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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Miranda, Eka Mutiara, Achmad Benny Ernastuti Wibowo, Wahvu Catur Information Systems Department Doctoral Program in Information Technology Gunadarma University School of Information Systems Bina Nusantara University Jakarta Indonesia Faculty of Computer Science and Information Technology Gunadarma University Jakarta Indonesia Computer Science Faculty Universitas Jakarta Indonesia

ISBN: (纸本)9781538658215

This paper intended to classify land cover of high-resolution satellite image using supervised classification method. The object of this research was the land cover image of the central Java area in Indonesia which chosen as an area observed with the cloud cover consideration. The Satellite image was obtained from Sentinel-2 through https://***. Supervised Classification and ArcMap 10.5 was used to classified image object. This research classified the land cover into four classifications class namely Water, Forest, Urban and Bare Land due to show the main classes in the Land Cover Classification of RSNI-l National Standardization Body of Indonesia, RSNI-l was used as reference for classification process. The confusion matrix used to calculate classification accuracy value. Subsequently, confusion matrix results compared to the truth information. The truth information was derived from the actual value of RASTERVALUE obtained from Google Earth. This research shown supervised classification maximum-likelihood classification has been classified land cover into four class (forest, water body, urban and bare land) with overall accuracy = 1 and kappa value = 0.4896. This result showed this research got moderate kappa accuracy value but high overall accuracy value. High accuracy value reached due to fully supervised experiment during classification process. © 2018 IEEE.

关键词： Standardization

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Hand-Woven Fabric Motif Recognition Methods: A Systematic Literature Review 3

Hand-Woven Fabric Motif Recognition Methods: A Systematic Li...

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3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Noprisson, Handrie Ermatita, Ermatita Abdiansah, Abdiansah Ayumi, Vina Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Knowledge of hand-woven motif recognition is only owned by the older generation, which has not been passed down to the younger generation. Moreover, computer technology can be used to support the recognition of traditional woven fabric motifs. The aim of this research is to conduct a literature review on hand-woven fabric motif recognition, with an emphasis on performance accuracy, techniques, and datasets utilized. This research included three distinct stages of systematic literature review (SLR): planning, execution, and reporting of findings. We found several datasets between 924,845 data and the number of classes between 3-25 class. Based on research result, we obtained several methods for hand-woven fabric motif recognition focused on performance examination. We recommended image pre-processing method, including Adaptive Filtering Denoising, Adaptive Wiener Filtering, Histogram Equalization, Gradient Pyramid (GP) Decomposition. Moreover, we suggested five feature extraction methods, including Radon Transform, Wavelet Transform, Locally Rotation Invariance Measure (LBP-ROR), Transform Invariant Low-Rank Textures (TILT) and Histogram of Oriented Gradients (HOG). For learning method, we recommend Fuzzy C-Mean (FCM), Convolutional neural network (CNN), Deep Neural Network (DNN), MobileNets, Inception-v3 and ResNet-50. © 2021 IEEE.

关键词： Weaving

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Clustering Satellite Images of Forest Area Change in Nabire Regency with NDVI, SAVI, MSAVI, VCI, TCI, dan VHI Method 6

Clustering Satellite Images of Forest Area Change in Nabire ...

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

作者： Prayitno, Gunawan Sediyono, Eko Sembiring, Irwan Prasetyo, Sri Yulianto Adi, Puput Dani Prasetyo Satya Wacana Christian University Indonesia Doctoral Program of Computer Science Faculty of Information Technology Salatiga Indonesia Satya Wacana Christian University Indonesia Salatiga Indonesia Bandung Indonesia

ISBN: (纸本)9798350358346

Based on data from the Forestry and Plantation Service of Nabire Regency, the forest area in Nabire Regency is 3.26% of the total forest area in Papua, which is 42,224,840 Ha. The existence of forests in Nabire Regency greatly supports the survival of unique animals such as cuscus, birds of paradise, cassowary, peacocks, and parrots, and there are also various types of reptiles, mammals, and insects. By looking at the diversity of animals and ornamental plants in the forests of Nabire Regency, further research is needed to see the development and change of forests as an effort to estimate forest destruction. This research focuses on developing a research model to classify forest areas as vulnerable to collapse. The methods used in this study used OLI Landsat 8 satellite imagery and advanced analysis methods to assess and predict potential forest destruction in Nabire District, Indonesia;using OLI Landsat 8 satellite imagery and multi-band sensors, the developed research model can present a systematic approach that understands forest dynamics. Research has several stages, such as data preparation, data classification, vegetation index analysis, interpolation, and grouping of vegetation index data. Applying vegetation indices such as NDVI, SAVI, MSAVI, VCI, TCI, and VHI yields insights into vegetation health and distribution, enabling comprehensive monitoring of ecosystem change. © 2023 IEEE.

关键词： Landsat

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Developing the Pointer Backward-Forward Algorithm in Token of Test in Text to Know the Level of Individual Depression 1

Developing the Pointer Backward-Forward Algorithm in Token o...

