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Enhanced Personality Prediction: Fusing Structural and BERT-Based Lexical Features on Augmented Twitter Data 7

Enhanced Personality Prediction: Fusing Structural and BERT-...

引用

7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Roslynlia Suhartono, Derwin BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Conventionally, assessing people's personality done by psychology experts requires more time and effort. Hence automatic personality prediction is starting to get noticed by researchers. Nowadays, many people engage themselves in social media platforms, one of which is Twitter. This platform stores actions done in this media which reflect one's personality. This research focuses on improving the performance of personality prediction models based on machine learning algorithms, by augmenting the dataset using Easy Data Augmentation to overcome imbalance dataset, thereafter combining n-grams with POS-tag weighted by TF-IDF. Results show that this method outperforms other machine learning approaches and decreases bias, with a maximum of 72.62% of the best average accuracy and 72.72% best average Fl score using Support Vector Machine classifier. The second-best model is obtained by XGBoost and, lastly, Naive Bayes classifier. Overall, this study shows that data augmentation with the addition of POS-tag feature is evident in improving the performance of personality prediction models. However, larger data is needed. © 2022 IEEE.

关键词： Learning algorithms

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Rice Commodity Supply Chain Issues in Indonesia: Exploring Blockchain Technology as an Alternative Solution

Rice Commodity Supply Chain Issues in Indonesia: Exploring B...

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Technology Innovation Management and Engineering science International Conference (TIMES-iCON)

作者： Rohmat Taufiq Harco Leslie Hendric Spits Warnars Haryono Soeparno Tanty Oktavia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program Master of IS Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350362602

ISBN: (纸本)9798350362619

Agriculture has long been recognized as pivotal to economic development, offering avenues to alleviate poverty, foster prosperity, and sustainably meet the food demands of a burgeoning global population projected to reach 9.7 billion by 2050. However, persistent inefficiencies within agricultural supply chains, particularly in sectors like rice production, pose formidable challenges. Addressing these inefficiencies is imperative to avert a surge in hunger and malnutrition. Failure to do so could condemn 815 million people to hunger and leave 3 out of 10 individuals malnourished. Thus, investigating solutions to enhance the resilience of rice supply chains is paramount. This research endeavors to identify and analyze challenges inherent in tracing the rice supply chain, offering a conceptual model proposal as a potential remedy. Employing a methodology centered on scholarly discourse and extensive literature review, our study reveals various impediments facing rice commodity supply chains in Indonesia. Furthermore, this study leverages blockchain technology, specifically the Sawtooth Hyperledger platform, to streamline the tracing process for rice commodities and rationalize the supply chain by reducing intermediary actors. From the proposed model, there is great hope that it can provide implications for the rice supply chain to run more efficiently.

关键词： Economics Supply chains Sociology Production Innovation management Blockchains Proposals

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Threat Modeling for Honeypot Deployment 10

Threat Modeling for Honeypot Deployment

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Silaen, Kalpin Erlangga Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798331521295

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes. © 2024 IEEE.

关键词： Zero-day attack

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Hate Speech Text Classification Using Long Short-Term Memory (LSTM) 4

Hate Speech Text Classification Using Long Short-Term Memory...

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4th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2022

作者： Ariwibowo, Suminar Girsang, Abba Suganda Diana Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

This paper aims to build hate speech text classification model by applying a combination of LSTM and FastText. The features of hate speech & non-hate speech, target hate speech, and categories of the hate speech. Dataset of those features taken from previous research by Okky Ibrohim. FastText word embeddings is used for formation of text vectors that will be used as input of the LSTM training model. The evaluation results obtained by getting the level of accuracy using confusion matrix. The accuracy value of text classification in this study is 83.52% on the classification of hate speech, 78.44% on the classification of target labels for hate speech, 82.75% on the classification of the label for category of hate speech. © 2022 IEEE.

关键词： Long short-term memory

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A review: Data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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Twitter-based Sentiment Analysis for Indonesian Drug Products using Supervised Feature Engineering 5

Twitter-based Sentiment Analysis for Indonesian Drug Product...

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5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022

作者： Purwandari, Kartika Jeremy, Nicholaus Hendrik Arisaputra, Panji Philip, Samuel Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455121

Social media communications offer valuable feedback to firms about their products. Twitter users share their opinions about e-commerce products on social networking sites. This paper reports a study in sentiment classification for Indonesian drug products for automatically classifying Twitter data as expressing positive or negative sentiments or opinions. The study investigates the effectiveness of using a Decision Tree, Support Vector Machine, Random Forest, Na rve Bayes, and Logistic Regression, along with the term frequency-inverse document frequency used to classify 531 tweets into recommended (positive sentiment) and not recommended (negative sentiment). Among all traditional machine learning models, the highest average accuracy value is 88.6 in the RF model and the lowest average accuracy value is 80.2 when using the LR model. Judging from the average accuracy obtained, the DT and RF model used has a considerable performance © 2022 IEEE.

