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2nd International Conference on Data, information and Computing Science, CDICS 2024

作者： Wijayakusuma, I.G.N. Lanang Sudarma, Made Putra, I. Ketut Gede Darma Sudana, Oka Engineering Study Program Universitas Udayana Bukit Jimbaran Indonesia Electrical Engineering Study Program Universitas Indonesia Bukit Jimbaran Indonesia Information Technology Study Program Universitas Udayana Bukit Jimbaran Indonesia

ISBN: (纸本)9781643685762

This research delves into the comparative assessment of diverse deep learning architectures for the automated identification of pulmonary diseases in chest X-ray images, aligning with the diagnostic framework of the International Classification of Deseases, 10th Revision (ICD-10). The evaluated architectures encompass Convolutional Neural Network (CNN), MobileNetV2, DenseNet, VGG16, and InceptionV3. Through an extensive investigation conducted on a comprehensive dataset comprising 108,948 chest X-ray images, we scrutinize the performance metrics and diagnostic accuracy of each architecture. Notably, our findings reveal MobileNetV2 as the frontrunner, exhibiting an outstanding accuracy rate of 97% for Emphysema disease. This remarkable accuracy underscores the robustness and efficiency of MobileNetV2, even amidst large-scale diasets, positioning it as a promising solution for the automated detection of pulmonary ailments from medical imaging. Additionally, we analyze the computational efficiency and resource requirements of each architecture, providing insights into their practical applicability in real-world clinical settings. The implications of our study extend beyond technical benchmarks, offering valuable insights for healthcare practitioners, researchers, and developers working towards enhancing diagnostic processes in pulmonary healthcare. By elucidating the comparative strengths and limitations of various deep learning architectures, this research contributes to the advancement of computer-aided diagnosis systems, paving the way for more accurate and efficient detection of lung diseases from chest X-ray imaging data. © 2025 The Authors.

关键词： Lung cancer

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Offsets Reconstruction Method for Clock Skew Measurement Over High-Jitter Communication

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SN Computer Science 2025年第5期6卷 1-14页

作者： Sastra, Nyoman Putra Saputra, Komang Oka Teng, Wei-Chung Study Program of Electrical Engineering Faculty of Engineering Udayana University Jalan Raya Kampus Bukit Jimbaran Bali Badung 80361 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology No. 43 Section 4 Keelung Rd Taipei 106 China

Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the clock skew sought. When some outliers separate from the thick line due to higher delay jitter, current advanced approaches use the lower bound or the majority of offsets to derive accurate clock skew. However, outliers in offsets that shape parallel dotted lines, reduce the availability of offsets in the lower region, and scatter widely the offsets from their majority zone. These conditions drive the current skew estimation approaches to lose their accuracy. This paper then developed a new method, which reconstructs back the outliers into their origin positions, in order to provide ideal offsets for clock skew measurement. The experimental evaluations have demonstrated that the clock skew accuracy of dotted-lines offsets tampered by outliers was improved from 2.1 to 0.9 ppm by the proposed reconstruction method. For a broader purpose, the reconstruction method has been adjusted to be implemented also on non-dotted-lines offsets. The robustness of the proposed method for this case has also been exhibited by the very ideal reconstructed offsets that shape like a thick line. © The Author(s) 2025.

关键词： Clock skew Delay jitter Dotted lines Hough transform

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Analysis Geomorphic Flood Index in Determining Flood-Prone Areas in East Kalimantan Province, Indonesia

Analysis Geomorphic Flood Index in Determining Flood-Prone A...