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1st International Conference of SNIKOM 2018, ICoSNIKOM 2018

作者： Fahmi, Hasanul Zarlis, Muhammad Situmorang, Zakarias Nababan, Adli A Dalimunthe, Yulia A Annisah, Yessi F Khairani, Mufida Departement of Computer Science Doctoral Program Faculty of Computer Science and Information Technology Sumatera Utara University St. Universitas No.9A Medan Indonesia Computer Science Departement Universitas Katolik Santo Thomas Indonesia STMIK Pelita Nusantara Sumatera Utara Indonesia Teknik Informatika-Universitas Harapan Medan Indonesia

The way you move and sleep, interact with people around you, depression change everything. This even looks at the way you talk and express yourself in writing. Sometimes this "depression language" can have a strong effect to others. Natural Language Processing (NLP) is one of the fields of computer science, artificial intelligence, and language (linguistics) related to the interaction between computers and natural human languages, such as Indonesian or English. The main objective of the NLP study is to make a machine that is able to know and understand the meaning of language, lexical analysis has the role of reading input characters and generating output tokens and making spaces, tabs, newlines and connecting rows of programs. However, the problems will occur when lexical analyzers where lexical is not able to determine when there are misspellings on tokens. Therefore, with the development of the backward-forward pointer algorithm, it is expected that in the future it will be easier to analyze both the words in front, in the middle and behind, so that the machine can quickly determine the individual depression level of speech tokens and speech tones. © Published under licence by IOP Publishing Ltd.

关键词： Natural language processing systems

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Enhancing safety with an AI-empowered assessment and monitoring system for BSL-3 facilities

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Heliyon 2025年第1期11卷 e40855页

作者： Fan, Yi-Ling Hsu, Ching-Han Wu, Ju-Yu Tsai, Ying-Ying Chen, Wei J. Lee, Min-Shi Hsu, Fang-Rong Liao, Lun-De Institute of Biomedical Engineering and Nanomedicine National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan Department of Biomedical Engineering & Environmental Sciences National Tsing-Hua University Hsinchu Taiwan Doctoral Program in Tissue Engineering and Regenerative Medicine National Chung Hsing University Taichung Taiwan Center for Neuropsychiatric Research National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes 35 Keyan Road Miaoli County Zhunan Town 350 Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung 407 Taiwan

Introduction: The COVID-19 pandemic has created an urgent demand for research, which has spurred the development of enhanced biosafety protocols in biosafety level (BSL)-3 laboratories to safeguard against the risks associated with handling highly contagious pathogens. Laboratory management failures can pose significant hazards. Methods: An external system captured images of personnel entering a laboratory, which were then analyzed by an AI-based system to verify their compliance with personal protective equipment (PPE) regulations, thereby introducing an additional layer of protection. A deep learning model was trained to detect the presence of essential PPE items, such as clothing, masks, hoods, double-layer gloves, shoe covers, and respirators, ensuring adherence to World Health Organization (WHO) standards. The internal laboratory management system used a deep learning model to delineate alert zones and monitor compliance with the imposed safety protocols. Results: The external detection system was trained on a dataset consisting of 4112 images divided into 15 PPE compliance classes. The model achieved an accuracy of 97.52 % and a recall of 97.03 %. The identification results were presented in real time via a visual interface and simultaneously stored on the administrator's dashboard for future reference. We trained the internal management system on 3347 images, achieving 90 % accuracy and 85 % recall. The results were transmitted in JSON format to the internal monitoring system, which triggered alerts in response to violations of safe practices or alert zones. Real-time notifications were sent to the administrators when the safety thresholds were met. Conclusion: The BSL-3 laboratory monitoring system significantly reduces the risk of exposure to pathogens for personnel during laboratory operations. By ensuring the correct use of PPE and enhancing adherence to the imposed safety protocols, this system contributes to maintaining the integrity of BSL-3 facilities

关键词： Bioengineering Biomedical instrumentation Biomedical laboratory safety Biosafety level-3 laboratory

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A Preliminary Study of Decision Support Model of Photovoltaic for Village Area in South of Sumatera 4

A Preliminary Study of Decision Support Model of Photovoltai...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Ardianto, Feby Ermatita, Ermatita Sofijan, Armin Ayumi, Vina Noprisson, Handrie Purba, Mariana Universitas Sriwijaya Doctoral Program in Engineering Science Palembang Indonesia Universitas Muhammadiyah Palembang Engineering Faculty Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Engineering Faculty South Sumatera Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

Indonesia has a very high potential for solar energy irradiation, including in 66 countries in the sunbelt area. This can be alternative for power generation system that converts energy sources from solar radiation into electricity through solar cells or photovoltaic cells. This research goal is to develop an initial decision support model for the photovoltaic village area in South Sumatra, Indonesia. This research was conducted six months in Pandan Arang Village, Kandis District, Ogan Ilir Regency, South Sumatra. As the result, the proposed model is consisted of four sub-system, including photovoltaic system, data logger, database management and decision support system. Based on the results of experiments on solar panels with the parameters of current, voltage and light intensity, the highest value of light intensity was 1989.1 W/m2, while the voltage and current of 15.49V and current of 14.91 A were displayed through a 16x2 LCD display which served as information display in real time. The expected result in this study is an alternative fault detection PV system in the form of normal conditions, shadows, and overloads. © 2022 IEEE.

