validation across multiple deep learning models and tokenizers. Results show that the proposed SentiBERT + Enhanced BiGRU model outperformed all baselines, achieving 88.03% accuracy and 88.25% macro F1 score demonstrating its ability to better interpret contextual and emotional nuances. These findings imply that integrating sentiment specific embeddings with sequential modeling and lexical features offers a promising direction for future real time applications in climate monitoring and disaster alert systems.
Copyright (c) 2026 Mohamad Anwar Syaefudin, Arijal Ibnu Jati, Hilya Tsaniya, Chastine Fatichah, Diana Purwitasari https://creativecommons.org/licenses/by-sa/4.0
Optimized Closed Frequent High Utility Itemset Mining Using OSR, OWL, and MSU Pruning on Retail Transaction Data
This research proposes the optimization of the Frequent Closed High-Utility Itemset Mining (FCHUIM) algorithm for retail transaction datasets using heuristic-based pruning techniques, Observed Support Ratio (OSR), Observed Weighted Lift (OWL), and Modified Subtree Utility (MSU). The algorithm aims to efficiently extract high-value itemsets that are both frequent and economically significant while minimizing redundant patterns through closed itemset mining. A real-world retail dataset from a consumer cooperative, comprising 56,274 transactions and 4,265 unique items, was used in the experiments. The study evaluates the effectiveness of each pruning technique, individually and in combination, across multiple scenarios of minimum support and utility thresholds. Results show that the proposed optimizations reduce the search space by up to 92.5%, significantly lowering execution time and memory usage. Sensitivity analyses reveal that the minimum utility parameter has a stronger impact on computational efficiency than minimum support, while scalability tests confirm the algorithm's ability to handle increasing dataset sizes with linear performance degradation. These findings confirm that the optimized FCHUIM algorithm is suitable for large-scale retail data mining applications, especially in scenarios requiring fast and concise pattern extraction. Future work may explore real-time integration into recommendation systems and adaptive thresholding for dynamic retail environments.
Copyright (c) 2026 Kinana Syah Sulanjari, Chastine Fatichah https://creativecommons.org/licenses/by-sa/4.0
Gambling Comments Detection on Youtube: A Comparative Study of Tree-Based Boosting, LSTM and GRU Models
The exponential growth of online gambling in Indonesia poses significant socio-economic challenges, particularly affecting vulnerable populations through sophisticated digital marketing strategies targeting social media platforms. This study addresses the critical need for automated detection systems to identify gambling-related content in YouTube comments. We scraped and manually labeled 11,673 comments from diverse YouTube videos, creating an extremely imbalanced dataset with gambling comments representing only 10% of the total data. Multiple machine learning approaches were developed and evaluated, comparing traditional gradient boosting methods (LightGBM, XGBoost, CatBoost) using TF-IDF features against deep learning models (LSTM & GRU) with Word2Vec embeddings. The experimental results demonstrate that gradient boosting methods significantly outperform deep learning approaches in generalization capability. LightGBM achieved the highest holdout F1-score with balanced precision (0.8912) and recall (0.8886), while XGBoost followed closely with comparable performance. In contrast, deep learning models exhibited severe overfitting, with GRU and LSTM showing excellent test performance but drastically reduced holdout recall (0.5022 and 0.4844, respectively). The findings indicate that the dataset size was insufficient for deep learning approaches to learn generalizable representations effectively. For practical deployment in YouTube gambling content detection, gradient boosting methods are recommended due to their superior performance with limited, imbalanced datasets.
