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Exploring the Spectrum of Biometric Technologies: A Systematic Literature Review of Conventional and Unconventional Modalities 9th

Exploring the Spectrum of Biometric Technologies: A Systemat...

引用

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

作者： Aldo Arista, W.P. Bryan Fernando, K.S. Sidharta, Sidharta Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Biometric recognition is a process of validating and identifying people based on their distinct physiological or behavioral traits. Through this literature review, we strive to reduce the knowledge gap among the public about existing biometric modalities, how the biometrics technology is applied, what algorithms are often used in processing the biometrics data, and what common benchmarks are often used to test and measure the performance of each biometrics. This paper provides a systematic literature review with the Kitchenham and Cochrane method, aimed at exploring variant types of biometric modalities and how each of them collects data, how biometric modalities are applied in various industries, common algorithms or methods used in biometric technologies, and establishing metrics and benchmarks for evaluating the performance of biometric technology. As a result, each biometric modality has its way of collecting data, for example, fingerprint recognition uses techniques like contrast correction, noise filtering, and sharpening to extract the fingerprint pattern. Biometric modalities are already used in many sectors of industries, and fingerprint readers or facial feature detection cameras are used to reduce the authentication process in the banking sector. Minutiae-based algorithm commonly used for fingerprint biometric modality, Eigenface for facial detection, and Euclidean distance or Support Vector Machine (SVM) for gait detection. Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Receiver Operating Characteristic (ROC) metrics and benchmarks are used to evaluate the performance of biometric technology. © 2024 The Authors.

关键词： Face recognition

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Analyzing Sleep Health from Lifestyle Data Using Lasso Regression 9

Analyzing Sleep Health from Lifestyle Data Using Lasso Regre...

引用

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

作者： Riupassa, Ascendiazorg Rambi, Putri Ireine Ibrahim, Muhammad Amien Fredyan, Renaldy School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Optimal sleep is the primary determinant of one's overall well-being and contentment. This study investigates the correlation between lifestyle choices and the quality of sleep by use the Lasso regression technique. The Lasso regression algorithm was employed to analyse lifestyle data obtained from Kaggle. The findings indicate that the Lasso regression model exhibits strong performance, with a training score of 0.9113 and a testing score of 0.8995. Additionally, the model demonstrates a low error rate of 0.1757. The regression coefficient analysis reveals that sleep length, physical activity, stress levels, and the occurrence of sleep disorders are the lifestyle factors with the greatest impact on sleep quality. These findings enhance our comprehension of how lifestyle choices impact the quality of sleep and can be utilised to create more efficient interventions for enhancing sleep quality and general well-being. © 2024 IEEE.

关键词： Sleep research

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Comparison of Spelling Error Correction Algorithms for the Indonesian Language 11

Comparison of Spelling Error Correction Algorithms for the I...

引用

11th International Conference on Information and Education Technology, ICIET 2023

作者： Yanfi, Yanfi Setiawan, Reina Soeparno, Haryono Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781665465489

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language. © 2023 IEEE.

关键词： Error correction

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Deep Learning Implementation with DenseNet for Alzheimer's Disease Detection in MRI

Deep Learning Implementation with DenseNet for Alzheimer's D...

引用

2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Prasetyo, Simeon Yuda School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350379839

The challenge posed by Alzheimer's disease (AD) in contemporary healthcare is significant due to its profound impact on cognitive functions. Recent progress in medical imaging and machine learning techniques offers promising avenues for early detection and diagnosis of AD, particularly through analyzing magnetic resonance imaging (MRI) scans. This study addresses the critical need for precise AD detection by optimizing pretrained DenseNet models for AD classification in MRI images. We perform transfer learning and fine-tuning stages on the DenseNet121, DenseNet169, and DenseNet201 architectures to tune the models for AD detection. The model's performance is thoroughly evaluated using comprehensive assessment measures like as accuracy, precision, recall, and F1-score. Our findings demonstrate that DenseNet169 achieves a remarkable accuracy of 99.806%, making it the top performer during the transfer learning phase. Moreover, DenseNet121 and DenseNet201 display competitive performance, showcasing the effectiveness of DenseNet architectures in neuroimaging tasks. These results underscore the potential of pretrained DenseNet models as potent tools for AD detection, providing valuable insights into enhancing diagnostic accuracy and patient care in neuroimaging-based disease diagnosis. © 2024 IEEE.

关键词： Deep learning

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Fake Job Vacancy Detection Using Ensemble Voting Classifier 2

Fake Job Vacancy Detection Using Ensemble Voting Classifier

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Yaphet, Torian Ariel Putra, Made Adhiaksena Wikrama Avianny, Vicensia Charitas Edbert, Ivan Sebastian Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350351613

The increase in fake job vacancy information has become a concern for people looking for jobs in the current digitalization era. Survey shows that 79% of job seekers search for job vacancies on the internet. The high demand is being exploited by malicious people, resulting in identity leaks and material losses for job seekers. This research is conducted to create a system that can detect fake job vacancies on the internet using techniques and algorithms that are increasingly developing. Author used several machine learning approaches such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Extra Trees (XT), and also voting classifier to perform classification on fake job vacancies. In addition, author also use Natural Language Processing (NLP) to perform data cleaning, Term Frequency-Inverse Document Fre- quency (TF-IDF) for feature extraction, and Synthetic Minority Oversampling Technique (SMOTE) for oversampling to overcome data imbalance. After implementing these approaches, the single classifier achieves F1 values between 60-75%. Meanwhile, the designed voting classifier model using XT, SVM, and XGB as base estimators can produce an accuracy of 98.21%, F1 of 77.7%, and MCC of 76,32%. In conclusion, the voting classifier model with SVM, XGB, and XT estimators can effectively improve the quality and accuracy of the predictions produced. © 2024 IEEE.

