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Attendance Management System Using Face Recognition 8

Attendance Management System Using Face Recognition

引用

8th International Conference on Electrical, Electronics and Information Engineering, ICEEIE 2023

作者： Oei, Axel Jeremy Tan, Michael Rio Agustino Anderies Chowanda, Andry School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350340501

The attendance management system is crucial in educational bodies. Over the years, several attendance methods have been implemented, such as doing roll calls. This type of manual attendance has many flaws and can be improved. Research done on improving attendance management systems has been conducted by many people. We also aspire to improve attendance management systems by creating an automated attendance management system. A method of using face recognition to improve the attendance management system has been proposed. The face recognition of the attendance management system utilizes deep learning as its base. We tested this system on several students and asked for their feedback. The experiment subjects were satisfied by our system, and the results gathered were satisfactory as the system can recognize the students and register their attendance accordingly. This type of attendance management system could serve as an alternative and improve the flaws of manual attendance management systems. © 2023 IEEE.

关键词： Face recognition

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Audio-based Indonesia Toxic Language Classification using Bidirectional Long Short Term Memory 5

Audio-based Indonesia Toxic Language Classification using Bi...

引用

5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Luckianto, Marvin Addison, Renaldi Vincent, Vincent Muliono, Yohan Prasetyo, Simeon Yuda Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350369489

Users are able to connect with one another via web-based internet technologies through social media platforms like Twitter. On these platforms, hate speech has become a troubling problem, when people or groups are targeted because of characteristics like color, ethnicity, gender, nationality, religion, or organization. Such information is spreading quickly as a result of the practice of using hate speech to gain fast notoriety. 80% of cybercrime prosecutions in Indonesia involve hate speech and defamation on social media, despite the fact that there are regulations like the UU ITE Law, Chapter 28, Number 2, that forbid inciting hatred or enmity on the basis of race, religion, or ethnicity. This research paper explores a novel approach to audio-based toxic language classification in the Indonesian language, leveraging key techniques including Bidirectional Long Short-Term Memory (BiLSTM), speech recognition, and natural language processing. The study focuses on identifying toxic language using a balanced dataset of 5000 entries, equally divided between toxic and non-toxic categories. This dataset was compiled from diverse sources such as NusaCrowd, Kaggle, and others, and was subsequently cleansed of emojis and symbols to maintain a plain text environment. Utilizing audio-based content analysis, the methodology involved the transcription and classification of audio data, yielding an accuracy of 95.2%, precision of 96.4%, recall of 93.2% and F1 score of 94.8% in the detection of toxic speech. The implications of this research extend beyond the academic sphere of toxic language detection, holding significant real-world application potential in areas such as content moderation for Indonesian social media platforms and toxic language detection in customer service interactions. Thus, this study addresses a critical issue of toxic language proliferation in Indonesian online communities and social media platforms, further contributing to the development of healthier digital

关键词： Speech recognition

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Comparison of Machine Learning Algorithms for Diabetes Classification 9

Comparison of Machine Learning Algorithms for Diabetes Class...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Irmawan, Frederick Nathan Muliawan, Nicholas Hans Angky, Edbert Valencio Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

Diabetes is a chronic disease that influences how the human body turns food into energy and affects a significant portion of the global population. Diabetes prediction plays a major role in helping diagnose diabetes, which can help prevent complications and improve patient outcomes. This paper presents a machine learning approach for predicting the onset of diabetes using clinical and demographic data. The dataset used is from the Pima Indian Diabetes Database (PIDD), which was gathered from the National Institute of Diabetes and Digestive and Kidney Diseases. This dataset contains various variables such as pregnancies, glucose, blood pressure, skin thickness, insulin, body mass index (BMI), diabetes pedigree function, and age. Some preprocessing methods, such as oversampling and removing zero values to impossible data, was done to the dataset in order to balance and improve the input data. Then the paper compares four different machine learning algorithms, which are the K-Nearest Neighbors Algorithm (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). These models are then built to be predictive. The model's performance is assessed using a number of metrics, including the F1-score, accuracy, precision, and recall. The Random Forest algorithm works better than other models, according to the results by achieving an accuracy of 89.5%, a precision of 97%, a recall of 83%, and an f1-score of 89%. This study shows the potential of machine learning approaches in diabetes prediction as well as the efficacy of the suggested preprocessing strategy which hopefully help clinicians identify individuals at risk of developing diabetes for early intervention. © 2023 IEEE.

