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Deepfake Technology: Negative Impacts, Mitigation Methods, and Preventive Algorithms 8

Deepfake Technology: Negative Impacts, Mitigation Methods, a...

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Gonzales, Nicholas Hernan Louis Lobian, Albert Hengky, Federigo Matthew Rendra Andhika, Muhammad Achmad, Said Sutoyo, Rhio Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

Deepfakes technology allows the act of generating fake content via deep generative methods. The current technology of deepfake techniques can create life-like images, audio, and fake video. Those outputs can be used for entertainment and education. However, it also has potential risks if it is utilized wrongly. For instance, fake content from deepfakes can spread malevolent propaganda to erode individual/organization/government character. It can also create ethical or legal problems about privacy, consent, and exploitation. This work explores the effects caused by deepfakes and summarizes essential items for policymakers, tech companies, and society to implement proper regulation. A systematic literature review is used as the basis for the methodology of this work by gathering data from pre-existing papers using certain keywords. The purpose is to promote awareness to prevent deepfake technology from being misused. © 2023 IEEE.

关键词： Deep learning

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Weather Prediction in Jakarta: An Analysis of Climate Data and Regional Influences using LSTM and GRU

Weather Prediction in Jakarta: An Analysis of Climate Data a...

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2023 International Conference on Data science and Its Applications, ICoDSA 2023

作者： Respaty, William Arga Hong, Castin Putra, Nicholas Kurniawan Kurniadi, Felix Indra Riccosan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350305197

This research investigates meteorological predictions in Jakarta using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, focusing on precipitation forecasting. Meteorological data, encompassing wind speed, temperature, humidity, and precipitation, were sourced from the Kemayoran Meteorological Station in Jakarta. After data pre-processing to ensure suitability for machine learning training, the study contrasted LSTM and GRU models' performances using Root Mean Square Error (RMSE) and R-squared (R2) as evaluation metrics. Findings showed the GRU model marginally outperformed during testing, though the difference wasn't statistically significant. The LSTM model exhibited greater consistency, suggesting its potential for more dependable weather predictions given the right dataset. The research underscores the necessity of considering regional factors, climatological data, and data time intervals for accurate forecasting. It offers insights into machine learning's potential in meteorological predictions. Specifically, at a learning rate of 0.0002, GRU had an RMSE of 18.034, slightly better than LSTM's 18.075. However, at 0.0003, LSTM outperformed with an RMSE of 17.972 compared to GRU's 17.988, highlighting the nuanced performances of these models. © 2023 IEEE.

关键词： Long short-term memory

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Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

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BISINDO Hand-Sign Detection Using Transfer Learning 8

BISINDO Hand-Sign Detection Using Transfer Learning

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8th IEEE International Conference on Recent Advances and Innovations in Engineering, ICRAIE 2023

作者： Joan, David Vincent, Vincent Daniel, Kevin Jason Achmad, Said Sutoyo, Rhio Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

This paper presents the development of a BISINDO hand sign language recognition system using transfer learning methods the system aims to address the communication challenges faced by the deaf community in interacting with the majority of people and alleviate the feelings of isolation experienced by the deaf and mute society. Two models, SSD MobileNet v2 FPNLite 320x320 and SSD MobileNet v2 FPNLite 640x640, were trained for 20,000 steps using a custom dataset of BISINDO alphabets (A-Z) the results demonstrate the successful detection and recognition of BISINDO alphabet characters by both models the second model, trained with SSD MobileNet v2 FPNLite 640x640 pre-trained model, exhibited slightly higher accuracy with improved mAP and AR scores these findings highlight the potential of computer vision technology to facilitate real-time detection and recognition of BISINDO hand signs, enabling effective communication between deaf and hearing individuals. Future research can focus on refining the models, expanding the dataset, and incorporating more comprehensive sign gestures to enhance the system's accuracy and usability. This research contributes to advancing assistive technologies for the deaf and hard-of-hearing community, fostering inclusivity and accessibility in society. © 2023 IEEE.