关键词： Sentiment analysis

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YOLOS-MCI: Automating Microservices Coupling Index Calculation using Vision Transformers 9th

YOLOS-MCI: Automating Microservices Coupling Index Calculati...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Gintoro Heryadi, Yaya Lukas Wulandhari, Lili Ayu Sonata, Ilvico Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

Microservice Architectures (MSA) provide flexibility and scalability in software development. However, accurately measuring the level of interdependence among Microservices continues to be a difficult task. Precisely evaluating this connection is essential for efficient MSA design, maintenance, and future development. Conventional techniques for assessing Microservice coupling are frequently done by hand, require a significant amount of time, and are susceptible to mistakes. This impedes the capacity to make well-informed judgments regarding the integration and adjustment of services. This study introduces a new method for automating the computation of the Microservice Coupling Index (MCI) by utilizing the You Only Look at One Sequence (YOLOS) object identification technique in combination with Vision Transformer (ViTs) technology. YOLOS is utilized for identifying constituents within Unified Modeling Language (UML) Component Diagrams, facilitating precise classification and effective assessment of coupling. The model exhibits varying performance over multiple Intersection over Union (IoU) thresholds and object sizes, with an average precision (AP) of 0.406 over IoU values ranging from 0.50 to 0.95. The maximum precision is achieved at an IoU of 0.50, with an AP of 0.709. The model demonstrates good performance in identifying smaller components, especially when evaluated at a 0.75 IoU threshold. However, it faces challenges in detecting small items, suggesting potential areas for improvement in future iterations. Initial results indicate that this automation greatly decreases the need for manual, labor-intensive tasks and enhances the precision of measuring coupling in MSA, hence facilitating effective decision-making in service integration and modification. Automating the computation of the coupling index has the potential to significantly influence the design and management of durable and readily controllable microservice architectures. © 2024 The Authors.

关键词： Software design

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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Hoax Detection in Social Media: A Literature Review 4

Hoax Detection in Social Media: A Literature Review

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Saputra, Carsten Sebastian Wijaya, Norbert Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

For both print and digital media, the accuracy of the information has long been a problem that has impacted society and the business world. Information travels so quickly on social networks and is amplified that it can be skewed, false, or misleading. Inaccurate or incorrect information has great potential to affect millions of real-world users directly. Therefore, finding or getting accurate, trusted, and precise information becomes a problem. People start to spread fake news, which can cause certain events, such as mental disturbance, riots, and unnecessary anxiety. To prevent these problems, we will solve/research them with CountVectorizer and TF-IDF, which is what this study is all about (Discusses the detection of hoax news using CountVectorizer and TF-IDF). It was found that using these methods makes it possible to detect which words are most common in fake news and real news. © 2023 IEEE.

关键词： Digital storage

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Exploring EfficientNet Variants for Image Encoding in Auxiliary Signal Guided Knowledge Encoder-Decoder Framework 9th

Exploring EfficientNet Variants for Image Encoding in Auxili...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

The growing number of medical images has led to radiologist burnout, which seriously impacts the radiologist's performance. To address the previously mentioned issue, an Auxiliary Signal Guided Knowledge (ASGK) multimodal encoder-decoder framework was designed to automatically generate the medical report based on the proposed medical graph and natural language decoder. It utilizes DenseNet-121 as the image encoder. With DenseNet-121 lack of computational and memory efficiency, this study aims to explore the potential of EfficientNetB0 to EfficientNetB4 as an ASGK image encoder substitute. The framework is trained with IU X-Ray dataset for 30 epochs, with Adam optimizer, a learning rate of 0.01 with 0.8 decay rate, binary cross entropy loss for the medical tags, and cross-entropy loss for the generated medical captions. During the framework training process with each image encoder, the parameter that achieves the highest CIDEr score on the validation set is considered the best image encoder parameter and will be used on the test set. On the test set, EfficientNetB3 as an ASGK image encoder has been shown to increase the CIDEr score to 0.35, a significant increase from the 0.28 CIDEr score obtained by the ASGK using DenseNet-121. This score is only a 1% decrease from the best validation score. It suggests that not only EfficientNetB3 increases the framework's performance, it is also less prone to overfitting. This study has demonstrated that EfficientNetB3 is a potential image encoder substitute for DenseNet-121 in the ASGK framework. © 2024 The Authors.

关键词： Medical imaging

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