Copyright (c) 2025 Agung Widiyanto, Mayesq Prameswari, Muhammad Abdul Latief https://creativecommons.org/licenses/by-sa/4.0
Topic Modeling for Constructing Learning Profiles Using LDA and Coherence Evaluation
Understanding individual learning patterns is important for supporting effective learning strategies in the digital education ecosystem. This study proposes a topic modeling approach using the Latent Dirichlet Allocation (LDA) algorithm to form learning profiles based on student interaction data from EdNet-KT1. The dataset includes 153,824 interactions with 11,613 questions, which were converted into semantic tag-based pseudotexts. Modeling was performed with 20 topics, which were selected as a compromise between semantic quality (coherence score 0.6688) and model readability, although the highest coherence score appeared with a larger number of topics. Each question is linked to a dominant topic, and student accuracy is calculated to form a student-topic performance matrix. The results of the analysis show that 66% of students mastered more than five topics, reflecting a broad range of knowledge. Visualization with heat maps, radar charts, and line charts provides a detailed overview of each individual's strengths and weaknesses. Segmentation was performed using the K-Means algorithm and produced four clusters based on student performance distribution. Adaptive learning recommendations are compiled based on an accuracy threshold of < 0.5 and a number of interactions > 10. Topics_13, topics_10, and topics_12 were identified as the most challenging topics. The results of this study indicate the potential of LDA-based approaches and clustering as analytical tools for shaping more personalized and contextual learning systems. Further research could explore sequential modeling and experimental validation of the effectiveness of recommendations
Copyright (c) 2025 Andika Dwi Arko, Muhamad Yusril Helmi Setyawan, Roni Andarsyah https://creativecommons.org/licenses/by-sa/4.0
Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style
Writing can be a powerful and unique medium of self-expression for every individual. Therefore, we propound a deep metric learning technique to acquire the vector representation of text, aiming to enhance the performance of deep learning classification models in typing style classification. The study also compared the effect of text pre-processing and distance metrics on model performance using tweet data from six different Twitter users. The outcomes of the study showed that the model without text pre-processing and with deep metric learning using the Cosine distance metric had the optimal result with an accuracy of 0.79, compared to the deep learning model with a categorical cross-entropy loss function which only had an accuracy of 0.76. Additionally, the model with text pre-processing also produced a good performance, with an accuracy of 0.63 using the deep metric learning approach and Cosine distance metric, and an accuracy of 0.64 using deep learning classification with a categorical cross-entropy loss function.
Copyright (c) 2025 Hendri Darmawan, Zulfa Muflihah, Tita Karlita https://creativecommons.org/licenses/by-sa/4.0
Garch Model Hybridization With Feed Forward Neural Network Algorithm Approach For Predicting The Volatility Of The Composite Stock Price Index
Stock market volatility is a crucial indicator in measuring investment risk and influencing investor decision-making, where proper understanding of volatility movements can help investors optimize their investment portfolios. Time series data from the stock exchange shows complex heteroscedasticity characteristics, where volatility levels can change dynamically over time, creating distinct challenges in modeling and prediction. The implementation of the hybrid model is carried out by integrating the advantages of both models, where GARCH is used to capture volatility clustering characteristics, while FFNN is utilized to capture complex non-linear patterns in the data. By using evaluation of several comprehensive error measurement metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), to ensure model reliability in various aspects of prediction. The use of the GARCH-FFNN hybrid model is expected to provide more accurate volatility predictions compared to using GARCH or FFNN models separately, with potential improvements in prediction accuracy and adaptability to changing market conditions. These findings provide important contributions to stock market volatility modeling and can serve as a reference for investors, portfolio managers, and financial practitioners in making better investment decisions, as well as paving the way for the development of more sophisticated volatility prediction models in the future
Copyright (c) 2025 Rangga Kurnia Putra Wiratama, Ahmad Saikhu, Nanik Suciati https://creativecommons.org/licenses/by-sa/4.0
Gated Recurrent Unit Based Predictive Modeling for Dynamic Obstacle Avoidance in Autonomous Aerial Vehicles
The entry of Autonomous Aerial Vehicles (AAVs) has reshaped multiple industries through novel solutions such as transport, monitoring, and deliveries. Nevertheless, the existence of dynamic operating environments, and the unpredictability of barrier emergence, constitutes a complicated path planning challenge that is difficult to cope with. Current methods of dynamic obstacle avoidance, e.g. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks, accomplished the task and became the essential part of AAV navigation systems development. These techniques may work, but they have a disadvantage of being slow in processing and less energy efficient, which are important for a real-time operation and for a mission which lasts for a long time. The purpose of the research is to fill up the identified gaps by introducing a GRU-based predictive model for dynamic obstacle avoidance in AAV’s. While the previous models concentrate on the improvement of reaction time and energy consumption without the degradation of computational efficiency, the recent GRU model is particularly designed for such purpose. It is realized through a streamlined design that facilitates rapid and precise object trajectory predictions, thus, making AAVs be able to rethink their paths in advance of any obstacles lurking. We show that the RNN-based GRU model is benchmarked significantly better than the RNN and LSTM models in simulated settings. In the Eco mode, the model GRU responded in 0.35 seconds in low-speed and its energy consumption never exceeded 130 units even in the high-speed scenarios with maximum load. Path efficiency was preserved and the path length was kept to the minimum in most cases, which indicates the model's capability in finding the most direct paths. Additionally, computer loads were at a tolerable level, thus further showing the applicability of this model for systems on-board having inducted limits for their processing capabilities. GRU- based model comes out as a robust and economical technique for the obstacle avoidance, giving a potential solution to the critical problems of AAVs.
