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检索条件"机构=Postgraduate Program in Computer science"

共 266 条记录，以下是1-10 订阅

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The Conceptual Model of the Student Learning Outcomes Monitoring System for Higher Education Institutions 3

The Conceptual Model of the Student Learning Outcomes Monito...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Mulyawan, Bagus Prabowo, Harjanto Harco Leslie Hendric, Spits Warnar Kartowisastro, Iman H. Bina Nusantara University Binus Postgraduate Program - Computer Science Department of Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9798350381931

Monitoring learning outcomes is an activity that must be carried out by the study program and requires time. Implementation of a system that can monitor learning outcomes needs to be done to eliminate the gap between information needs and the monitoring process. We propose an architectural model to monitor and evaluate student learning outcomes. The architectural model proposed will be able to assist study programs in building the tools used for monitoring learning outcomes. The research method was carried out qualitatively by collecting primary data and secondary data. Primary data was obtained through interviews with heads of study programs of Tarumanagara University, while secondary data was obtained from literature studies, government policies, best practices, and other related documents. The results of the research are in the form of a conceptual model architecture which consists of the components needed to monitor student learning outcomes, which can help study programs, lecturers, and students monitor learning outcomes. © 2024 IEEE.

关键词： Students

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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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Predictive Value Chain Analysis to Improve Generalization Accuracy In Quality Assurance

Predictive Value Chain Analysis to Improve Generalization Ac...

引用

2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Harmayani Sihombing, Poltak Mahyudin Elveny, Marischa Universitas Sumatera Utara Postgraduate Program in Computer Science Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Postgraduate Doctorate in Computer Science Medan Indonesia

ISBN: (纸本)9798331511166

Adaptive predictive analysis model utilizing Spiking Neural Networks (SNNs) to enhance quality assurance processes in Smart Campus environments. Traditional predictive models often struggle with the dynamic and complex nature of educational data, leading to inaccurate predictions and inefficient decision-making. SNNs, inspired by biological neurons, are particularly suited for processing temporal data and adapting to evolving patterns. The proposed model integrates spike-timing-dependent plasticity (STDP) and dynamic weight adjustment to continuously learn and refine predictions in response to real-time data, ensuring improved accuracy and robustness. The study uses historical data from the Internal Quality Assurance System (SPMI) over a three-year period, focusing on key performance indicators such as student enrollment trends, faculty productivity, and resource utilization. Experimental results demonstrate that the SNN-based model outperforms traditional neural networks, achieving a 20% reduction in Mean Squared Error (MSE) and a higher R-squared value. These findings indicate that the model not only enhances predictive accuracy but also offers significant improvements in operational efficiency and resource management. By providing timely and precise insights, this model supports proactive decision-making in educational institutions, enabling administrators to optimize resource allocation, monitor academic quality, and anticipate future challenges. This research contributes to the growing field of artificial intelligence in education, offering a scalable, adaptive approach to managing complex data and improving institutional performance in real-time. © 2024 IEEE.

关键词： Data accuracy

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A Machine Learning Pipeline for Emotion Recognition based on Brain Topographic Maps Derived from Electroencephalogram Signals 37

A Machine Learning Pipeline for Emotion Recognition based on...

引用

37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Alves, Bruno Cascaes Melo, Marla Pereira Cerri, Artur Melchiori de Freitas, Larissa Astrogildo Adamatti, Diana Francisca de Aguiar, Marilton Sanchotene Postgraduate Program in Computing Federal University of Pelotas Pelotas Brazil Undergraduate in Computer Science Federal University of Pelotas Pelotas Brazil Postgraduate Program in Computing Federal University of Rio Grande Rio Grande Brazil

Emotion recognition is an increasingly relevant field due to its direct implications for various sectors of society. The area aims to enhance the understanding of how emotions influence human behavior. Exploring brain activity analysis through electroencephalogram signals becomes possible when considering that emotions can manifest non-verbally. In this scenario, machine learning applications prove promising due to the complexity of recognizing emotions from electrical signal data from the brain. The case study focuses on DEAP, a recognized dataset constructed through experiments in electroencephalography, exposing subjects to musical and visual stimuli. The main objective of this work is to present a pipeline for the classification of emotions based on images of topographic maps generated from the EEGLAB tool and electroencephalogram signals. Additionally, the contributions of this work include the presentation of a structured dataset created through the mapping of temporal, spatial, and frequency data derived from topographic images and models for predicting dimensional emotions of arousal and valence based on the new dataset. Results demonstrate accuracies of 85.46% and 85.05% for the classification of low/high arousal and valence emotions, respectively. Copyright © 2024 by the authors.

