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information technology Research and Innovation (ICITRI), International Conference on

作者： Nanang Susanto Hilman Ferdinandus Pardede Computer Science Study Program Universitas Nusa Mandiri Jakarta Indonesia National Research and Innovation Agency Indonesia Computer Science Faculty of Information Technology Universitas Nusa Mandiri Jakarta Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

Machine learning has been employed to automatically detect the defects on car engines in several studies. One of crucial challenges on applying machine learning is on the amount of defect data collected is often large with high dimensional data, making manual detection inefficient and inaccurate. The other problem is on the missing data as oftentimes the collected data are incomplete. In this paper, we employ machine learning frameworks for engine defect detection. It comprises the data pre-processing stage which includes imputing missing value data, then performing feature correlation using the Pearson method, and selecting the features to use. After that, the label encoder and standard scaler are carried out. The experimental process begins with creating a baseline, then continues with improving imbalance data using SMOTE, and feature reconstruction using variational autoencoder (VAE). Afterwards, for classification, we employ convolutional neural networks (CNN). The proposed method achieved precision 99.63%. We collect engine quality dataset of 224,239 data with 90 features from major automobile manufacturing in Indonesia. This showed that SMOTE and Variational Autoencoder dimensional reconstruction method can overcome defect predictions in car engine defect data with data imbalance conditions. This novel methodology distinguished our study from prior methods and shows considerable increases in precision and recall matrix.

关键词： Representation learning Technological innovation Computational modeling Predictive models Computational efficiency Automobiles Convolutional neural networks Engines Tuning Defect detection

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Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

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arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

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Monitoring Applications for Vehicle based on Internet of Things (IoT) using the MQTT Protocol 8

Monitoring Applications for Vehicle based on Internet of Thi...

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

作者： Nurudin Sukmandhani, Arief Agus Zarlis, Muhammad Computer Science Department BinusOnline Learning Bina Nusantara University Jakarta11480 Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

Monitoring of vehicle conditions is needed as one of the securities in driving because one of the accident rates is negligence in checking the condition of the vehicle. This study aims to help reduce the level of accidents and increase human awareness for vehicle conditions, especially minibus type vehicles. The system works to provide information on the condition of vehicles sourced from the Engine Control Unit (ECU). ECU data cars are obtained by SNIP traffic broadcast message and ask for data directly at the ECU. The car can provide vehicle information using a Canbus communication protocol through the OBD-II socket. Vehicle information can be seen by users through the website in real time. Based on the evaluation from the user side, this system provides ease of monitoring vehicle conditions because it can be accessed via the internet using various kinds of devices. © 2023 The Authors. Published by Elsevier B.V.

关键词： Blackbox ECU OBD-II Sstem Logging

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C2PD: Continuity-Constrained Pixelwise Deformation for Guided Depth Super-Resolution 39

C2PD: Continuity-Constrained Pixelwise Deformation for Guide...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kang, Jiahui Cai, Qing Tan, Runqing Liu, Yimei Liu, Zhi Faculty of Computer Science and Technology Ocean University of China China School of Information Science and Engineering Shandong University China

ISBN: (纸本)157735897X

Guided depth super-resolution (GDSR) has demonstrated impressive performance across a wide range of domains, with numerous methods being proposed. However, existing methods often treat depth maps as images, where shading values are computed discretely, making them struggle to effectively restore the continuity inherent in the depth map. In this paper, we propose a novel approach that maximizes the utilization of spatial characteristics in depth, coupled with human abstract perception of real-world substance, by transforming the GDSR issue into deformation of a roughcast with ideal plasticity, which can be deformed by force like a continuous object. Specifically, we firstly designed a cross-modal operation, Continuity-constrained Asymmetrical Pixelwise Operation (CAPO), which can mimic the process of deforming an isovolumetrically flexible object through external forces. Utilizing CAPO as the fundamental component, we develop the Pixelwise Cross Gradient Deformation (PCGD), which is capable of emulating operations on ideal plastic objects (without volume constraint). Notably, our approach demonstrates state-of-the-art performance across four widely adopted benchmarks for GDSR, with significant advantages in large-scale tasks and generalizability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Forecasting Malaysian Palm Oil Futures Using Random Forest and LSTM Networks: Enhanced Predictive Modeling with Climate-Based Parameters 6