Copyright (c) 2025 Parthasarathi Periasamy, Joemax Agu Maxwell Thompson, Biju Johnson; Sridar Krishnan https://creativecommons.org/licenses/by-sa/4.0
DDoS Mitigation in Kubernetes: A Review of ExtendedBerkeley Packet Filtering and eXpress Data Path Technologies
Kubernetes, as a widely adopted container orchestration platform, is increasingly targeted by sophisticated cyber threats, including Distributed Denial of Service (DDoS) attacks, which can severely compromise the stability, availability, and operational integrity of Kubernetes clusters by overwhelming the cluster’s control plane, disrupting pod scheduling, or saturating network resources. Emerging Linux kernel technologies, such as the Extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP), offer innovative and efficient solutions to mitigate these challenges by enabling high-performance packet filtering, real-time traffic monitoring, and advanced intrusion detection directly within the kernel. These capabilities help reduce latency, enhance resource efficiency, and strengthen the security posture of modern cloud-native environments. This review explores advancements in network security by examining the integration of eBPF and XDP for defending Kubernetes environments against DDoS attacks. By analyzing existing studies and identifying their limitations, this review highlights the potential of these technologies to establish efficient, scalable, and adaptive mitigation frameworks. The insights gained from this research can guide the development of robust security policies tailored for modern containerized infrastructures.
Copyright (c) 2025 Mircea Ţălu https://creativecommons.org/licenses/by-sa/4.0
Exploring The Role of Augmented Reality in Education: Systematic Literature Review
The development of digital technology drives innovation in education, one of which is through the implementation of augmented Reality (AR), which increases interactivity and understanding of abstract concepts in learning. This study employs a Systematic Literature Review (SLR) with the PRISMA method to analyze the implementation of AR in education. Of the 3,225,372 articles reviewed, 30 journals met the research criteria, with Marker-Based Tracking as the most commonly used AR method because of its stability and accuracy. The study results showed that AR increases students' interactivity, facilitates understanding of abstract concepts, increases student engagement, improves information retention and memory, facilitates simulation and practice, develops creativity and collaboration, adapts learning to individual needs, and improves cost and resource efficiency, although it still faces challenges in the infrastructure and technical skills of teachers. Therefore, further development in AR applications at various education levels is recommended to improve understanding and adaptation to scientific developments. Keywords: Augmented Reality, Learning Media, Marker-Based Tracking.
Copyright (c) 2025 Passion Timothy Gerald Sianipar, Wilonotomo, Priati Assiroj https://creativecommons.org/licenses/by-sa/4.0
Mixed-Integer Linear Programming for Optimal Operation of Integrated Electricity and Natural Gas System Considering Take or Pay Agreements
This paper is proposed to demonstrate the implementation of Mixed-Integer Linear Programming (MILP) for solving the optimal operation of the Integrated Electricity and Natural Gas System (IENGS). The MILP is used to realize an economical and reliable power electricity system based on Dynamic Optimal Power and Gas Flow (DOPGF) considering Take or Pay (TOP) agreements for natural gas. This method is simulated on the integrated 6-bus electricity and 6-node natural gas systems. By using MILP, the best costs for optimal operation of IENGS are obtained in three scenarios. The superiority of the MILP is validated by suppressing the increasing best cost for optimal operation to be below 10%. In the first case, the best cost is $735,405.37 without the TOP agreement. In the second scenario, the best cost ranges from $748,399.30 to $760,320.57 with the TOP agreement implemented in one-by-one generators, which is 1.77% to 3.39% higher than the first scenario. In the third case, the best cost is $791,833.04 with the TOP agreement in all of the generators, which is 7.67% higher than the first scenario. In addition, the MILP can perform the DOPGF for IENGS without violating the problem constraints regarding the load demand fulfillment and power system limitations in both coal-fired and gas-fired generators.
Copyright (c) 2025 Ervina Nooraini, Mohamad Almas Prakasa, Muhammad Ruswandi Djalal, Rony Seto Wibowo, Imam Robandi https://creativecommons.org/licenses/by-sa/4.0
A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting
Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.