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2024 Annual Conference, ASPRS 2024

作者： Virtriana, Riantini Amalia, Annisa Fitri Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Harto, Agung Budi Deliar, Albertus Geospatial Information Science and Technology Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung40132 Indonesia Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung40132 Indonesia Universitas Pendidikan Indonesia Bandung40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung40132 Indonesia

In 2022, there are 1,524 flood events out of a total of 3,531 disasters occurring in Indonesia. East Kalimantan Province ranked 20th among the 35 provinces in Indonesia with 28 flood occurrences in 2022, resulting in two fatalities. This highlights the urgent need for addressing flood hazard to prevent future casualties. There are some methods that can be used to determine the flood hazard in a region. In this research aims to identify flood-prone areas in East Kalimantan Province and evaluate the effectiveness of different mapping methods. The methodology that compares are the Pairwise Comparison method and Geomorphic Flood Index (GFI). The study's results provide flood-prone area maps of East Kalimantan Province using both methods, recommend the more efficient method for flood-prone area identification, and offer mitigation strategies based on the findings. This research is intended to serve as a reference for selecting the best method for identifying flood-prone areas and for informing policy decisions to mitigate flood impacts in East Kalimantan Province. © Author(s) 2025.

关键词： Hazards

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A Robust Resource-Aware Framework for YOLO-Based Object Detection in Dynamic and Multi-Platform Resource-Constrained Environments

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IEEE Access 2025年 13卷 79977-79995页

作者： Shiddiqi, Ary Mazharuddin Navastara, Dini Adni Studiawan, Hudan Romadhona, Yasinta Azhar, Muhammad Fath Mushaffa Rahma, Fayruz Bramantoro, Arif Alzahrani, Ahmad A. Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Surabaya60111 Indonesia Informatics Study Program Telkom University Surabaya Campus Surabaya60231 Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta55584 Indonesia Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri BegawanBE1410 Brunei King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia

Efficient management of resources such as CPU, RAM, and disk storage is a critical challenge for object detection systems that rely on multi-camera video streaming, especially for resource-constrained devices. These constraints frequently result in the risk of overheating and potential system failure during high workloads. This paper presents a novel Resource-Aware Framework (RA-Framework), designed to optimize resource allocation using dynamic adaptation mechanisms. The RA-Framework dynamically manages CPU, RAM, and disk storage resources for YOLO-based object detection, supporting multiple camera streams and scheduling mechanisms. The framework was implemented in resource-constrained environments using various YOLO models with single and dual-camera setups. Experimental results showed that the RA-Framework successfully reduced average CPU usage by up to 68.62%, decreased RAM consumption by approximately 13.47%, and extended the time for disk usage to increase by 1% by more than 500% in specific scenarios. In contrast, systems without the RA-Framework experienced higher resource utilization and greater susceptibility to resource scarcity or even overheating. © 2025 IEEE.

关键词： Resource allocation

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Classification Of Ornamental Fish Types Using CNN Method With VGG-16 And U-NET Architecture

Classification Of Ornamental Fish Types Using CNN Method Wit...

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Consumer technology (ICCT-Pacific), International Conference on

作者： Ni Luh Wiwik Sri Rahayu Ginantra Christina Purnama Yati I Gusti Ayu Agung Diatri Indradewi I Komang Agus Yudiawan Technology and Informatics Informatics Engineering Institut Bisnis dan Teknologi Indonesia (INSTIKI) Denpasar Indonesia Department of Informatics Engineering Information Systems Study Program Faculty of Engineering and Vocational Studies Universitas Pendidikan Ganesha (UNDIKSHA) Denpasar Indonesia

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Ornamental fish are a type of fish that live in freshwater or seawater habitats and are typically maintained for aesthetic purposes, often in ornamental ponds or as household pets. The diversity of colors, shapes, and patterns observed in ornamental fish presents a challenge in their classification. Therefore, a method capable of accurately recognizing the visual characteristics of these fish is required. The objective of this study is to evaluate the performance of two Convolutional Neural Network (CNN) architectures, specifically VGG-16 and U-Net, using five distinct types of ornamental fish, namely Angel Fish, Betta Fish, Clown Fish, Gold Fish, and Guppy Fish. The dataset comprises 3.240 images that have been processed using TensorFlow and Keras on Google Collaboratory. The results demonstrated that U-Net demonstrated superior performance in segmentation, with an Average Intersection over Union (IoU) of 58.91 % and an Average Dice Coefficient of 73.72%. In contrast, VGG-16 exhibited signs of overfitting, as evidenced by lower evaluation metrics. To enhance the efficacy of the transfer learning model, further optimization is recommended.

