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STUDINUS: A Comprehensive E-Learning Platform for Academic Enthusiasts 8

STUDINUS: A Comprehensive E-Learning Platform for Academic E...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Fernando, David Kurniadi, Adrian Nathanael Nickholas Kurniadi, Felix Indra Riccosan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Online education has acquired prominence in higher education, particularly in light of the COVID-19 pandemic. Higher education institutions have investigated various online technologies, such as learning management systems, discussion forums, and video conferencing, to meet the learning objectives of online students. To facilitate effective learning, however, additional materials or tutorials from external sources are frequently required. In response to this demand, our team developed 'STUDINUS,' a web-based application designed to sell lecture notes and course materials to high school and college students. STUDINUS provides features to improve the administration of academic activities, including the organization of study materials, the monitoring of academic progress, and the facilitation of connections between peers and professors. This study concentrates on the development of an E-Learning website that enhances communication and enriches the learning experiences of students, as well as improves the learning quality. In the testing phase of the initiative, five individuals were chosen to participate in Black Box Testing. They were tasked with registering on the website and testing its numerous features. In order to evaluate the functionality of adding books to the server, we uploaded sample notes in PDF format and submitted old volumes. There were no reported issues with the search for science and English study material notes during the testing procedure. The forum's usability and simplicity were evaluated and determined to be adequate. User feedback regarding the website and its features will influence the project's ongoing development and enhancement. © 2023 IEEE.

关键词： E-learning

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Topic Modeling Using LDA-Based and Machine Learning for Aspect Sentiment Analysis

Topic Modeling Using LDA-Based and Machine Learning for Aspe...

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Herqutanto, Mochammad Farel Putra Zatari, Rafli Sutoyo, Rhio Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

E-commerce platforms facilitate the buying and selling of goods. Customers can also provide feedback on their shopping experiences. Analyzing consumers' feedback is essential for the e-commerce industry, particularly for shop owners. However, a detailed analysis of reviews individually is impractical. Fortunately, machine learning models allow for identifying the factors that drive consumer decisions when purchasing products and analyzing their opinions expressed in reviews. As the number of product transactions rises, it is also increasingly essential to automatically identify the factors influencing consumer choices and their resulting positive or negative reactions. This paper proposes topic modeling using Latent Dirichlet Allocation (LDA) for identifying influential topics from customer text reviews. The PRDECT-ID dataset was utilized to build the machine-learning models. The XGBoost Classifier achieved the highest accuracy of 87% in topic modeling through hyperparameter tuning. Furthermore, the Random Forest model achieves the highest accuracy of 93% for sentiment analysis. During the experiment, the authors discovered that topic modeling accuracy improves with hyperparameter tuning of LDA and machine learning models. Moreover, utilizing TF-IDF as a technique for extracting features during sentiment analysis boosts sentiment classification accuracy. © 2023 IEEE.

关键词： Machine learning

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Accuracy of Comparison Random Forest, Gradient Boosting Tree, Decision Tree, and Naïve Bayes Algorithms in Predicting the Size of Companies Where Data Scientist Works 5

Accuracy of Comparison Random Forest, Gradient Boosting Tree...

引用

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

作者： Putra, Dias Perdana Wilson Alexander, Ang Rizal, Sahrian Putra Amin Rais, Muhamad Nathaniel, Dominique Christopher Iskandar Pohan, Husni Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

Therefore, this research discusses the comparison of accuracy between machine learning algorithms such as Naïve Bayes, Decision Tree, Random Forest, and Gradient Boosting Tree for data scientist salary data in predicting the size of the company where they work. This research method involves data from the Kaggle platform. The data includes attributes such as salary paid in the year, salary currency, employee type, company location, job title, salary in USD, experience level in the year, salary, employee residence, company size, and remote ratio. The results of this study show that the three algorithms which is Random Forest, Decision Tree, and Gradient Boosting Tree, have a high level of accuracy compared to Naïve Bayes in predicting the size of the company where data scientists work. However, the performance of Random Forest is less optimal due to unbalanced data which results in the results of Random Forest being less accurate than Decision Tree. A high level of accuracy can affect the classification results so that the better the accuracy level, the better the classification results. Meanwhile, Gradient Boosting Tree obtained excellent accuracy results from the three machine learning algorithms. The conclusion of this research is that Gradient Boosting Tree and Decision Tree can be a good choice to predict the size of the company where data scientists work. However, it is important to ensure that the data is balanced so that the Random Forest algorithm can run more optimally. © 2023 IEEE.

关键词： Decision trees

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Evaluating the Effectiveness of LightGBM for Fetal Health Prediction

Evaluating the Effectiveness of LightGBM for Fetal Health Pr...

