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Cognitive Assessment of Students Using Testing Instruments on computer Networks Courses in Informatics and computer Engineering Education Study program at UNJ 8

Cognitive Assessment of Students Using Testing Instruments o...

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8th International Conference on Education and Technology, ICET 2022

作者： Duskarnaen, Muhammad Ficky Sastrawijaya, Yuliatri Ajie, Hamidillah Arrazi, Muhammad Fakhruddin State University of Jakarta Informatics and Computer Engineering Education Study Program Jakarta Indonesia

ISBN: (数字)9781665492140

ISBN: (纸本)9781665492140

This study aims to design an assessment instrument to assess students' cognition in computer networking lessons. The design and design of the test instrument followed the ADDIE model, from the analysis stage to determine student targets and characteristics, then the design stage, determining learning objectives, learning material design, and question grids, at this stage the number of Subject Matter Experts (SME) was also determined as many as 6 people, for 6 Subject Matter Experts (SME), the minimum acceptable Content Validation Ratio (CVR) is 0.99, while the Content Validation Index (CVI) is 0.83. At the development stage, 81 items were designed as a draft assessment instrument. At the implementation stage, the Subject Matter Experts (SME) assessed each item on the draft assessment instrument. This study uses a quantitative content validity approach from the Lawshe model with a 3-point scale as follows: essential, useful but not necessary, and non-essential. Each Subject Matter Expert (SME) was given an instrument to assess each item in the test instrument. The Subject Matter Expert (SME) will determine whether an important question is given, normal or normal, or whether the question is not important given to students. At the evaluation stage, the Subject Matter Expert (SME) assessment of the draft test instrument will be compiled and the Content Validation Ratio (CVR) value and the Content Validation Index (CVI) value calculated to determine content validity. © 2022 IEEE.

关键词： Students

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Intelligent Tutoring System: A Pedagogical Model Approach Based on Deep Reinforcement Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第4期52卷 1196-1212页

作者： Alshaikh, Fatema Hewahi, Nabil Computing and Information Science program University of Bahrain Bahrain Computer science working University of Bahrain Bahrain

The primary goals of research in the fields of Artificial Intelligence (AI) and Machine Learning (ML) are to develop an autonomous agent that can perform tasks or make decisions without human intervention. Deep Reinforcement Learning (DRL) is a machine learning technique employed in Intelligent Tutoring Systems (ITS). Deep reinforcement learning is the integration of Reinforcement Learning (RL) and Deep learning (DL) techniques. In ITS, DRL provides students with individualized learning experiences based on their learning styles and preferences. This work uses a deep reinforcement learning architecture to build a pedagogical model for ITS. We present four different models. The first model is based on the Deep Q-Network (DQN) structure. The second and third models (Double DQN and Dueling DQN) are extensions of the DQN algorithm and have been further developed to address some limitations of the DQN approach. Double DQN solves the problem of overestimation of Q-values, while Dueling DQN improves the representation of the value function. The last proposed model is a hybrid model that combines the standard DQN with a learning classifier system based on Genetic Algorithms (GA). The findings of the study indicate that both Double DQN and Dueling DQN showed complete data efficiency with 100% accuracy, 1.0 F1 score, and 1.0 precision at the highest training level of 1000 episodes and 10 steps, whereas DQN performed fairly but was outperformed by advanced techniques at the same configuration level. The standard DQN achieves a precision of 0.975, an F1 score of 0.987, and an accuracy of 98%. On the other hand, the hybrid approach, which combines GA with DQN, consistently performed worse than the other three individual models in all training setups. © (2025), (International Association of Engineers). All rights reserved.

关键词： Deep reinforcement learning

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Machine Learning for Threat Detection

Machine Learning for Threat Detection

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Sutanto, Dustin Fransisca, Febri Liem, Steven Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila School of Computer Science Cyber Security Program Computer Science Department Bina Nusantara Jakarta Indonesia School of Computer Science Computer Science Program Computer Science Department Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

These days almost all people in the world use the Internet as the internet is constantly evolving. Cyber attack scale are increased thanks to cybercriminals that have become sophisticated in employing threats. Since the 1980s, Intrusion Detection Systems (IDS) have been developed to defend computer networks against malicious activity and unauthorized access. Companies choose to implement IDS the part of their Cyber Security strategy to protect their data and network from potential threats. in this case, IDS has played a crucial role in detecting abnormal traffic and protecting digital assets. furthermore, a high-accuracy IDS is needed in concern of having false positive feedback from the IDS machine itself that can cause various problems. to accompany the need for high accuracy and trainable IDS machines, we experimented with a hybrid system using machine learning that is combined with traditional IDS machines resulting in higher accuracy and lower error rate. By adjusting the parameter the accuracy improved and could achieve an accuracy of 99.66% of classifying the traffic. © 2024 IEEE.