Copyright (c) 2025 Ary Mazharuddin Shiddiqi, Bagaskoro Kuncoro Ardi, Bilqis Amaliah, I Komang Ari Mogi, Agung Mustika Rizki, Bintang Nuralamsyah, Ilham Gurat Adillion, Moch. Nafkhan Alzamzami https://creativecommons.org/licenses/by-sa/4.0
Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU
Technological development and the growth of the internet today have a positive and revolutionary impact in various areas of human life, such as banking, health, science, and more. The presence of Open Data and Open API also facilitates the exchange of data and information between entities without the restrictions imposed by different regions and geographical areas. However, information openness not only has a positive impact but also makes data vulnerable to data theft, viruses, and various other types of cyber attacks. The large-scale data exchange that occurs across the network poses a challenge in detecting unusual activity and new cyber attacks. Therefore, the existence of an Intrusion Detection System (IDS) is urgently essential. The IDS helps system administrators detect cyber attacks and network anomalies, thus minimizing the risk of data leaks and intrusions. The research developed a new approach using time-based sequential clustered data sets in the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. This IDS model was implemented using the CIC-IDS 2018 data set, which has more than 4 million data lines. The capabilities and uniqueness of the LSTM and GRU models are used to classify and determine various attacks in IDS based on sequential data sets ordered by time and clustered according to the destination ports and protocols, such as TCP and UDP. The model was evaluated using the accuracy, precision, recall, and F-1 scores matrix, and the results showed that the time-based sequential clustered models in LSTM and GRU have an accurities of up to 97.21%. This suggests that this new approach is good enough to be applied to the future IDS models.
Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
Mandibular Image Segmentation and 3d Reconstruction using U-Net Model
Penelitian ini bertujuan untuk meningkatkan presisi dan efisiensi dalam segmentasi citra mandibula dan rekonstruksi 3D menggunakan model U-Net. Segmentasi otomatis dengan U-Net menangani tantangan metode manual yang memakan waktu. Struktur Encoder-Decoder pada U-Net memungkinkan pembelajaran fitur citra medis yang kompleks dengan akurasi tinggi, menghasilkan segmentasi yang konsisten dan presisi. Hasil penelitian menunjukkan bahwa Res U-Net mencapai performa segmentasi yang unggul dengan Dice Similarity Coefficient (DSC) sebesar 95,37%, meskipun memerlukan waktu komputasi yang lebih lama. Sementara itu, U-Net standar menawarkan efisiensi komputasi yang lebih tinggi dan cocok untuk aplikasi real-time meskipun akurasinya sedikit lebih rendah. Integrasi segmentasi dengan rekonstruksi 3D meningkatkan visualisasi anatomi mandibula, memperbaiki efektivitas perencanaan bedah, serta menyediakan alat simulasi interaktif untuk perawatan personal dan pelatihan profesional. Penggunaan standar DICOM memfasilitasi aksesibilitas antar perangkat medis, mendukung interoperabilitas sistem perawatan kesehatan. Studi ini menyimpulkan bahwa Res U-Net optimal untuk kebutuhan presisi tinggi, sedangkan U-Net lebih cocok untuk aplikasi dengan pemrosesan cepat. Temuan ini diharapkan dapat memajukan teknologi segmentasi dan visualisasi medis yang andal dan efektif dalam praktik klinis.
Survey on Risks Cyber Security in Edge Computing for The Internet of Things Understanding Cyber Attacks Threats and Mitigation
Dalam era pesatnya perkembangan teknologi, penggunaan IoT terus meningkat, terutama dalam konteks edge computing. Makalah survei ini secara teliti menjelajahi tantangan keamanan yang muncul dalam implementasi IoT pada edge computing. Fokus utama penelitian ini adalah potensi serangan dan ancaman siber yang dapat mempengaruhi keamanan sistem. Melalui metode survei literatur, makalah ini mengidentifikasi risiko keamanan siber yang mungkin timbul dalam lingkungan IoT di edge computing. Pendekatan metodologi penelitian digunakan untuk mengklasifikasikan serangan berdasarkan dampaknya pada infrastruktur, layanan, dan komunikasi. Keempat dimensi klasifikasi, yaitu Network Bandwidth Consumption Attacks, System Resources Consumption Attacks, Threats to Service Availability, dan Threats to Communication, memberikan dasar untuk memahami dan mengatasi risiko keamanan. Makalah ini diharapkan memberikan landasan pemahaman yang kokoh tentang keamanan pada IoT dalam edge computing, serta kontribusi untuk pengembangan strategi keamanan yang efektif. Dengan fokus pada pemahaman mendalam tentang risiko keamanan, makalah ini mendorong pengembangan solusi keamanan yang adaptif di masa depan untuk mengatasi tantangan keamanan yang berkembang seiring dengan pesatnya adopsi teknologi IoT di edge computing.