关键词： Object detection

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An Analysis of Hate Speech Detection Techniques 4

An Analysis of Hate Speech Detection Techniques

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

作者： Krisdianto, Ricky Apriani, Ivana Halim, Peter Miracle Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Hate speech is any act of provoking or insulting another person or group based on their ethnicity, religion, race, gender, sexual orientation, physical ability, or other characteristics. This can be done in a variety of ways. Because of this, a study was conducted that focused on ascertaining the most effective technique for identifying hate speech. It was discovered that LSTM, a form of recurrent neural network (RNN) that is particularly good at modeling sequential data, is the most successful technique to apply. Many natural language processing (NLP) activities may be performed with it, including hate speech detection, which makes LSTM have advantages over other techniques, both in terms of long-term memory mechanisms. LSTM, also able to handle long and complex text sequences, capable of recognizing more complex patterns, able to understand and consider the context in the text, to recognize the meaning of a word or phrase in a broader context. Then, based on the dataset obtained from the Kaggle website with a total of 24783 data, then the dataset is divided into training data and validation data, with a ratio of training data to validation data. of 80%: 20%. Then, the testing step results in an accuracy value of 87.10%. © 2023 IEEE.

关键词： Speech recognition

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Diabetes Risk Prediction Exploration: Uncovering Patterns and Enhancing Predictive Accuracy through Ensemble Learning 4

Diabetes Risk Prediction Exploration: Uncovering Patterns an...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Prasetyo, Simeon Yuda Santy Yunanda, Rezki Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Information Systems Department School of Information Systems Jakarta11480 Indonesia

ISBN: (纸本)9798350376111

Diabetes, recognized as a chronic noncommunicable condition, has become increasingly prevalent and now affects an estimated 425 million individuals worldwide as of 2020. This escalating global prevalence underscores the urgent need for comprehensive research initiatives to address this significant health challenge. Undiagnosed diabetes poses grave risks, including serious complications such as cardiac stroke and diabetic nephropathy, emphasizing the critical importance of early detection and intervention. Leveraging ensemble learning methods like Gradient Boosting, Random Forest, XGBoost, and AdaBoost, this study aims to construct a robust machine learning framework for precise diabetes classification, utilizing pertinent health indicators from the BRFSS dataset. Through rigorous evaluation, Gradient Boosting emerged as the most effective model, achieving an accuracy of 84.85%. This finding highlights the potential of ensemble learning techniques in refining predictive accuracy for diabetes risk assessment and ultimately improving patient outcomes, contributing significantly to the advancement of diagnostic tools in combating this global health challenge. The results underscore the importance of continued research in leveraging machine learning for enhancing diabetes management and public health strategies. © 2024 IEEE.

关键词： BRFSS dataset Diabetes Ensemble learning Gradient boosting Machine learning

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Model-Based Learning Techniques for Accurate Heart Disease Risk Prediction

Model-Based Learning Techniques for Accurate Heart Disease R...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Yuda Prasetyo, Simeon Amalia Saputri, Hanis Zain Nabiilah, Ghinaa Wulandari, Ajeng School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369359

The escalating global health crisis posed by cardiovascular disease, which claims an astonishing 17.9 million lives annually, necessitates precise and timely heart disease prediction, particularly in regions with limited access to advanced medical technology. Machine learning techniques have demonstrated potential in this regard, with previous studies achieving accuracies ranging from 86.6% to 90.78%. This paper addresses the urgency of accurate heart disease risk prediction by employing model-based learning techniques, focusing on four key algorithms: Support Vector Machines, Naive Bayes, Decision Trees, and Random Forests. Leveraging the Heart Failure Prediction dataset, which we split into training and testing sets, we systematically evaluated the performance of these models. Our findings reveal that Support Vector Machine achieved an impressive accuracy of 90.76%, emphasizing its proficiency in classification. Gaussian Naive Bayes displayed a balanced performance with an accuracy of 88.59%, maintaining an equilibrium between precision and recall. Although Decision Tree exhibited slightly lower accuracy at 82.07%, it maintained a well-balanced trade-off. However, Random Forest stood out as the top performer, with an accuracy of 91.85%, excelling in classification, precision, and true positive detection. These results provide crucial guidance for selecting an optimal model, and future research avenues include hyperparameter fine-tuning, ensemble methods, larger datasets, and the interpretability of model predictions, particularly in the medical field, where accuracy and transparency are paramount. Overall, this study underscores the potential of model-based learning techniques in accurate heart disease prediction, with Random Forest emerging as a promising solution. © 2023 IEEE.