关键词： Network intrusion

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Security Challenges and Issues in Cloud Computing

Security Challenges and Issues in Cloud Computing

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Zakiyyah, Rika Permana, Daniel Valenecio, Devan Chowanda, Andry Muliono, Yohan Izdihar, Zahra Nabila Cyber Security Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia Computer Science Program Computer Science Department School of Computer Science Bina Nusantara Jakarta Indonesia

ISBN: (纸本)9798350364910

Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding their infrastructure to meet the demand from the customer. With such advantages there will also be disadvantages such as security problems. This paper will discuss about security problems such as encryption, access control, multi-factor authentication, regular audits, secure configurations, incident response planning. This paper will also talk about security measures to deal with threats such as malicious insiders, data abuse, unsecured interfaces and APIs, shared technology complications, data loss or leakage, hijacking, and enigmatic risk profiles. This study examines and evaluates a range of cloud computing security concerns and issues through a systematic analysis of the literature. The goal of the research is to raise public awareness of the need for cloud computing security and to offer possible remedies. Identify and classify network security threats and vulnerabilities in cloud computing by doing an extensive examination of the body of research and empirical investigations is one of the answer to the questions from the paper. In the end, this take a look at unearths vulnerabilities and their practical implications through very well reading network safety in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Enhancing Stroke Detection in Brain CT Images with Residual Attention Networks

Enhancing Stroke Detection in Brain CT Images with Residual ...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Larasati, Sza Sza Amulya Shalsadilla, Shafatyra Reditha Utaminingrum, Fitri Graduate Program of Computer Science Faculty of Computer Science Universitas Brawijaya Indonesia

ISBN: (纸本)9781837242689

Stroke is becoming one of the most common diseases in modern society. Stroke caught in the early phase has a higher potential for recovery as it can be treated before the worsening of the patient's condition. Detection through brain CT images with deep learning has a favorable impact. Residual Attention Network (RAN) architecture is proposed to perform stroke detection. RAN builds upon the regular ResNet architecture by incorporating an attention layer within each residual block to extract meaningful information. RAN combines the advantages of residual connections and attention mechanisms. RAN is able to prevent vanishing gradient problem while focusing on the most important features. RAN yields an accuracy of 96.12% surpassing the regular ResNet with an accuracy of 94.40%. RAN shows great potential, especially in fields that require high accuracy and efficient feature extraction. This paper explores the effectiveness of Residual Attention Networks and their potential applications in areas requiring robust and accurate feature extraction. © The Institution of Engineering & Technology 2024.

关键词： computerized tomography

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Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

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computers, Materials & Continua 2023年第12期77卷 3637-3657页

作者： Derwin Suhartono Alif Tri Handoyo Franz Adeta Junior Computer Science Department School of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia Cyber Security Program Computer Science DepartmentSchool of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online *** study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and *** research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training *** inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original ***,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text *** exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original *** methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation ***,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using *** increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in *** proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

关键词： Data augmentation Generative Adversarial Network(GAN) Reverse GAN(RGAN) sarcasm detection

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Analyzing the Impacts of Dynamic Game Balancing in a Survival Roguelike Chess Game

Analyzing the Impacts of Dynamic Game Balancing in a Surviva...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Mario Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

This research investigates the novel application of Dynamic Game Balancing (DGB) techniques in the context of a hybrid chess-survival roguelike game, a unique combination of genres not widely explored in previous studies. The study addresses two primary research questions: the identification of suitable DGB techniques for these genres and the enhancement of user experience through DGB. A game prototype was developed using Unity, where players control a chess knight in a turn-based system, facing increasingly challenging enemy pieces. The DGB system dynamically adjusts the game's difficulty based on player performance, utilizing various inputs such as damage received or dealt, health points, powerup usage, and decision speed. Preliminary results from 14 beta-testers show overall positive feedback, with players reporting higher engagement and enjoyment due to the seamless difficulty adjustments, as reflected by the high mean scores from surveys like the User Experience Questionnaire (UEQ). This research fills a gap in the literature by demonstrating the feasibility and benefits of integrating DGB in a chess-survival roguelike setting. These findings highlight the potential for DGB to significantly improve player experience in hybrid game genres, suggesting that its application can create more adaptive and immersive gaming environments in various other genres as well. © 2025 IEEE.