Enhancing Face Detection Performance In 360-Degree Video Using Yolov8 with Equirectangular Augmentation Techniques
This study aims to enhance face detection performance in 360-degree videos by utilizing advanced image augmentation techniques with the YOLOv8 algorithm, which is effective for real-time object detection. Acknowledging the unique challenges posed by equirectangular projection, this research introduces a novel equirectangular augmentation method specifically designed for this medium. Our findings demonstrate a remarkable 1.346% improvement in detection accuracy in Equirectangular Projection (ERP) settings compared to default YOLOv8 augmentation strategies. This significant enhancement not only addresses the geometric distortions inherent in panoramic video formats but also emphasizes the critical need for tailored augmentation approaches to improve face detection in complex environments. By showcasing the effectiveness of these customized methods, this research contributes to the growing field of deep learning applications for immersive video technologies, with implications for sectors like security, virtual reality, and interactive media. Ultimately, this work highlights the potential of innovative augmentation techniques to ensure robust face detection in challenging visual contexts.
Evaluating Object Collection In Emergency Simulations Using Virtual And Augmented Reality
Virtual Reality (VR) and Augmented Reality (AR) are two technologies that have received significant attention in recent years. While both hold immense potential, they offer distinct ways for users to interact with digital content and their physical surroundings. This research aims to evaluate the interaction between users and a collection of objects in both VR and AR settings. To achieve this, a user study was conducted with 24 participant using Meta Quest 3 headset to run simulation in both environments. The study focused on tasks related to object collection and emergency management while utilizing combination of objective and subjective metrics to evaluate user interactions in both VR and AR environments. Despite the relatively close scores for both result, research shows that participants prefer AR for emergency simulations over VR. Even considering participants' first-time use of the applications, AR remains more popular, supported by lower symptom rates reported in the sickness than VR. Additionally, participants tended to focus more on collecting small objects, though VR users often forgot these items, while medium-sized objects were more frequently overlooked in AR. Although VR users experienced more human errors related to collisions with real objects, the overall impact on immersion during simulations was not significant enough to favor one technology over the other. Based on this result, it can be said that while VR is better for showing immersion, it is generally better for a first-time user to engage in AR first since it will give less incidence of virtual sickness.
Evaluation of Synthetic Data Effectiveness using Generative Adversarial Networks (GAN) in Improving Javanese Script Recognition on Ancient Manuscript
The imbalance of Javanese script data in ancient manuscript recognition poses a challenge due to the limited availability of data. A potential approach to addressing this issue is the use of Generative Adversarial Networks (GAN). This study evaluates the effectiveness of synthetic data generated using Enhanced Balancing GAN (EBGAN) in mitigating data imbalance. Various evaluation scenarios are conducted, including: (i) assessing the impact of syn-thetic data as augmentation, (ii) evaluating the sufficiency of synthetic data for recognition models, (iii) analyzing minority class oversampling with different selection strategies, and (iv) evaluating model generalization through cross-validation. Quantitative analysis of the generated synthetic data, based on Fréchet Inception Distance (FID) and Structural Similarity Index (SSIM), as well as visual assessment, indicates that the quality of synthetic data closely resembles real data. Additionally, experimental results demonstrate that combining real and synthetic data improves accuracy, precision, recall, and F1-score. The oversampling strategy for synthetic data has proven effective in meeting the data sufficiency requirements for training recognition models. Meanwhile, selecting minority classes and determining threshold values based on percentage, distribution, and model performance in oversampling can serve as guidelines for enhancing script recognition performance. Compared to previous methods, the use of EBGAN has been shown to produce more diverse synthetic data with better visual quality. However, further research is still needed to optimize GAN performance in supporting script recognition.
