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检索条件"机构=Computer Science Department Binus Graduate Program - Master of Computer Science"

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Indoor Positioning System Classification with Wi-Fi Signal Data: A Machine Learning Approach

Indoor Positioning System Classification with Wi-Fi Signal D...

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International Conference on Knowledge and Smart Technology (KST)

作者： Ivan Alexander Gede Putra Kusuma Yaya Heryadi Widodo Budiharto Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement School of Computer Science - Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Indoor positioning systems (IPS) are critical for enabling accurate navigation, asset tracking, and emergency response in indoor environments where GPS fails. This study evaluates five machine learning algorithms-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree, and Random Forest-using two benchmark datasets, UJIIndoorLoc and UTSIndoorLoc. The results reveal that SVM achieves the highest accuracy, with 81.3 % on UJIIndoorLoc and 92 % on UTSIndoorLoc, followed by MLP with $\mathbf{7 8. 8 \%}$ and 90.9 %, respectively. Random Forest provides stable performance, with 77.6 % and 86.08 %, while KNN reaches 75 % and 89.7 %, performing well in structured environments. Decision Tree shows the lowest accuracy, 71.2 % and 76.54 %, highlighting its limitations with complex data. The UTSIndoorLoc dataset consistently yields higher accuracies, demonstrating its structured signal distribution. These findings underscore SVM and MLP as optimal algorithms for Wi-Fi fingerprinting IPS, offering robust and scalable solutions for indoor localization.

关键词： Support vector machines Accuracy Machine learning algorithms Nearest neighbor methods Fingerprint recognition Decision trees IP networks Random forests Wireless fidelity Indoor positioning systems

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Detecting Bipolar Disorder based on Crowdsourced Symptoms using Machine Learning with Shapley Additive Explanations 9th

Detecting Bipolar Disorder based on Crowdsourced Symptoms us...

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

作者： Martawidjaja, Marvel Lefrandt, Matthew Paramita, Ni Luh Putu Satyaning Pradnya Zhan, Rongfang Komalasari, Renata Kurniadi, Felix Indra Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Cahyono, Dwy Bagus Jati, Putu Hadi Purnama Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Services University of North Texas Denton76203 United States College of Nursing Pennsylvania State University State College16802 United States Statistics Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Meta Lab Jakarta10310 Indonesia

Bipolar disorder is a mental health condition characterized by extreme mental states ranging from manic highs to depressive lows. Early intervention is crucial to prevent progression and complications of bipolar disorder, which can lead to significant loss. This study proposes a framework to detect bipolar disorder based on crowdsourced symptoms in the form of free texts. We extract the features by transforming these free-text symptoms into binary features using a set of natural language processing techniques. We build support vector machine models with different kernel functions and penalized logistic regression models with different penalty functions, where the best models have a precision of 0.7, recall of 0.78, F1 score of 0.74, and accuracy of 0.88. Moreover, the models are explainable since we incorporate a model-agnostic explanation method called Shapley additive explanations to understand the symptoms that mostly contributed to the prediction of bipolar disorder. The models presented in this study can be implemented in the initial screening process of bipolar disorder for further examination. © 2024 The Authors.

关键词： Adversarial machine learning

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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A Logistic Regression Model using Pearson Correlation for Breast Cancer Classification

A Logistic Regression Model using Pearson Correlation for Br...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Maria Susan Anggreainy Devi Fitrianah Computer Science Department BINUS Graduiate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduiate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

We conducted research to diagnose breast cancer by classifying malignant and benign tumors. Several machine learning algorithms are used for classification, including Logistic Regression, Decision Trees, and ANN. The aim of this research is to determine the comparison of the three algorithms that have the highest level of accuracy, as well as the parameters and methods for optimizing the training data process in breast cancer classification. This research uses a dataset from Breast Cancer Wisconsin with the Pearson correlation method as feature selection to optimize the dataset for preprocessing data with drop features that have a correlation of more than 0.95. The research results show that the Logistic Regression algorithm is the algorithm that achieves the highest level of accuracy of 97.368421%.