关键词： Emotion Recognition

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New Student Prediction Using Algorithm Naive Bayes and Regression Analysis in Universitas Potensi Utama 4

New Student Prediction Using Algorithm Naive Bayes and Regre...

引用

4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Aditya, Elsa Situmorang, Zakarias Hayadi, B. Herawan Zarlis, Muhammad Wanayumini Universitas Potensi Utama Computer Science Postgraduate Study Program Medan Indonesia Saint Thomas Catholic University Computer Science Postgraduate Study Program Medan Indonesia University of Sumatera Utara Computer Science Postgraduate Study Program Medan Indonesia

ISBN: (纸本)9781665453950

Universitas Potensi Utama has various study programs, and also has various facilities to support student learning activities. However, the main obstacle in higher education is the uncertainty of the interest of students who register, so that sometimes the facilities are inadequate. To overcome these problems, there must be activities to predict prospective new student applicants to improve facilities and interest of prospective students to choose to study at the Universitas Potensi Utama. Forecasting is the most important thing that must be applied to a company. With this forecasting, companies can see the opportunities that exist to generate sales predictions in the future based on the results of past sales data. Naive Bayes is a classification using probability and statistical methods, the Naive Bayes algorithm can be used in scientific fields, one of which is predicting future opportunities based on previous experience. Linear regression method can be used for forecasting with the assumption that the correlation between variables will continue in the future. Linear Regression is a Regression Method where the resulting equation is linear. Based on the resulting equation, predictions can be calculated by entering the values of the predictor variables in the equation. Based on this process, the predictive value of the response variable can be generated. In this study, the author explains more about how to determine new student predictions using a combination of the two algorithms where the Naïve Bayes algorithm is used to state which study programs have a lot of interest in the coming year, and the Linear Regression algorithm is used to show which study programs are most in demand based on the number of students who registered in the previous year, In the description that has been stated above, the author makes a paper with the title: 'Predictions of New Student Registration Using the Naive Bayes Algorithm and Regression Analysis at the Universitas Potensi Utama'. ©

关键词： Forecasting

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DevFinder: An approach to finding expert software developers considering desired technologies and industry domains 26

DevFinder: An approach to finding expert software developers...

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： De Campos, Vitor David, José Maria N. Ströele, Victor Braga, Regina Oliveira, Alessandreia M. Federal University of Juiz de Fora Computer Science Postgraduate Program Juiz de Fora Brazil

ISBN: (纸本)9798350331684

Finding software development experts who address the specific needs of companies is essential in software development environments. However, companies have struggled to find experts who master specific technical skills aligned with the experience of acting in specific industry domains. To address this issue, we propose a recommendation system approach that helps to identify and classify eligible experts. In order to achieve this goal, data from repository databases are extracted, and a complex network is modeled considering a combination of three main aspects: i) the impact of a developer on the repositories of the industry context, ii) the technologies considered in the search terms, and iii) the date when the developer contributed to the project. A final recommendation experts list is presented in three contexts considering different technologies and industry domains, followed by a collaboration trends discussion. © 2023 IEEE.

关键词： Recommender systems

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A Process to Analyze Software Ecosystem Social Dimension Through a Collaboration Perspective 26

A Process to Analyze Software Ecosystem Social Dimension Thr...

引用

26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Guércio, Hugo Ströele, Victor David, José Maria N. Braga, Regina Federal University of Juiz de Fora Computer Science Postgraduate Program Juiz de Fora Brazil

ISBN: (纸本)9798350331684

Development communities and the software industry increasingly adopt the Software Ecosystems approach (SECO). This approach can provide advantages but add additional complexity to resource management, affecting the software supply network. Observing SECOs, we can see through three dimensions: business, technical, and social. The social dimension focuses on stakeholders and how they interact with other dimensions. This paper presents a process for analyzing the social dimension of Software Ecosystems, supported by Complex Networks metrics, which allow the presentation of existing SECO relationships' through visualizations and the use of complex networks. A preliminary evaluation with real data was carried out. The results point to the solution's viability. © 2023 IEEE.

关键词： Complex networks

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

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Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning 9

Features Reduction with PCA Technique for Malware Detection ...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Mantoro, Teddy Ginanjar, Agung Ayu, Media Anugerah Aditiawarman, Umar Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia

ISBN: (纸本)9798350370126

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688. © 2023 IEEE.

关键词： Principal component analysis

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