Forecasting Malaysian Palm Oil Futures Using Random Forest a...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wu, Meng Wei, Yi Sun, Yanhong Huang, Dengke Subramaniam, Geetha Li, Zeyu School of Information Science and Technology Sanda University Shanghai201209 China Faculty of Education Languages Psychology and Music SEGi University Selangor Petaling Jaya47810 Malaysia Computer Science and Artificial Intelligence Department Leiden University Leiden2311 EZ Netherlands

ISBN: (纸本)9798331541699

This study presents a comprehensive analysis of machine learning approaches for predicting palm oil futures prices in Malaysia. The research employs Random Forest (RF) and Long Short-Term Memory (LSTM) models to evaluate the impact of both conventional market indicators and climate factors on price prediction accuracy. Through rigorous analysis of 112 potential indicators across agricultural, commodity, Malaysia-specific, economic, and climate categories, 83 core indicators were identified for the predictive models. Results demonstrate that the RF model consistently outperformed LSTM, achieving a Mean Absolute Percentage Error (MAPE) of 7.45% compared to LSTM's 13.36% in multivariate analysis. The incorporation of climate factors significantly improved LSTM model performance, reducing its MAPE from 21.59% to 13.36%, while showing minimal impact on RF model accuracy. Notably, the RF model exhibited superior performance in long-term (100-day) forecasting compared to short-term (15-day) predictions, with MAPE values of 3.61% and 7.45% respectively. This research provides valuable insights into agricultural commodity trading and risk management, particularly highlighting the effectiveness of RF models for palm oil futures price prediction and the potential benefits of incorporating climate variables in forecasting models. © 2024 IEEE.

关键词： Prediction models

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Automatic Smart Crawling on Twitter for Weather information in Indonesia 8

Automatic Smart Crawling on Twitter for Weather Information ...

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

作者： Purwandari, Kartika Perdana, Reza Bayu Sigalingging, Join W.C. Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate. © 2023 The Authors. Published by Elsevier B.V.

关键词： smart crawling Social media SVM Twitter weather

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Enhancing Computational Thinking Based on Virtual Robot of Artificial Intelligence Modeling in the English Language Classroom 1

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Muthmainnah Obaid, Ahmad J. Al Yakin, Ahmad Brayyich, Mohammed Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq National University of Science and Technology Thi-Qar Iraq

ISBN: (数字)9789819965502

ISBN: (纸本)9789819965496

There has been a lack of both educational focus on computational thinking (CT) and research into effective methods of assessing CT competence. This research takes a comprehensive look at the methods used to grade CT in higher education. This study examines the use of educational robotics (ER) as a teaching strategy in higher education, with particular attention to how undergraduate students might respond differently to this kind of learning. Students collaborate using artificial intelligence to find out how to program robots (start installing, interact, sharing information, feedback, and evaluate). Of 181 undergraduates’ students from Indonesia, Saudi Arabia, Riyadh, Pakistan, the Philippines, Egypt, Algeria, Lesoto, Cameron, Bangladesh, Sudan, Uganda, Portugal, Ukraine, Pakistan, and England make up the study groups. These students are enrolled in the first semester of the 2022 academic year. In this work, researchers employed a quantitative method technique. During the exercise, the participants were closely monitored to assess their performance in relation to the core CT constructs in the robotics class. These constructs include confidence in dealing with complexity, persistence in problem-solving, tolerance for ambiguity, ability to handle open-ended problems, effective communication skills, and teamwork abilities. In order to enhance the implementation of strategic initiatives. To facilitate more strategic learning, this study found that the use of visual robotics of artificial intelligence helped to developing CT and the respondents strong believe it to increase students’ confidence in dealing with complexity, persistence in working through difficult questions, tolerance for ambiguity, ability to handle open problems, communication skills, and teamwork. It is thought that this study would be valuable to academics, curriculum designers, and teachers because students who play artificial intelligence games exhibit considerably greater cognitive flexibility than b