Audio Feature Analysis and Selection for Deception Detection in Court Proceedings
Deception detection is a method to determine whether a person is lying or not. One lie detector is a polygraph that measures human physiology, such as pulse and blood pressure. However, polygraphs have a problem in that they cannot be measured based on human psychology, such as speech and intonation. Therefore, audio deception detection is required, and this can be measured based on human psychology. This research will extract audio features, such as the Mel Frequency Cepstral Coeffi-cient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP), from the Real-Life Trial dataset, which comprises 121 audio data. From the extraction results in the form of numerical data totaling 6387 features, various feature-selection methods are employed, such as Feature Importance (FI), Principal Component Analysis (PCA), Information Gain, Chi-Square, and Recursive Feature Elimination (RFE). After feature selection, the selected features are input to machine learning models, such as random forest and support vector machine (SVM). After model testing, metrics such as accuracy, precision, recall, and F1 score were evaluated, as well as statistical evaluation, to assess the developed model. Results from this experiment show that the deception detection model is improved after a feature selection process to reduce irrelevant features. Comparing the accuracy, Chi-Square achieves a significantly higher result, reaching up to 92% with an improvement of 24.32%, surpassing the SVM model's accuracy of 67.57% before feature selection. In contrast, the RFE technique yielded the best accuracy of 86%, with an increase of 13.52%, building upon its baseline accuracy of 72.97%.
AUTOMATIC TESTING FRAMEWORK BASED ON SERENITY AND JENKINS AUTOMATED BUILD
Software Testing plays an important role in making high quality products and the right time. The process of testing done manually is often inaccurate, unreliable, and needed more than automatic testing. This research proposes a new framework for automation testing. This framework will help developers to create applications with better quality and shorten testing time. This framework offers a solution for developers so that the testing process is carried out easily and quickly. Our proposed concept consists of an automated test script based on Serenity Framework and can be done as a background process using Jenkins. Input of the system is a testing scenario, then mapped into Java Programming Language. Output of this system are test reports that represent the scenario that has been carried out. the results of implementation system prove that developers are helped by this framework in the software testing process. So that in this study it can be concluded that the automated testing framework that has been developed can improve the quality of application products through effective and efficient work methods.
AN IOT-BASED AUTOMATED WATERING SYSTEM FOR PLANTS USING INTEGRATED FUZZY LOGIC AND TELEGRAM BOT
The development of automatic plant watering systems has recently gained popularity due to the need to conserve water and ensure healthy plant growth. This study focuses on integrating fuzzy logic, sensors, and algorithms to provide an automatic watering system. Fuzzy logic is a powerful tool that allows the system to interpret sensor data and make informed decisions. The sensors measure soil moisture, humidity, temperature, and light intensity. The data collected from these sensors is analyzed using algorithms to determine the appropriate watering schedule. The system’s ability to analyze and interpret data ensures that the plants receive the necessary moisture without over-watering or under-watering. Integrating the Telegram Bot is a significant feature of the system, enabling users to monitor and control the system remotely. The Telegram Bot sends users notifications when the system is activated, or the plants require attention. The system can also be controlled remotely through the Bot, enabling users to adjust the watering schedule or turn the system on or off. This research shows that the designed features of the system function effectively and can be used on a daily household scale. The system’s automated features reduce the need for constant monitoring and manual watering, making it ideal for those who engage in gardening at home. This innovation is particularly relevant in increasing the productivity of plants. In addition, the system’s ability to be controlled remotely through the Telegram Bot is a significant advantage, making it accessible and convenient for users.
DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT
The self-check-in kiosk is one of the digital technologies used by the aviation industry to help passengers check in on passenger flights independently and efficiently without the need for a conventional check-in counter at the airport. However, the phenomenon on the ground indicates that many users have not yet used the service. As a result, the check-in area in some of the flight masks often has a long wait. Studies conducted by several airports in campsites such as Malaysia, South Africa, and Switzerland show that self-check-in kiosks do not meet the echoes of users. The same thing happened at Indonesian airports, where the use of self-check-in kiosks was still below 20% of total passenger traffic in 2022–2023. The study introduces the User Experience Technology Readiness and Acceptance Model (UX TRAM), which is used to evaluate user readiness and acceptance of the application of new technologies in the airport environment. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used to analyze the research model and the proposed hypothesis. Based on the results of the test of significance and relevance of the relationship in this study, the structural model proposed by the majority is of significant value, except for the variables Innovativeness and Insecurity versus Perceived Ease of Use. Based on the results of the test of the hypothesis carried out, out of 15 hypotheses tested, there are 13 accepted and 2 rejected hypotheses related to the readiness and acceptance of users in the use of new technology on the Self-Check-in Kiosk service at Soekarno-Hatta International Airport. The results of this study show that the proposed research model has varying explanatory strengths (near moderate to substantial/high) as well as predictive strengths that offer better predictable performance.