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

作者： Aristan, Claudio Januar Yang, Nicolas Biliam, Viery Octavianus Kurniadi, Felix Indra Riccosan Bina Nusantara University School of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350305197

The health of the fetus is essential for assuring that babies are born healthy. A mother's fetus must be in excellent health. The categorization of fetal health is essential for assessing the well-being and development of unborn infants. Despite this, numerous findings continue to indicate a high neonatal mortality rate. During pregnancy and labor, the Cardiotocography (CTG) is a common method for monitoring fetal health. It measures fetal heart rate and uterine contractions, allowing healthcare professionals to evaluate the baby's health and identify potential problems early on. However, interpretation of CTG data remains subjective, resulting in potential misdiagnoses of fetal health. This study seeks to reduce infant mortality rates caused by delivery complications by employing machine learning technology to assess a baby's health based on Cardiotocography test results. In this study, we evaluated the performance of three machine learning algorithms - XGBoost, Random Forest, and LightGBM - in predicting fetal health based on Cardiotocography data. We implemented various scenarios involving preprocessing techniques, feature selection methods, and hyperparameter tuning to enhance the model's prediction accuracy. The scenarios are without preprocessing, feature selection using Information Gain, feature selection using K-Best, Feature selection using both feature selection algorithm, without hyperparamter tuning and hyperparamter tuning with Grid Search. The result showed the use of all techniques combined proved beneficial, resulting in accuracies of 96% for XGBoost, 95% for Random Forest, and 97% for LightGBM. © 2023 IEEE.

关键词： Adaptive boosting

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Emotion Detection in Textual Data using Deep Learning 8

Emotion Detection in Textual Data using Deep Learning

引用

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

作者： Yohanes, Daniel Putra, Jessen Surya Filbert, Kenneth Suryaningrum, Kristien Margi Saputri, Hanis Amalia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Technological advancement is something we can no longer ignore, deep learning being one of those advancements. With deep learning, emotions can be extracted and analyzed in textual data to be utilized in various sectors, involving human behavior analysis. The main objective of this paper is to investigate and discover basic emotional categories that are typically exhibited by humans, such as sadness, joy, anger, fear, and disgust. Sectors focused on business can utilize emotion detection for various applications, such as to create personalized services, and in medical scenarios to help develop specialized mental treatments. This paper enables other researchers to enrich their knowledge and develop a more accurate and efficient deep learning model. To create an emotion detection model, analysis regarding the best performing model has to be done. This paper discusses the difference in performance across three different RNN models, namely LSTM, BiLSTM, and GRU. The RNN based models, in theory, share the same architecture and will be evaluated using the ISEAR dataset to create an emotion detection model. The result of this paper concluded that the simplest RNN model, GRU, achieved the highest score across four scoring metrics: accuracy, recall, precision, and F1 score. The GRU model achieved an accuracy of 60.26%, BiLSTM with 59.3%, and LSTM with 57.65%. Hence, based on the results, the GRU-based models are a strong choice for emotion detection when tested on the ISEAR dataset. © 2023 The Authors. Published by Elsevier B.V.

关键词： Deep Learning Emotion Detection Natural Language Processing Recurrent Neural Network Textual Data

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Advancing Breast Cancer Classification in Ultrasound Imaging with ResNetV1 and ResNetV2 for Improved Diagnostic Accuracy 3

Advancing Breast Cancer Classification in Ultrasound Imaging...

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3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

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

ISBN: (纸本)9781509062805

Breast cancer, a global health concern with a rising incidence rate, underscores the imperative for improved diagnostic accuracy to enable timely detection and mitigate mortality. Digital mammography, the primary diagnostic tool, faces limitations in detecting tumors within dense breast tissue. In this context, ultrasound imaging emerges as a cost-effective and widely available alternative, particularly for women with dense breast tissue. While ultrasound-based breast imaging holds promise, challenges persist in image interpretation and classification. Machine learning techniques exhibit remarkable potential in enhancing breast cancer diagnosis, and this paper builds upon prior studies to advance the field. Specifically, This research delve into the application of ResNet and ResNetV2 architectures, incorporating transfer learning and fine-tuning. Our findings highlight that fine-tuning significantly boosts model performance, with the ResNet50V2 model achieving an impressive accuracy of 99.11%. This study underscores the relevance of fine-tuning in breast image classification and contributes to ongoing efforts to improve diagnostic accuracy, aiming to reduce delayed diagnoses and decrease breast cancer mortality. © 2023 IEEE.

关键词： Image enhancement

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Sentiment Analysis E-commerce Review 8

Sentiment Analysis E-commerce Review

引用

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

作者： Abighail, Be Muhammad Doohan Fachrifansyah Firmanda, Muhammad Reyhan Anggreainy, Maria Susan Harvianto Gintoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Reviews give customers the opportunity to tell the rest of the world about how they love or hate a product. While there is a limit to the number of words, there is no limit to the words that reviewers can use to express their anger, frustration, excitement or joy in buying the product. Online reviews have become such a standard part of the buying process for many people these days that every online retailer needs to think about optimizing them in their business strategy. The sheer number of reviews requires a special method or technique that can automatically categorize reviews, whether they are positive or negative. This research tries to classify e-commerce reviews with Naïve Bayes Classifier (NBC). The results of this research show that the accuracy with NBC is 72%, Recall 72% and Precision 78%. © 2023 The Authors. Published by Elsevier B.V.