关键词： Forecasting

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Advancing Predicting Heart Disease: A Comparative Study of SVM, Naive Bayes, KNN, Linear Regression, and LSTM Models 9

Advancing Predicting Heart Disease: A Comparative Study of S...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Ivan, Jonathan Ferdinand, Julius Steven, Brandon Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

The heart is one of the most essential organs in the human body, working as the major pump for the blood that passes through the veins of the body, transporting energy and nutrients needed for the body to function. However, even though it is also one of the most protected organs in a human body, it is not exactly free from threats like diseases, malfunctions, etc. as it can still be damaged by various factors. As such, this research was made to detect the existence of heart diseases that might affect the functionality of the organ itself, by using various features to be used in an experiment where it involves multiple models with hyperparameter tuning with hyperparameters that we consider to be the best for each model used from our experiment in addition with experiment using LSTM and KNN in order to determine whether or not a patient has a cardiac condition. The experiment done by this research has gained quite a result, utilizing techniques like Hyperparameter optimization to find the best performing model, which was found to be tuned KNN with gain accuracy of 91.85%. © 2023 IEEE.

关键词： Diseases

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Predicting the Trend of Indonesian Minimum Regional Income Using Statistical and Deep Learning Approaches

Predicting the Trend of Indonesian Minimum Regional Income U...

引用

2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Reidolvo Tjan, Francis Nicolas Candika, Jedi Hardine Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Analyzing economic growth indicators is crucial in determining a country's economy in the future. One key indicator is minimum wage, in which this paper examined further by using both statistical methods and machine learning methods to forecast the indicator's future values. The data used is from Indonesian Central Bureau of Statistics' public website. Utilizing and comparing between four models: Linear Regression Model (LR), Autoregressive Integrated Moving Average Model (ARIMA), Deep Learning Model with Long Short-term Memory (LSTM), and Deep Learning Model with Gated Recurrent Unit (GRU), the paper concluded that GRU had the best result upon evaluation through finding all the models' Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2 score), in which GRU had values of 122,701.02, 104,442.75, and 0.3777 respectively. Prediction by GRU showed that Indonesian minimum wage would start to decrease and become stagnant in the 2023-2027 duration. This prediction however could potentially be false due to the GRU model having low R2 score, thus indicating the model had low predictive ability. Further research would be needed to explore larger datasets, utilize more economic indicators, and trying other models. © 2023 IEEE.

关键词： Long short-term memory

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Early Ischemic Stroke Detection Using Deep Learning: A Systematic Literature Review

Early Ischemic Stroke Detection Using Deep Learning: A Syste...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Tan, Karel Marvell, Yohanes Amadeo Agung Santoso Gunawan, Alexander Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339215

Stroke is a medical condition in which poor blood flow to the brain causes cell death and causes the brain to stop functioning properly. There are two types of strokes, which is ischemic and hemorrhagic. One of the techniques for early stroke detection is computerized Tomography (CT) scan. The purpose of this paper is to gather information or answer related to this paper's research question. Our research will be more focused on finding the most effective technique with technologies such as deep learning and machine learning to detect early ischemic stroke. Reviewing and gathering a conclusion from these papers are the methods which will be used in this paper. To conduct a literature review, we will use Systematic Literature Review method to filter research papers that are related to our topic and are matched with the keywords. This research came to a conclusion of the best datasets to use for deep learning in predicting ischemic stroke are MRI images and CT scan images, and the method with the best accuracy is Convolutional Neural Network (CNN). © 2023 IEEE.

关键词： Magnetic resonance imaging

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Usability Analysis of Text Generation by ChatGPT OpenAI Using System Usability Scale Method 8

Usability Analysis of Text Generation by ChatGPT OpenAI Usin...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Mulia, Angelina Patience Piri, Pirelli Rahelya Tho, Cuk Computer Science Department School of Computer Science Bina Nusantara University Malang65154 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

