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Six classes named entity recognition for mapping location of Indonesia natural disasters from twitter data

International Journal of Intelligent Computing and Cybernetics 2024年第2期17卷 395-414页

作者： Abba Suganda Girsang Bima Krisna Noveta Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakartaIndonesia

Purpose-The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter *** Twitter text is extracted by using named entity recognition(NER)with six classes hierarchy location in ***,the tweet then is classified into eight classes of natural disasters using the support vector machine(SVM).Overall,the system is able to classify tweet and mapping the position of the content ***/methodology/approach-This research builds a model to map the geolocation of tweet data using *** research uses six classes of NER which is based on region *** data is then classified into eight classes of natural disasters using the ***-Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data *** results also show good performance in geocoding such as match rate,match score and match ***,with SVM,this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region,which originate from the tweets *** limitations/implications-This study implements in Indonesia ***/value-(a)NER with six classes is used to create a location classification model with StanfordNER andArcGIS *** use of six location classes is based on the Indonesia regionalwhich has the large ***,it hasmany levels in its regional location,such as province,district/city,sub-district,village,road and place names.(b)SVMis used to classify natural *** of types of natural disasters is divided into eight:floods,earthquakes,landslides,tsunamis,hurricanes,forest fires,droughts and volcanic eruptions.

关键词： Twitter geolocation Natural disaster detection Support vector machine Natural language processing Named entity recognition

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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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Improved Model for Identifying the Cyberbullying Based on Tweets of Twitter

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Informatica (Slovenia) 2023年第6期47卷 159-164页

作者： Samalo, Darwin Martin, Rizky Utama, Ditdit Nugeraha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The surge of cyberbullying on social media platforms is a major concern in today's digital age, with its prevalence escalating alongside advancements in technology. Thus, devising methods to detect and eliminate cyberbullying has become a crucial task. This research meticulously presents a refined model for identifying instances of cyberbullying, building on previous methodologies. The process of devising the model involved a thorough literature review, object-oriented design, and decision tree methodologies to shape the labelling procedure and build the classifier. Data pre-processing was executed using RapidMiner, considering six intrinsic components. The final model successfully classified Indonesian-language tweets into five distinct categories: animal, psychology and stupidity, disabled person, attitude, and general bullying, with an accuracy rate of 99.56%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Decision trees

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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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Group Decision Support Model for Tech-Based Startup Funding Using Multistage Fuzzy Logic

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Informatica (Slovenia) 2023年第6期47卷 131-144页

作者： Devanda, Muhammad Donny Utama, Ditdit Nugeraha Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

The startup business model has grown rapidly in the last few years. However, giving investment or funding to a startup, especially in its early stages, is difficult because the risk is higher than a conventional company. This paper proposes a group decision support model (GDSM) that can help both government venture capital (GVC) and private venture capital (PVC) make the right funding decision. The model was built using a simple mathematics method (SMM) and multistage fuzzy logic (MFL) to examine twenty-two parameters in fuzzy and nonfuzzy values. Two experts from GVC and PVC were interviewed to weigh all the parameters. The model is implemented and tested using three real-world data. Ultimately, the model can help decision-makers in GVC and PVC to decide the most optimum funding for startups. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Investments

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Lip-Reading with Visual Form Classification using Residual Networks and Bidirectional Gated Recurrent Units

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第2期4卷 375-386页

作者： Anni Suharjito Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Lip-reading is a method that focuses on the observation and interpretation of lip movements to understand spoken language. Previous studies have exclusively concentrated on a single variation of residual networks (ResNets). This study primarily aimed to conduct a comparative analysis of several types of ResNets. This study additionally calculates metrics for several word structures included in the GRID dataset, encompassing verbs, colors, prepositions, letters, and numerals. This component has not been previously investigated in other studies. The proposed approach encompasses several stages, namely pre-processing, which involves face detection and mouth location, feature extraction, and classification. The architecture for feature extraction comprises a 3-dimensional convolutional neural network (3D-CNN) integrated with ResNets. The management of temporal sequences during the classification phase is accomplished through the utilization of the bidirectional gated recurrent units (Bi-GRU) model. The experimental results demonstrated a character error rate (CER) of 14.09% and a word error rate (WER) of 28.51%. The combination of 3D-CNN ResNet-34 and Bi-GRU yielded superior outcomes in comparison to ResNet-18 and ResNet-50. The correlation between increased network depth and enhanced performance in lip-reading models was not consistently observed. Nevertheless, the incorporation of additional trained parameters offers certain benefits. Moreover, it has demonstrated superior levels of precision in comparison to human professionals in the task of distinguishing diverse word structures. © Authors retain all copyrights.