关键词： Game design

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Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class Classification of Network Packet Attacks

Edge-IoTDistilBERT: Fine-Tuning DistilBERT for Multi-Class C...

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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

作者： Setiawan, Valentino Soewito, Benfano Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331513320

This exponential proliferation of IoT devices is creating an ever-growing demand for efficient cybersecurity solutions in resource-constrained environments. In this study, we propose Edge-IoTDistilBERT, a fine-tuned DistilBERT model, intended for the multi-class classification of network packet attacks within IoT ecosystems. Our proposed model was trained on the Edge-IIoT dataset, representative of all traffic classes and attack variants with high accuracy of up to 99.99%, outperforming state-of-the-art solutions. The new preprocessing pipeline of PCAP to text would transform the network traffic to a textual form allowing the model to learn the pattern in the dataset and making it able to generalize its content. Our fine-tuned model show a robust result on imbalanced dataset with two diffrent splits namely 80-20 and 70-30. The result shown by Edge-IoTDistilBERT place this model as a feasible effective cybersecurity solution for IoT networks, offering a good trade-off between high performance and resource constraints linked to edge devices. © 2025 IEEE.

关键词： Cyber attacks

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Optimizing Earthquake Forecast Through Clustering: K-Means and DBSCAN Approaches

Optimizing Earthquake Forecast Through Clustering: K-Means a...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Rajagukguk, Indra Marihot Lukito, Benedictus Kenneth Santoso Gunawan, Alexander Agung Syahputra, Muhammad Edo School of Computer Science University Bina Nusantara Computer Science Program Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350379839

An earthquake happens when the energy from the fracture of rock strata within the crust causes the earth to vibrate. Earth's surface experiences seismic waves because of this energy, which is produced by tectonic plate movement and radiates outward in all directions. Indonesia is especially vulnerable to earthquakes because of its location straddling three tectonic plates: the Pacific, Eurasian, and Indo-Australian. This project aims to mitigate this risk by using the clustering approach to evaluate large seismic datasets from the Meteorology, Climatology, and Geophysics Agency (BMKG) through 2022. Using the Clustering approach to forecast earthquake events requires a few crucial steps. First, a variety of sources provide seismic data that includes information about earthquake locations, magnitudes, depths, and timestamps. Based on the commonalities found in these attributes, the Clustering Method is then utilized to categorize seismic occurrences. The study concludes that K-Means is a better method for assessing seismic event clusters than DBSCAN. Compared to DBSCAN (0.009), K-Means produced more balanced and well-organized clusters with a silhouette score of 0.568. According to the K-means clustering results, there have been 3,756 occurrences (34.62%) and 3,932 incidents (36.24%) in the bright green and dark blue zones, respectively, which are places with high seismic intensity. This implies that to lessen earthquake damage in the future, these zones need to increase their mitigation efforts. © 2024 IEEE.

关键词： Seismic waves

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NoSQL Injection Detection Using Deep Neural Network and Principal Component Analysis of Injection Feature Vectors 11

NoSQL Injection Detection Using Deep Neural Network and Prin...

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11th International Conference on computer, Control, Informatics and its Applications, IC3INA 2024

作者： Soegiharto, Irwin Santoso Girsang, Abba Suganda Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program - Master of Computer Science Binus Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

NoSQL database has gained popularity in Big Data and other various applications for its simplicity and flexibility. The non-relational nature of NoSQL database such as MongoDB proves to improve development lifecycles and resources efficiency. However, security challenges arise along with increasing usage of NoSQL database, and NoSQL database is no exception to injection attacks. Machine learning proved to be an efficient method, as much has been researched. However, in the future there may be an increasing complexity of features that may prove costly to the model’s performance. Therefore, this research aims to utilize principal component analysis as dimensionality reduction and deep neural network as the classification method, to improve the security of NoSQL database. The text query is converted to feature vectors then further processed to reduce the input dimension of the deep neural network using PCA. The features used are based on previous research and various sources, and some are added after analyzing the dataset.10-fold cross validation is also applied to ensure that the model does not overfit the data, attempting to reduce bias to the result. The 10-fold cross validation model accuracy result is in average 97.44% with a standard deviation of 1.7%, and the testing results are 97.5% in accuracy,95.65% in precision, 91.67% in recall, and 93.61% in F1 score. Thus, it can be concluded that the usage of PCA on injection feature vectors can reduce complexity of the model. © 2024 IEEE.

关键词： Dimensionality reduction

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