关键词： Students

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Novel hybrid data-driven models for enhanced renewable energy prediction

引用

Frontiers in Energy Research 2024年 12卷

作者： Alharbi, Talal Iqbal, Saeed Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Information Technology and Computer Science University of Central Punjab Lahore Pakistan

Global power grid management depends on accurate solar energy estimation, yet present prediction techniques frequently suffer from unreliability as a result of abnormalities in solar energy data. Solar radiation projections are affected by variables such as anticipated horizon length, meteorological classification, and power measuring techniques. Therefore, a Solar Wind Energy Prediction System (SWEPS) is proposed as a solution to these problems. It improves renewable energy projections by taking sun trajectories and atmospheric characteristics into account. In addition to using a variety of optimization methods and pre-processing techniques, such as Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Least Absolute Shrinkage Selection Operator (LASSO), and recursive feature addition processes (RFA), complemented by a genetic algorithm for feature selection (GAFS). The SWEPS also makes use of sophisticated machine learning algorithms and Statistical Correlation Analysis (SCA) to find important connections. Neural Network Algorithms (NNA) and other metaheuristic techniques like Cuckoo Search Optimization (CSO), Social Spider Optimization (SSO), and Particle Swarm Optimization (PSO) are adopted in this work to increase the predictability and accuracy of models. Utilizing the strengths of machine learning and deep learning techniques (Artificial Neural Networks (ANN), Decision Trees, Support Vector Machine (SVM), Recurrent Neural Networks (RNN), and Long Short Term Memory (LSTM)) for robust forecasting, as well as meta-heuristic optimization techniques to fine-tune hyper-parameters and achieve near-optimal values and significantly improve model performance, are some of this work contributions to the development of a comprehensive prediction system. Copyright © 2024 Alharbi and Iqbal.

关键词： Decision trees

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Bibliometric Analysis of Using IT-GRC for Corporate Resilience and Sustainability 2

Bibliometric Analysis of Using IT-GRC for Corporate Resilien...

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2nd International Conference on Software Engineering and information technology, ICoSEIT 2024

作者： Mahendra, Irfan Prabowo, Harjanto Warnars, Harco Leslie Hendric Spits Tjong, Yanti Bina Nusantara University Computer Science Jakarta Indonesia Nusa Mandiri University Faculty of Information Technology Jakarta Indonesia Bina Nusantara University Management Department Jakarta Indonesia Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350317503

GRC and IT-GRC are currently playing an essential role and receiving a lot of attention. GRC has a role to help companies ensure the attainment of business goals and prevent losses that can cause company losses. This research was conducted to carry out a bibliometric analysis and literature review on GRC and IT-GRC to determine research trends and topics, as well as the current information technology discussed in available research on GRC. In this research, we propose a future GRC research agenda that is related to research topics, industries that need to be supported, and what modern information technology can be used to encourage the successful implementation of GRC, to increase the role of GRC in increasing company resilience in facing turbulence, increasing business, and helping companies in achieving sustainable growth. © 2024 IEEE.

关键词： Risk assessment

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ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging 26

ProMIL: Probabilistic Multiple Instance Learning for Medical...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Struski, Lukasz Rymarczyk, Dawid Lewicki, Arkadiusz Sabiniewicz, Robert Tabor, Jacek Zieliński, Bartosz Faculty of Mathematics and Computer Science Jagiellonian University Poland Faculty of Applied Computer Science University of Information Technology and Management Rzeszow Poland Department of Pediatric Cardiology and Congenital Heart Diseases Medical University of Gdansk Poland Ues Ltd. Doctoral School of Exact and Natural Sciences Jagiellonian University Poland

ISBN: (纸本)9781643684369

Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available. © 2023 The Authors.

关键词： Medical applications

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