关键词： classifier e-commerce Naïve Bayes review sentiment analysis

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Diabetic Retinopathy Diagnosis System Based on Retinal Biomarkers Using EfficientNet-B0 for Android Devices 12

Diabetic Retinopathy Diagnosis System Based on Retinal Bioma...

引用

12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Matthew, Alvin Gunawan, Alexander Agung Santoso Kurniadi, Felix Indra Bina Nusantara University Jakarta School of Computer Science Computer Science Department 11480 Indonesia

ISBN: (纸本)9798350341102

Currently, Diabetic Retinopathy has become one of the main causes of vision loss for the working-class population (20-65 years) and the disease is an asymptomatic disease which makes detection of the disease without medical assistance more difficult. Along with the development of the era, many new methods have been found in diagnosing diseases, one of which is computer Aided Diagnosis (CAD) which relies on computer technology to assist medical personnel in diagnosing diseases. This research uses CAD with machine learning to classify Diabetic Retinopathy using transfer learning on the EfficientNet-B0 model which is implemented in a program for mobile devices for android. The program was designed with the aim of helping medical personnel to be able to diagnose Diabetic Retinopathy in areas that do not have sufficient medical facilities using only a smartphone, 20D lens, and several other drugs supports. The results showed that the EfficientNet-B0 model for classifying Diabetic Retinopathy was able to produce an accuracy of 91.85 percent in classifying three classes namely no DR, Non-proliferative DR, and Proliferative DR. This research was done to contribute in the growth of Artificial intelligence involvement in the medical world and also to help areas that lack the medical equipment to have better treatment. © 2023 IEEE.

关键词： Android (operating system)

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Development of Stock Price Prediction System Using NeuralProphet: A Combination of Deep Learning and Statistical Approach 12

Development of Stock Price Prediction System Using NeuralPro...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Bilal, Ananda Gunawan, Alexander Agung Santoso Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350341102

Uncertainty in investing money into stocks is a common problem experienced by investors, as investing in stocks without prior analysis can result in a financial loss & crisis, and even bankruptcy. This research employs the NeuralProphet model, which combines deep learning and statistical approaches, to analyze company stock prices and predict their trends. The objective of this study is to evaluate the performance of NeuralProphet in predicting a company's stock price, and to develop a web application system that can efficiently present and disseminate the research findings. Comparison using MAPE of actual vs. predicted stock prices from NeuralProphet will be used as the metric to calculate the accuracy and performance of the model. On top of that, NeuralProphet's performance will be compared to neural network models ANN, and the Naïve moving average method. This research found that NeuralProphet can predict with a comparable accuracy, sometimes even better than both ANN and Naïve. On prediction period of 130 days, NeuralProphet gives an average MAPE of 1.76, compared to 1.79 of ANN, and 3.03 of Naïve. Therefore, NeuralProphet is appropriate to be used for predicting company stock prices. © 2023 IEEE.

关键词： Forecasting

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Development of Augmented Reality Application in Physics through Newton's Laws and Object Interaction 8

Development of Augmented Reality Application in Physics thro...

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

作者： Ferdiman, Bayu Al Akbar, Hadad Faturrahman, Muhammad Reza Maulana, Fairuz Iqbal Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Physics learning is often challenging for students due to its abstract and complex concepts. This research is made to overcome the problem of lack of understanding related to physics learning due to abstract concepts. This research aims to develop and evaluate an Augmented Reality (AR) application that focuses on learning Newton's Law by interacting with 3D objects like balls, cars, and rockets. AR-based technology has been recommended for use in education due to its excellent potential in helping to improve the understanding of the concept of a material to be better. Therefore, this research will develop and evaluate the use of augmented reality in physics learning related to newton's law. This paper uses the ADDIE model method as the basis for developing Augmented Reality applications by involving the stages of analysis, design, development, implementation, and evaluation. The study results show that the developed AR application can help students understand the concept of Newton's Law and increase interest in learning. Student feedback obtained through questionnaires indicated that they rated this application positively regarding ease of use, engagement, and effectiveness in supporting the learning process. The AR application developed in this study improves the quality and effectiveness of physics learning through innovative and interactive technology. Further research could focus on developing additional content and features and exploring the impact of this application on a broader group of students and in different areas of education. © 2023 The Authors. Published by Elsevier B.V.

关键词： ARCore Augmented Reality Interactive Learning Newton's Law Object Interaction Physics Education

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