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A Logistic Regression Model using Pearson Correlation for Breast Cancer Classification 6

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

引用

6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Anggreainy, Maria Susan Fitrianah, Devi Bina Nusantara University BINUS Graduiate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia Bina Nusantara University BINUS Graduiate Program Master of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350358346

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%. © 2023 IEEE.

关键词： Classification (of information)

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A review: Data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

引用

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

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

来源：评论

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Comparison of Effectiveness of Logistic Regression, Naive Bayes, and Random Forest Algorithms in Predicting Student Arguments 7

Comparison of Effectiveness of Logistic Regression, Naive Ba...

引用

7th Information Systems International Conference, ISICO 2023

作者： Wahyuningsih, Tri Manongga, Danny Sembiring, Irwan Wijono, Sutarto Doctoral Program of Computer Science Universitas Kristen Satya Wacana Salatiga Indonesia

Currently, in the process of assessing and giving feedback on students' argumentative writing, educators have to spend a considerable amount of time reading and analyzing each essay individually. This can be a complicated and time-consuming process, especially if the number of students to be assessed is quite large. The problem of this research is to find the most effective algorithm in providing accurate and reliable predictions in the context of evaluation and feedback of students' argumentation. This study compares three algorithms (logistic regression, Naive Bayes, and Random Forest) to predict student argumentation using essays from grades 6-12. Logistic regression performed best with 94.34% accuracy, followed by random forest with 91.98% accuracy, and Naive Bayes with 88.93% accuracy. The study optimized preprocessing and selected algorithms for an automated guidance model. It is the first stage of a three-part study for developing automated guidance models. Data came from Kaggle, and the study aims to improve the accuracy of automated guidance models for student argumentation. © 2023 The Authors. Published by Elsevier B.V.

关键词： Students

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Dictionary-based Sentiment Analysis at Subword Level

Dictionary-based Sentiment Analysis at Subword Level

引用

2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Yang, Janghoon Computer Science Program Division of Science and Engineering Penn State Abington AbingtonPA United States

ISBN: (纸本)9798350353464

While deep learning can offer a promising approach to sentiment analysis, it often presents challenges in complexity and explainability. Alternatively, sentiment analysis based on dictionaries has been explored. In this paper, subword-level dictionaries in English are considered to address performance degradation resulting from domain mismatches. To work at subword level, a framework based on naïve bayes machine learning algorithm is exploited. Furthermore, stopwords at the subword level have been proposed to remove additional interference intrinsic to subword tokenization. Numerical experiments demonstrate that the proposed method achieves higher accuracy and F1 scores compared to the conventional dictionary-based method when there is a mismatch between the dictionary and the corpus of documents while performing marginally worse than state-of-the-art deep learning methods when applied to datasets from the same domain. ©2024 IEEE.

关键词： Contrastive Learning

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GAN.s Approach On Generating Synthetical Skin Image Dataset for Imbalance Dataset 8

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

引用

8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Setiawan, Tri Agus Heryadi, Yaya Lukas Wulandari, Lili Ayu Sonata, Ilvico Binus Graduate Program -Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350368970

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. © 2024 IEEE.

关键词： Adversarial machine learning

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GeoBeeDashboard: An Intelligent Geospatial Platform for Enhancing Waste Management in Tangerang City 13

GeoBeeDashboard: An Intelligent Geospatial Platform for Enha...

引用

13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Muljana, Richie Deffansyah, Syauqi Akmal Andrew, Hari Gunawan, Alexander Agung Santoso Irwansyah, Edy Pramudya, Fabian Surya Ario, Muhamad Keenan Syahputra, Muhammad Edo Achmad, Said School of Computer Science Bina Nusantara University Data Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368086