关键词： Forecasting

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Negation Handling on XLNet Using Dependency Parser for Sentiment Analysis 4

Negation Handling on XLNet Using Dependency Parser for Senti...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Winaya, Ferdinand Girsang, Abba Suganda Binus Graduate Program Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331506490

Negation handling is often overlooked in Indonesian sentiment analysis, making it difficult to automatically and accurately determine the polarity of sentences containing negation words. Negation is a challenging issue in natural language processing, as it can drastically alter the meaning of a sentence. For example, common Indonesian negation words such as 'tidak' (no/not), 'belum' (not yet), or 'jangan' (do not) can reverse the sentiment polarity in a text. Previous studies have proposed rule-based approaches, relying on linguistic rules or dependency parse trees to handle negation. However, negation is a complex problem that cannot be effectively addressed by simple rules alone, as they often lack flexibility in handling diverse sentence structures and negation complexities. Therefore, a more dynamic approach is needed, such as combining a dependency parser with an embedding layer, which can map syntactic relationships between words and learn vector representations of negation. This allows the model to flexibly determine the scope of negation words, enabling more accurate sentiment analysis even in complex sentences. This study evaluates the impact of negation handling on a transformer-based model, specifically XLNet, in Indonesian sentiment analysis. Using the proposed method, the model's F1 score increased by 2.13%, from 71.42% to 73.55%, compared to the baseline model. This demonstrates that the proposed negation handling strategy enhances sentiment prediction accuracy, making the model more effective at handling texts with negation. © 2024 IEEE.

关键词： Natural language processing systems

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Detection of Cardiovascular Disease Using Machine Learning Algorithms and Principal Component Analysis 15

Detection of Cardiovascular Disease Using Machine Learning A...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Narfian, Pradipta Syifa Halim, Dylan Christiandi Sembiring, Albert Nathan Fredyan, Renaldy Pranoto, Hady School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Cardiovascular disease is a family of diseases that are the leading cause of death worldwide. Late diagnosis and treatment contribute to the high number of deaths caused by this family of diseases. To increase accessibility for the general public to get diagnosed and treatment, a cardiovascular disease detection system powered by artificial intelligence (AI) can be developed and used. The healthcare sector has already utilized AI for many purposes and has proven to be as reliable as trained professionals. This study aims to develop an AI model that can detect whether a person has cardiovascular disease through analyzing their clinical data. Both the support vector machine and the decision tree classifier algorithm were trained with the Cleveland heart disease dataset and a version of the dataset that has undergone dimensionality reduction through the principal component analysis (PCA) algorithm, with a 75:25 ratio of training and testing data. The performance of the two algorithms was then evaluated using the confusion matrix which found that the PCA algorithm enhanced the performance of both algorithms and that the SVM model paired with PCA achieved the highest performance of 86.67% accuracy, 90% precision, and 79.41$ recall. © 2023 IEEE.

关键词： Decision trees

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Development of Sundanese Stemmer Based on Morphophonemics 10

Development of Sundanese Stemmer Based on Morphophonemics

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Maesya, Aries Arifin, Yulyani Zahra, Amalia Budiharto, Widodo BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Binus Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

The Stemming Process is used to get the base word by removing the word affixes. The purpose of this study is to improve the algorithm in previous research by incorporating the Stemming Process into a morphophonemic approach for language processing based on rules (Rule Based) which is named AM Sundanese Stemmer (AMS). The AM Sundanese stemmer algorithm will utilize morpheme information to perform stemming on Sundanese words and analyze the phonemes in a word to identify the morphemes therein contained. The experimental process was carried out with 11,885 existing data and a sample of 546 words were taken to be stemmed. The test was carried out by comparing the algorithms of Nazief and Adriani, UG 18 and AMS. The test results showed that the AMS Algorithm was better than the Nazief Andriani Algorithm and the UG18 Algorithm with a high F-1 score of 87.52% and the comparison of the execution time of the AMS Stemmer Algorithm was 0.041403 seconds faster than the Nazief Andriani and UG 18 Algorithms. © 2023 IEEE.

关键词： Rule Based Stemming Sundanese Stemmer

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