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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Irsyad Kharisma Sani Muhamad Isa Computer Science Binus Graduate Program Universitas Bina Nusantara Jakarta Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This study focuses on fire segmentation in peatland areas using the U-Net model, involving hyperparameter tuning combined with data masking developed using a modified ToPeCAl algorithm. The data masking process utilizes TIRS-1 band, band 7, and band 6, while the training data consists of composite images from bands 7, 6, and 4 (764) and 7, 6, and 2 (762) for comparison. Input data includes image patches sized 128×128, 256×256, and 512×512, derived from the 764 and 762 composites, respectively. The study reveals that the use of the leakyReL U activation function, a dilation rate strategy, a learning rate schedule, 50% image patch overlap, and a patch size of 128×128, improves the model's performance for fire segmentation compared to the baseline model. The final evaluation metrics demonstrate Precision, Recall, F1 Score, and IoU values of 88.85%, 89.70%, 89.08%, and 77.36%, respectively.

关键词： Measurement Image segmentation Schedules Training data Landsat Data models Informatics Tuning

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BERT base model for toxic comment analysis on Indonesian social media

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Procedia computer science 2023年 216卷 714-721页

作者： Ghinaa Zain Nabiilah Simeon Yuda Prasetyo Zahra Nabila Izdihar Abba Suganda Girsang Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897.

关键词： Toxic Comment MBERT IndoBERT IndoRoBERTa Small Indonesian social media

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Integration of Government Regulations to Anticipate the Threat of Terrorism with Radioactive and Nuclear Technology 8

Integration of Government Regulations to Anticipate the Thre...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Agustina, Mega Wijiyanti, Rika Mantoro, Teddy State Intelligence College Medical Intelligence Post-graduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

The development of science and technology is currently experiencing rapid progress, starting in the fields of energy, oil, non-destructive testing, health, agriculture, and other fields. The use of radioactive substances in the health sector is used for radiotherapy, nuclear medicine, and radiodiagnostics. The use of excess nuclear and radioactive substances can endanger living things and the environment. Monitoring and supervision of the use of these elements are very needed in order to avoid their utilization for non-peaceful purposes, such as terrorism. Nowadays, terrorist attacks in Indonesia began to evolve from the use of conventional explosives to the use of chemical, biological, radioactive, and nuclear materials. The data shows that during the period 2011-2019, there have been six attempted terror attacks using chemical, biological, radioactive, and nuclear elements. This paper examines the role of ministries/agencies in anticipating the use of nuclear and radioactive substances for terrorism. Based on the results of the library study, there are several ministries/agencies that have protocols for handling terrorism attacks that use chemical, biological, radioactive, and nuclear elements, but they are still sectoral. Therefore, it can be concluded that the integration of the regulations/protocols in handling terrorist attacks by using nuclear and radioactive elements are needed in order to increase the monitoring and supervision of the handling of radioactive waste. In addition, it is necessary to add the RPM tools to prevent the misuse of radioactive substances and nuclear materials by irresponsible parties. © 2022 IEEE.

关键词： Terrorism

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Network-based Intrusion Detection Through Image-based CNN and Transfer Learning

Network-based Intrusion Detection Through Image-based CNN an...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Pedro Horchulhack Eduardo K. Viegas Altair O. Santin João A. Simioni Graduate Program in Computer Science Pontifical Catholic University of Parana (PUCPR) Brazil

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Machine learning (ML) techniques for network intrusion detection is still limited in production environments despite promising results reported in the literature. Network traffic behavior exhibits considerable variability and evolves over time, requiring periodic model updates. This paper proposes a new approach to intrusion detection modeling based on CNN and transfer learning to reduce updating overhead. Its implementation is twofold. First, CNN is implemented using flow-based feature expansion derived from neural flattened hyperdimensional space. This expanded space representation contributes to a longer model lifetime and maintains system accuracy over time. Second, the required training data and computational cost are significantly reduced by performing periodic model updates based on a transfer learning approach. Experiments on a novel dataset with over 2.6 TB of data and one year of real-world network traffic demonstrate the feasibility of the proposal. Our proposal improves the average F1 score by up to 0.19 when no model updates are performed. While improving the system’s accuracy, model updates impose only 42.8% of the computational cost.

关键词： Wireless communication Accuracy Computational modeling Transfer learning Training data Network intrusion detection Telecommunication traffic

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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2024 IEEE International Conference on Data and Software Engineering, ICoDSE 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Kurnianingrum, Dian Ratnapuri, Chyntia Ika 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 Entrepreneurship Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331506407

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus. © 2024 IEEE.

关键词： Decoding

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Serverless utilization in microservice e-learning platform

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Procedia computer science 2023年 216卷 204-212页

作者： Bryan Andreas Nday Gede Putra Kusuma Renaldy Fredyan Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances.

关键词： Cloud Computing E-Learning Function as a Service Hybrid Deployment REST API

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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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The Influence of Well-Dressed NPCs on Player Perception, Immersion and Decision-Making in Gaming 5

The Influence of Well-Dressed NPCs on Player Perception, Imm...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Paradeda, Raul B. Freitas, Georgy Alves Filho, Sebastiao E. Abner Souza, Anderson State University of Rio Grande do Norte Department of Computer Science Natal Brazil State University of Rio Grande do Norte Computer Science Graduate Program UERN-UFERSA Department of Informatics Mossoró Brazil

ISBN: (纸本)9798350322774

This empirical study involved volunteers who played a game featuring NPCs specially developed for the research. The research investigated the influence and behaviour of NPC appearance on some factors regarding the players. Results suggest that NPC appearance can significantly affect the player's emotions, immersion, perception, and decision-making. Poorly dressed NPCs negatively influenced the player's emotional state, while well-dressed NPCs generated higher trustworthiness and positively influenced the player's decision-making. This study may help develop more efficient strategies for creating characters that influence the player's decision-making in video games and encourage the development of new research in the field of human-agent interaction and game development. © 2023 IEEE.

关键词： Decision making

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality

A Decision Model for Smart Village in Measuring Socioeconomi...

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International Conference on ICT For Smart Society (ICISS)

作者： Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity.

关键词： Object oriented modeling Simulation Indexes Stakeholders Socioeconomics

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