关键词： Face recognition

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Gender Classification on Twitter Based on Feeds and User Descriptions Using Xlnet-Fasttext

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第20期48卷 71-80页

作者： Alandeta, Vito Rozaan Suhartono, Derwin Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia

This study proposes a gender classification method for Twitter data using a hybrid XLNet-fastText model. The objective is to enhance gender classification accuracy by leveraging the contextual understanding of XLNet and the semantic richness of fastText embeddings. Computational experiments were conducted on a dataset derived from Kaggle, focusing on user account descriptions and Twitter feeds. The proposed method achieved an accuracy of 71.4%, precision of 77%, recall of 60.9%, and F1-score of 68% for gender classification based on Twitter feeds. For user account descriptions, the scores were 72.4% for accuracy, 75.1% for precision, 63.24% for recall, and 68% for F1-score. These results outperform the baseline XLNet model and demonstrate the potential of the XLNet-fastText combination in improving text-based classification tasks. Our approach highlights a viable pathway to enhance gender classification on social media platforms, suggesting further improvements through multi-modal data integration. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Tweets

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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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Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms

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International Journal of Advanced computer science and Applications 2024年第3期15卷 902-911页

作者： Antonius, Victor Hugo Fitrianah, Devi BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta11480 Indonesia

In this digital era, the use of e-commerce has expanded and is widely adopted by society. One of the reasons why people use e-commerce platforms is because of their convenience and ease of use. However, the rapid growth of e-commerce has led to a substantial rise in transactions within the platform, involving various business entities. Therefore, it is crucial to perform customer segmentation to group them based on their purchasing behavior. The implementation of data mining techniques, such as clustering, is highly beneficial in this case. Clustering helps process datasets and transform them into useful information. In this study, transaction data obtained from one of the e-commerce stores, i.e. MurahJaya888 and followed by analysis using various clustering methods such as K-means, K-medoids, Fuzzy c-means, and Mini-batch k-means. We also proposed a new model that will become the attributes cluster, namely, RFM + DP (Discount Proportion). The Discount Proportion Rate will provide more insights for customer segmentation as it helps understand purchasing behavior that is more responsive to discount utilization. Implementing these four clustering methods with RFM + DP model resulted in four clusters based on the optimal elbow method. Furthermore, the evaluation and performance metrics for each clustering algorithm indicate that Mini Batch K-Means achieved the highest silhouette score of 0.50. Meanwhile, K-Means obtained the highest CH index value compared to the other algorithms, which was 1056. © (2024), (science and Information Organization). All Rights Reserved.

关键词： Data mining

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Analysing the Impact of Rhythm Game in Enhancing Typing Performance 10

Analysing the Impact of Rhythm Game in Enhancing Typing Perf...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Lancaster, Lemuel Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

The research was centered on adapting and assessing a new, unique game concept. The game onKeys presents a new approach and poses as an alternative method for improving typing skills. However, despite offering a promising avenue, the game still grapples with certain limitations. Utilizing the MDA Framework, the study seeks to integrate additional game components into onKeys and evaluate its effectiveness in improving typing skills. A paired T-test is used to assess the Pre-Test and Post-Test data. The System Usability Scale gathers subjective feedback about the game design's usability. This study found that the onKeys game has little impact on players' typing speed and accuracy, which may be attributed to a higher average typing speed, short period of playtime or a small sample size. However, upon reviewing the record and survey data, particularly the System Usability Score, it becomes evident that the game design still has potential. Further research can delve deeper into this game concept and design. © 2024 IEEE.

关键词： Game design

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