关键词： Measurement Image segmentation Visualization Shape Transfer learning Fish Internet Convolutional neural networks Optimization Overfitting

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Herb compounds screening as meningitis inhibitor candidates using neural network and random forest methods

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AIP Conference Proceedings 2025年第1期3250卷

作者： Riska Aprilia Mohammad Hamim Zajuli Al Faroby Muhammad Adib Kamali Muhammad Dzulfikar Fauzi Information Technology Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Data Science Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Informatics Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia

Meningitis is an inflammation of the meninges that occurs in the protective lining of the brain and spinal cord caused by bacterial, viral, or fungal infections. This disease is difficult to recognize because it has initial symptoms like the flu where the patient has a fever and headache. Current efforts to prevent the disease by strengthening antibodies. Meanwhile, drug candidates for the treatment of this disease still have not found optimal results in reducing mortality due to meningitis. This study aims to find and analyses herbal compound candidates that might be inhibitors of meningitis. Compound data was acquired from a validated open database. The data acquired are smiles of the chemical bond structure of the compounds. In the data processing process, compound feature extraction is required by applying the concept of molecular fingerprint. The results of feature extraction are used as datasets to build classification models by applying the Multilayer Perceptron (MLP) and Random Forest algorithms. The two models are compared, and a more robust model is selected to be used as a prediction model for herbal compound search. The MLP model has a better accuracy of 0.97 compared to the Random Forest model. The results of screening using the MLP learning model obtained Symphytine, cis-Linalool oxide, and 3-O-Methylcalopocarpin compounds have the highest probability compared to thousands of other herbal compounds. This candidate compound can be used as a recommendation for drug discovery to treat patients who contract Meningitis.

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning Remote Sensing

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Design of Dual Space Inquiry framework for facilitating flexible learning in digital technology era

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International Journal of Educational Research Open 2025年 8卷

作者： Novitra, Fuja Abdullah, Mohd Nor Syahrir Özdemir, Erdogan Riyasni, Selma Emiliannur Festiyed Metra, Pol Department of Physics Faculty of Mathematics and Natural Science Universitas Negeri Padang Indonesia Department of Mathematics and Science Education Faculty of Education University of Malaya Malaysia Vocational School of Health Services Sivas Cumhuriyet University Turkey Information Systems Study Program Faculty of Science and Technology UIN Sulthan Thaha Saifuddin Jambi Indonesia

Mixed learning that offers flexibility is a current requirement. However, the challenge lies in ensuring that these approaches effectively accommodate interactive and practical elements, especially in physics education. This research aims to design a conceptual framework of the Dual Space Inquiry for facilitating flexible learning in basic physics course. An educational design research (EDR) approach was used to develop and validate the Dual Space Inquiry framework comprising three phases: analysis and exploration;design and construction;and evaluation and reflection. This research involved 253 students and 20 experts and practitioners. The findings highlighted the urgency of implementing a mixed learning framework that combines face-to-face and online learning methods in physics education contexts to optimize learning outcomes. These results became the basis for the design of the Dual Space Inquiry Framework which emphasizes flexibility, humanism, and student-centered. Furthermore, the results of expert and practitioner judgment showed that the framework's validity is in the high category (v-values=0.770). Based on the results, it is concluded that an approach that combines online and offline learning with inquiry activities integration in a balanced manner is recommended for optimal learning outcomes for physics education. The Dual Space Inquiry Framework, with its high validity, is a valuable framework for lecturers in physics and other science fields to enhance learning quality and adapt to the digital technology era. © 2024 The Author(s)

关键词： Digital pedagogy Framework ICT Inquiry Teaching strategies

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