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Enhancing Interoperability of Digital Twin in the Maintenance phase of Lifecycle 6

Enhancing Interoperability of Digital Twin in the Maintenanc...

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Ariansyah, Dedy Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Centre Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

In today's digitalization era, asset management has evolved following the rapid adoption of information systems and digital technologies. In the maintenance phase of the asset, Digital Twin (DT) emerges as an effective approach to improve failure recognition and structural damage prediction through DT-driven Prognostic and Health Monitoring (PHM). As a result, DT allows asset manager to proactively plan maintenance strategy to improve the availability of the assets. However, to reduce asset downtime in a cost-effective and time efficient manner, DT for maintenance phase needs to be integrated with other software systems that manage the provision of maintenance service and spare part such as computerized maintenance management system (CMMS) or Enterprise Resource Planning (ERP). Nevertheless, many of the developed DT in PHM were not equipped with semantic data model and hence, lacks interoperability with other systems (e.g., other DT or non-DT system) and limits the effective use of the DT. To qualify as an effective DT, it needs a semantical-enabled interface to connect with data and models from various domain to enable DT-driven decision-making. This paper leverages a rule-based ontology reasoning model to enhance DT interoperability with other maintenance software systems. The results depicts that the developed model paves a way for an effective use of DT-driven system in asset maintenance management. © 2022 IEEE.

关键词： CMMS Digital Twin ERP Maintenance Ontology

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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Sequence to Sequence Deep Learning Architecture for Forecasting Temperature and Humidity inside Closed Space 10

Sequence to Sequence Deep Learning Architecture for Forecast...

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Setiawan, Karli Eka Elwirehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

Solar Dryer Dome (SDD), an agricultural facility for drying and preserving agricultural products, needs a smart ability to predict the future indoor climate accurately, including indoor temperature and indoor humidity, in order to optimize electricity usage. To overcome these challenges, deep learning has been a widely adopted method. This research aims to forecast the future indoor climate using time series data by implementing a sequence-to-sequence (seq2seq) architecture, which is mostly used in Natural Language Processing (NLP) tasks. The two proposed seq2seq models, Long Short-Term Memory (LSTM) seq2seq and Gated Recurrent Unit (GRU) seq2seq, have proven to be superior to the adapted LSTM and GRU. The results show that the seq2seq GRU model outperforms the adapted GRU baseline model by an average difference of 0.03013 in MAE and the seq2seq LSTM model outperforms the adapted LSTM baseline model by an average difference of 0.00941 in MAE. To the best of our knowledge, this is the first implementation of seq2seq models for indoor climate forecasting on the Room Climate dataset. © 2022 IEEE.

关键词： Long short-term memory

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

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International Conference on Electrical Engineering and Informatics, ICEEI

作者： Richard Medyanto Nicholas Sanjaya Suryadiputra Liawatimena Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers.

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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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Input Representation on Text Data for E-Commerce Product Review Summarization Using Graph Convolutional Network

Input Representation on Text Data for E-Commerce Product Rev...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Azani Cempaka Sari Yaya Heryadi Iman Herwidiana Kartowisastro Widodo Budiharto Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This paper investigates recent advancements and methodologies in Automatic Text Summarization, a field that has garnered significant attention for its ability to condense lengthy documents into concise summaries while preserving key information and themes. The study examines the development of Automatic Text Summarization models, including both extractive and abstractive approaches. Additionally, the research delves into the use of graph convolutional networks for text representation in e-commerce product review summarization. The study employs an e-commerce dataset in conjunction with a graph convolutional network model, covering stages from preprocessing and tokenization to constructing graph-based data representation. The expected outcome is a graph-structured representation of input text data derived from Indonesian-language e-commerce product reviews.

关键词： Sentiment analysis Graph convolutional networks Reviews Text summarization Tokenization Multilingual Electronic commerce Meteors Recommender systems Postal services

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