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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Fahmi, Hilal Nugroho, Muhammad Ivan Setiawan, Kristian Daniel Gary, Vittorio Moedjiono, Sardjoeni Warnars, Harco Leslie Hendric Spits School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Budi Luhur University Faculty of Information Technology Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

The haircut art community is a community that is interested in the art of hair or hair-related issues and information. This haircut art community has several members, but they need the application of technology to support their hobby as a community. In this mobile application, a system where users can make transactions connected to the haircut community has been proposed. Users can make online appointments with barbers' haircuts, make online payments, engage with forums, and get information about the barbershop community. In addition, this mobile app will allow people in the community to sell and buy anything related to haircuts. In addition, it is hoped that it can be used as an income opportunity for people who have expertise in the art of barbering. This application is implemented using visual studio and MySQL database for the database. This application is modeled using use case diagrams to describe its business processes and class diagrams to describe the database design. © 2022 IEEE.

关键词： Database systems

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction

Examining the impact of attitude, availability, price, and q...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Ikrar Adinata Arin Dina Fitria Murad Harco Leslie Hendric Spits Warnars Meyliana Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Indonesia Jakarta

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions.

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Explainable Supervised Method for Genetics Ancestry Estimation 1

Explainable Supervised Method for Genetics Ancestry Estimati...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Budiarto, Arif Pardamean, Bens Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9781728133331

Ancestry estimation is one crucial stage in genomic research. It generates scores that represent the admixed genetics profile as the result of human evolution. In the previous research, we implemented multiple unsupervised methods to estimate these scores from large genomics data obtained from the 1000 Genome Project. These methods were limited to only cluster the samples to the five global populations in the dataset. The main challenge arose when implementing these methods to cluster the samples into more specific subpopulations. In this paper, we proposed a supervised approach to answer this challenge. Two state-of-the-art supervised machine learning methods, XGBoost and Deep Neural Network (DNN), were applied to the same dataset. These methods were aimed to classify samples, both into five main populations and also into 26 sub-populations. In the first classification task, both methods achieved similar results to our previous unsupervised approach. Interestingly, for the second classification task, which posses a relatively higher difficulty, DNN yielded better performance in the train, validation, and test dataset, despite its overfitting problem. Furthermore, the feature importance scores from each model were calculated using Shapley Additive Explanations (SHAP) method. Finally, 11 overlapped SNPs from all models were evaluated based on the reported Minor Allele Frequency (MAF) from the 1000 Genome Project. Overall, only using these 11 SNPs, we could differentiate each population in regards to its average MAF. © 2021 IEEE.

关键词： Genome

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Classification of Alzheimer’s Disease using Random Oversampling and Albumentations on Convolutional Neural Network

Classification of Alzheimer’s Disease using Random Oversamp...

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International Conference on Informatics and Computing (ICIC)

作者： Calvin Kamardi I Kadek Perry Bagus Laksana Maria Susan Anggreainy Nora Fitriawati Michiko Amaya Yonatan Tesalonika Abigail Eikwine Mangkang Vianny Pengesa Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

Alzheimer’s Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition.

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset

GAN.s Approach On Generating Synthetical Skin Image Dataset ...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Tri Agus Setiawan Yaya Heryadi Lukas Lili Ayu Wulandari Ilvico Sonata Computer Science Department BINUS Graduate Program -Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia - Atma Jaya Jakarta Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

The use of balanced medical datasets is essential to improve the precision and accuracy of machine learning models in the healthcare field. However, dataset imbalance often becomes an obstacle, especially in the diagnosis of skin diseases. This research aims to develop a Generative Adversarial Networks (GAN) method that is more effective in generating synthetic skin datasets to overcome problems in integrating medical datasets. The methods used include developing and training a GAN model to produce realistic synthetic skin images, with a focus on improving the quality and diversity of the synthetic data produced. Important results from this research show that the developed GAN model is able to produce synthetic skin images that are not only realistic but also able to balance the original dataset. The implications of these findings include improving the accuracy and performance of machine learning models in the diagnosis of skin diseases, as well as the potential use of these methods in other medical fields that face similar problems with imbalanced datasets.

关键词： Training Accuracy Machine learning Generative adversarial networks Reliability engineering Skin Data models Medical diagnostic imaging Diseases Synthetic data

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The Floating Fuzzy Logic based Evaluation Model for Appraising the Student Performance

The Floating Fuzzy Logic based Evaluation Model for Appraisi...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

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

Membership function (MF) in process of fuzzy logic is very meaningful. It depicts the core of model. It can be adopted from the expert judgment and also coming from the configuration of data behavior. The study is an experimental research by analyzing time-series data behavior in developing the MF by using the programming language Python. The MF academically produced called as floating MF (FMF). Such a FMF operated to realize the fuzzy logic conception in an evaluation model. The constructed model is a model in simply measuring the student's performance. By using the ten series-data of student's grade point average (GPA) and absence per annual (APS) values, the model can simulate the moving students' performance per semester. When compared to the conventional FL based model, the proposed model has a similarity average 85%.

关键词： Fuzzy logic Electrical engineering Behavioral sciences Information technology Python Information systems

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Improving Land Cover Segmentation Using Multispectral Dataset 8

Improving Land Cover Segmentation Using Multispectral Datase...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herlawati, Herlawati Handayanto, Rahmadya Trias Heryadi, Yaya Abdurachman, Edi Soeparno, Haryono Irwansyah, Edy Miranda, Eka Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Universitas Islam 45 Computer Engineering Bekasi Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Trisakti Institute of Transportation and Logistic Jakarta13410 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350342604

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia. © 2023 IEEE.

关键词： Landsat

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease

Comparative Analysis of CNN Models for Multiclass Detection ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho Mahmud Isnan Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection

关键词： Hands Deep learning Accuracy Endoscopes Real-time systems Gastrointestinal tract Data models Colon Medical diagnostic imaging Diseases

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Repurposing transfer learning strategy of computer vision for owl sound classification

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Procedia computer science 2023年 216卷 424-430页

作者： Kevin William Gunawan Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Accurately predicting an owl species based on its sound can be helpful for owl conservation. To build an accurate model for owl sound classification, deep learning is currently the most preferred algorithm, due to its excellent performance for modeling audio data. However, deep learning is generally underperformed for a small dataset, which is the case for recognizing scops owl sound. To overcome the issue, we proposed a transfer learning strategy, which is common for computer vision tasks, that can alleviate overfitting in a deep learning model for the owl sound classification. In our approach, we propose a neural network architecture consisting of the backbone of a EfficientNet model pre-trained on the massive ImageNet database. The model takes the sound input that has been converted as two image representations: Spectrogram and Mel Frequency Cepstral Coefficients. Our strategy enables the use of a relatively small size of pre-trained image classification model, which is widely available, for transfer learning in owl sound classification. Deploying the lightweight model in an automatic sound classifier provides a fast and accurate tool for various owl conservation purposes.

关键词： acoustics deep learning bird sound species detection transfer learning

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes

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Procedia computer science 2023年 227卷 1054-1061页

作者： Nicholas Dominic Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further.

关键词： rice phenotypes prediction statistics machine learning

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