The development of artificial intelligence systems has resulted in various AI products including ChatGPT, which is a new product classified as a chatbot. This research aims to ensure that text generation systems such as ChatGPT open AI have the best level of quality and usability and are able to provide a satisfying experience for users. To measure and evaluate the effectiveness, efficiency and user satisfaction of the ChatGPT platform, researchers used the System Usability Scale (SUS) method. This data collection was carried out using an online questionnaire. After the collected data has been tested for validity and reliability, the researchers then analyzed the data results. From the results of the research conducted, the SUS value of the ChatGPT platform is 67.44. This score is included in the marginal high category of class D, with a reasonable or sufficient interpretation. With the results of the analysis per question item, it shows that users tend to agree that the system runs quite effectively, efficiently, well and is easy to understand. Although ChatGPT is able to perform tasks or commands well. However, it should be noted that not all information loaded by ChatGPT is presented in a complete, current and correct manner. This is because the information presented by ChatGPT is only limited to 2021. Because ChatGPT is a new technology and is still under development, further researchers are expected to test other features or ChatGPT to ensure the stability and reliability of the entire ChatGPT system using other research methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Artificial Intelligence ChatGPT OpenAI Questionnaire SUS System Usability Scale Text Generation Usability Analysis

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A Systematic Literature Review of Generative Adversarial Network Potential in AI Artwork

A Systematic Literature Review of Generative Adversarial Net...

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

作者： Rasyad, Farrel Kongguasa, Hardi Andry Onggususilo, Nicholas Christandy Anderies Kurniawan, Afdhal Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Humans have studied calligraphy and calculated programs to foster creativity for years. Image generation technology using artificial intelligence and Generative Adversarial Networks is currently reaching the peak of its performance. While there are newer and newer algorithms to improve the image generation system, the output of the images is still suitable at best and only excels in their category. While it is true that some of the images generated are good enough to be used, it is still unclear whether the capabilities of AI image generation can outperform their creative human counterparts. Therefore, this literature study aims to explore the basics of AI image generation, how they work, and what factors contribute to creating art such as simple pictures. Previous studies from several years ago show that most generated images are not good enough for creative usage because they only replicate traces of their dataset. The most significant factor contributing to this is the algorithm used and how it is used to create new images. In general, the concluded that while current AI-generated images are improving, they are still not creative enough to replace human creativity. © 2023 IEEE.

关键词： Generative adversarial networks

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A Review of Security in Financial Technology 8

A Review of Security in Financial Technology

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Nurmara, Muhammad Azril Kurniawan Hakim, Muhammad Naufal Ardy, Oey Hans Christian Jeffrey, Reynaldo Setiono, Vincentius Albert Kanigoro, Bayu Irwansyah, Edy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Financial Technology (FinTech) is a popular name for innovative technology utilized by financial organizations, with a focus on security and privacy concerns. These concerns include threats, attacks, hostile activity, and adversaries, as well as potential responses. The goal of this study is to undertake an assessment of current advances in financial industry security and privacy issues. FinTech is viewed as a gateway to increased business potential by financial sectors and entrepreneurs, but in order to explore such possibilities, mobile applications and other digital platforms must be established. However, security vulnerabilities to mobile applications have expanded dramatically, posing a dilemma for users and FinTech entrepreneurs. The findings of this study can be used to develop a theoretical framework for FinTech security and privacy, which will provide critical support for the establishment of a trustworthy security mechanism in FinTech. © 2023 The Authors. Published by Elsevier B.V.

关键词： financial technology machine learning security

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Comparative Analysis of Neural Network Method on Stroke Prediction 10

Comparative Analysis of Neural Network Method on Stroke Pred...

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

作者： Lee, Jonathan Apriliyandi, Kartono Gunawan, Erwin Honova, Shilvia Meidhi Salim, Brigita Vanessa Anggreainy, Maria Susan Parmonangan, Ivan Halim School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

Stroke is a medical condition that occurs when the blood supply to the brain is disrupted. This research conducts a comparative analysis of the performance of Neural Network (NN) methods for stroke prediction. The methods under consideration are dimension reduction using Principal Component Analysis (PCA) and without PCA, as well as two resampling methods, upsampling and downsampling. The results of the study provide insight into the strengths and limitations of each method and provide a basis for future work in this area. The highest accuracy is obtained by the model using the PCA and upsampling methods, with an accuracy about 73.7%. The study concludes that PCA and upsampling may improve the accuracy of the model, the choice of method will depend on the specifics of the problem and the goals of the project. © 2023 IEEE.

关键词： Principal component analysis

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