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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 389 Documents
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Mixed-Integer Linear Programming for Optimal Operation of Integrated Electricity and Natural Gas System Considering Take or Pay Agreements Nooraini, Ervina; Prakasa, Mohamad Almas; Djalal, Muhammad Ruswandi; Wibowo, Rony Seto; Robandi, Imam
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1265

Abstract

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.                                                        
Exploring The Role of Augmented Reality in Education: Systematic Literature Review Sianipar, Passion Timothy Gerald; Wilonotomo; Priati Assiroj
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1267

Abstract

    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.
DDoS Mitigation in Kubernetes: A Review of ExtendedBerkeley Packet Filtering and eXpress Data Path Technologies Ţălu, Mircea
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1268

Abstract

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.
Gated Recurrent Unit Based Predictive Modeling for Dynamic Obstacle Avoidance in Autonomous Aerial Vehicles Parthasarathi Periasamy; Joemax Agu Maxwell Thompson; Biju Johnson; Sridar Krishnan
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1271

Abstract

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.
Garch Model Hybridization With Feed Forward Neural Network Algorithm Approach For Predicting The Volatility Of The Composite Stock Price Index Putra Wiratama, Rangga Kurnia; Saikhu, Ahmad; Suciati, Nanik
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1278

Abstract

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
Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style Darmawan, Hendri; Zulfa Muflihah; Tita Karlita
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1292

Abstract

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.
Topic Modeling for Constructing Learning Profiles Using LDA and Coherence Evaluation Andika Dwi Arko; Muhamad Yusril Helmi Setyawan; Roni Andarsyah
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1301

Abstract

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
Gambling Comments Detection on Youtube: A Comparative Study of Tree-Based Boosting, LSTM and GRU Models Widiyanto, Agung; Prameswari, Mayesq; Abdul Latief, Muhammad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1305

Abstract

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.
Exploring The Effectiveness of In-Context Methods in Human-Aligned Large Language Models Across Languages Prathama, Ubaidillah Ariq; Ayu Purwarianti; Samuel Cahyawijaya
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1323

Abstract

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.

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