In this study, we created an Intelligent Geospatial Platform called GeoBeeDashboard. The GeoBeeDashboard combines advanced geospatial technologies and artificial intelligence (AI), allowing real-time data visualization and optimized decision-making across multiple sectors. The system architecture consists of a ***-based front end, a FastAPI-driven back end, and PostGIS for geospatial data management. One of the GeoBeeDashboard's primary use cases is addressing waste management issues in Tangerang City, Indonesia. As rapid urbanization increases waste generation, effective management becomes critical. The dashboard identifies critical trends and inefficiencies in Tangerang's districts by K-Means clustering waste management data from 2020 to 2023. Our findings identify distinct clusters, highlighting areas with effective waste management and those that require targeted interventions. The benefits that local government can get from GeoBeeDashboard by utilising insights based on clustering analysis of data per each district in Tangerang to optimise waste collection, allocate resources efficiently, and improve the policy-making process for each district in Tangerang. The GeoBeeDashboard improves local government capabilities by providing a scalable model for other cities facing similar urban challenges. In conclusion, GeoBeeDashboard provides local governments with a powerful tool for addressing waste management inefficiencies and fostering sustainable urban development. © 2024 IEEE.

关键词： Urban planning

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Multiclassifier for Aspect-Based Sentiment Analysis on Indonesian Reviews of Kredit Pintar Online Lending App 12

Multiclassifier for Aspect-Based Sentiment Analysis on Indon...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Yuniar, Nanda Musdholifah, Aina Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350363432

Aspect-based sentiment analysis is carried out to determine user sentiment's polarity while still considering the aspects of the discussion so that all information in the reviews can be represented. The multiclassifier in this research means that two classifiers are used, the first is an aspect classifier to perform aspect classification, and the second is a sentiment classifier to perform sentiment classification based on its aspect. Two approaches were studied according to their classification work, namely, individual model in the form of an aspect classification and sentiment classification based on the aspects individually and sequentially model by testing the aspect and sentiment models sequentially. This study evaluates the performance of Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) algorithms and their combination in sequential testing. In addition, to overcome imbalance dataset, the use of oversampling types is also compared. In the individual approach, the SVM model with random oversampling showed the best performance with average accuracy, precision, recall, and f1 score of 92%, 86%, 80%, and 83% for the aspect model and 86%, 81%, 80%, and 80% for the sentiment model, respectively. While in the sequential approach, the combination of SVM-SVM with oversampling shows the best performance with an average accuracy, precision, recall, and f1-score of 79%, 56%, 50%, and 53%, while the performance of other model combinations is only in the maximum range of 77% accuracy, 55% precision, 51% recall, and 52% f1-score. © 2024 IEEE.

关键词： Support vector machines

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Determination of C4.5 Algorithm Performance Based on Training Data and Testing Data in Determining Diseases in Rice Plants 7

Determination of C4.5 Algorithm Performance Based on Trainin...

引用

7th IEEE International Conference of computer science and Information Technology, ICOSNIKOM 2023

作者： Siregar, Rahmadani Hartono Roslina Zarlis, Muhammad Wanayumini Universitas Potensi Utama Computer Science Postgraduate Study Program Medan Indonesia Politeknik Negeri Medan Computer Science Postgraduate Study Program Medan Indonesia University of Bina Nusantara Computer Science Postgraduate Study Program Jakarta Indonesia

ISBN: (纸本)9798350360752

Disease detection in rice plants at the Padang Lawas District Agriculture Service is carried out every year by collecting data from the Agriculture Service. Therefore, it is necessary to carry out classification to determine the best percentage of training data and test data in determining rice plant diseases and a comparison between testing data and training data for accurate accurate predictions. So data mining algorithms are needed that must be used to classify these diseases. Classification is carried out to group data into categories or groups so that objects from the same data are more similar to data objects from different groups. In this study, disease classification was processed using the C4.5 algorithm using disease data collection on rice plants from 2017 to 2020 which had a total of 248 data. The results of testing data and training data will be compared to see accuracy performance. © 2023 IEEE.

关键词： Agriculture

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Investigation of player emotion and instinct on racing video game using 6-11 framework (case study: split second (2010) single player modes) 9th

Investigation of player emotion and instinct on racing video...

引用

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

作者： Pratama, Galih Dea Novika, Andien Dwi Ardiyanto, Elshad Ryan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes. © 2024 The Authors.

关键词： 6-11 framework game analysis racing video games video games

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Design of an Automatic Price Tag System for Web-Based Retail Business 9

Design of an Automatic Price Tag System for Web-Based Retail...

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

作者： Liawatimena, Suryadiputra Gunawan, Devina Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper. © 2024 IEEE.

关键词： Sales

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