关键词： Support vector machines

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Combining DistilBERT and LSTM to Enhanced Detection of Depression in Social Media Text 9

Combining DistilBERT and LSTM to Enhanced Detection of Depre...

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

作者： Kurniadi, Felix Indra Satyaning Pradnya Paramita, Ni Luh Putu Angela Sihotang, Erna Fransisca Anggreainy, Maria Susan Zhan, Rongfang School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science University of North Texas Department of Rehabilitation and Health Service United States

ISBN: (纸本)9798350390025

Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data. © 2024 IEEE.

关键词： Social networking (online)

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Prediction on gdp per capita position using energy data 9th

Prediction on gdp per capita position using energy data

引用

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

作者： Himawan, Nico Saputri, Hanis Amalia Sari, Azani Cempaka Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Electricity is one of the most used forms of energy nowadays. Electricity consumption increases each year with the industrial sector as one of the biggest consumers, and the industrial sector obviously correlates with the economy. One way to measure a country's economic power is through GDP per capita. Even though it cannot describe everything about a country's economy, it can be used to measure the general health of a country's economy. GDP per capita prediction can lead to some benefits such as making an educated plan for government officials and for investors to make decisions whether investing in a country is beneficial or not. We tried to predict a country's GDP per capita position using their electricity consumption and generation data with machine learning approach. We are categorizing the GDP per capita so each category represents the position of the country relative to others like top 10%, top 20%, et cetera. The correlation between variables that we use are generally quite good, and our best model can predict the GDP per capita position of a country with above 80% accuracy for 10 categories used. © 2024 The Authors.

关键词： electricity energy gdp per capita machine learning prediction

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Object detection algorithms for car tracking with euclidean distance tracking and YOLO 9th

Object detection algorithms for car tracking with euclidean ...

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

作者： Ronariv, Raien Antonio, Renaldi Jorgensen, Steven Farrelio Achmad, Said Sutoyo, Rhio Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Traffic congestion is an important problem that causes wasted gasoline, lost time, and stress. Traffic congestion is caused by the high number of motorized vehicles on the road and ineffective traffic light control. Dynamic traffic lights are a solution to reduce traffic congestion by changing the duration of the lights based on vehicle density. This research uses object detection algorithms for car tracking and finding the most effective algorithm. The result of this work can be utilized in real-time analysis of traffic conditions by detecting and tracking vehicles at crossroads. This work tests and compares Euclidean Distance Tracking and YOLO algorithms for tracking moving objects. The testing dataset is traffic videos from highway CCTV footage. The datasets were pre-processed and passed to the tracking algorithms. Lastly, the performance of both algorithms is compared. The experiment shows that the Euclidean algorithm performs worse than YOLO. Euclidean algorithm erroneously identifies certain parts of the vehicle, such as the windows, rear-view mirrors, and even the vehicle's shadows, as part of the vehicle itself. In contrast, YOLO can effectively track vehicles with minimal errors. This study also highlights limitations in accurately tracking and counting vehicles using the current system and video footage. For example, the tracking performance was affected in scenarios involving camera movement or multiple vehicle types. © 2024 The Authors.

关键词： Traffic congestion

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Transfer Learning using IndoBERT with Long Short-Term Memory Classifier for Detection of Suicide Ideation Themed Indonesian Twitter Posts 4

Transfer Learning using IndoBERT with Long Short-Term Memory...

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

作者： Setiawan, Michael Dany Kimberly, Sofie Suharjo, Felix Antonius Harefa, Jeklin Bina Nusantara University School of Computer Science Computer Science Department Alam Sutera Indonesia

ISBN: (纸本)9798350376111

Indonesia's lack of profiling on suicide and an overall negative mindset towards mental health issues contributes to a lack of effective suicide prevention methods, particularly in the scope of social media as a platform to express suicide ideations, with little to no studies having been made regarding building robust models aimed at suicide ideation detection of Indonesian language posts. This paper presents eight distinct text classification models fine-tuned to effectively detect suicide ideation in Indonesian language Twitter posts by combining four variants of pre-trained IndoBERT models with a convolutional neural network (CNN) or long short-term memory (LSTM) network through transfer learning. Each of these eight models are trained using a dataset of 17464 Twitter posts collected through querying Twitter using Indonesian phrases comprising suicide and non-suicide themed words, which is then annotated and preprocessed through lemmatization, emoji, user tag, and link removal. It is found that an IndoBERT large-p1 pre-trained model combined with a LSTM network using transfer learning offers the highest performance in the detection of suicide ideation in Indonesian language Twitter posts, boasting a 91.41% accuracy rate, 0.9570 recall score, 0.8830 precision score, 0.9185 F-score, and 0.9675 receiver operating characteristic (ROC) area under curve (AUC) score, achieving high overall performance and minimizing undetected suicidal posts. This is attributed to IndoBERT large's high number of parameters, coupled with LSTM's capability of storing long-spanning features within the dataset, allowing the model to effectively understand context within a post and differentiate suicidal and non-suicidal Twitter posts. © 2024 IEEE.

关键词： Tweets

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"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring 9th

"Nyctophy": Assessing the effectiveness of serious game for ...

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

作者： Ardiyanto, Elshad Ryan Alexander, Patrick Putra, Edyth Novian Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four bina nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy. © 2024 The Authors.

关键词： Serious games

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