关键词： Decision support systems

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Comparison of Machine Learning Text Classification for Intent Sentiment Analysis 7

Comparison of Machine Learning Text Classification for Inten...

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7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Muhammad, Abulwafa Defit, Sarjon Cahyo, Gunadi Widi Nur Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Informatics Technology Padang Indonesia Universitas Putra Indonesia Yptk Padang Doctoral Program in Information Technology Padang Indonesia

ISBN: (纸本)9798350382266

Tourist destination reviews on Google Maps have become a valuable point of reference for visitors seeking enjoyable spots to visit. Additionally, users can gain insight into the reasons for writing reviews. Text classification is used to determine the motivation behind these reviews. The aim of this study is to compare machine learning (ML) text classification techniques for identifying the intent behind sentiments expressed as complaints (0), suggestions (1), opinions (2), statements(3), and awards (4). The ML techniques assessed are Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To achieve this, tourist destination review data sets, comprising 738 reviews, were collected using web scraping from the Google Maps website. Preprocessing stages, including case-folding, tokenisation, filtering and stemming, are executed prior to the data being prepped for feature extraction with TF-IDF. The employed classification models perform well with KNN achieving 1.00 accuracy on a 90:10 data split, and SVM obtaining 0.90 on the same data split. Although, the Multinomial Naive Bayes algorithm with 0.70 accuracy on a 90:10 data split is classified as fair. Comparison of methods was conducted by means of a Receiver Operating Characteristic (ROC) diagram. The testing accuracy for the SVM method was found to be 95%, MNB at 91%, and KNN at 84%. © 2023 IEEE.

关键词： Sentiment analysis

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Detect Areas of Upward and Downward Fluctuations in Bitcoin Prices Using Patterned Datasets 9

Detect Areas of Upward and Downward Fluctuations in Bitcoin ...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Parlika, Rizky Mustafid Rahmat, Basuki Pascasarjana School Diponegoro University Department of Doctoral Program of Information System Semarang Indonesia University of Pembangunan Nasional 'Veteran' Jawa Timur Department of Informatics Faculty of Computer Science Surabaya Indonesia

ISBN: (纸本)9798350306835

Many researchers have proposed various theories and techniques to estimate the value of Bitcoin. However, there is still a great opportunity to suggest a new approach to Bitcoin forecasting. This research will examine the concept of forming a patterned dataset to prove the hypothesis regarding the start of a bitcoin price fluctuation until the end of the fluctuation using patterned dataset pairs in conditions of price decline (moon) and in conditions of price decline (crash). Patterned datasets were initially built from data on the movement of all cryptocurrency assets on a digital cryptocurrency trading market. The Patterned Dataset is programmed based on the response of all crypto assets to changes in the price of bitcoin over time. Patterned datasets are better able to describe the conditions of the digital cryptocurrency market, along with patterns of potential increases and decreases in Bitcoin prices in particular and cryptocurrencies in general. To deepen the observations, resampling of the patterned dataset was carried out with a 4-hour timeframe using the mean, median, min, max, and sum filter functions on pairs of crash and moon conditions per month. The results show that filter functions other than sum, namely mean, median, min, and max, provide markings of fluctuation area patterns that are easier to recognize. Two hypotheses related to fluctuations were also successfully proven in this research. © 2023 IEEE.

关键词： Bitcoin

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Improved Chest X-Ray Image Quality Using Median and Gaussian Filter Methods 4

Improved Chest X-Ray Image Quality Using Median and Gaussian...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Kesuma, Lucky Indra Ermatita, Ermatita Erwin, Erwin Sari, Purwita Purabaya, Rudhy Ho Universitas Sriwijaya Doctoral Program in Engineering Faculty of Engineering Palembang Indonesia Universitas Sjakhyakirti India Universitas Sriwijaya Information System Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer System Palembang Indonesia Universitas Sriwijaya Informatic Management Faculty of Computer Science Palembang Indonesia Universitas Pembangunan Nasional Veteran Jakarta Information System Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665473279

The lungs are one of the organs of the body that are responsible for the human respiratory process and are very susceptible to dangerous diseases. For this reason, early detection and diagnosis of lung organs is needed, one of which is through examination of Chest X-Ray (CXR) images. Examination of the results of CXR images is still done manually by doctors and radiologists, this requires time and high accuracy. To facilitate the examination, it is necessary to image quality enhancement in order to get better image quality results so as to produce an accurate diagnosis. The initial stage in this research is to apply the contour improvement technique to the image using Morphology Opening, and followed by noise reduction using Median Filter and Gaussian filter. The results of the two methods of noise reduction are compared with the results of image quality in order to find out the best method that can be applied. The implementation of image quality enhancement results was measured quantitatively using Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index Metrics (SSIM). In the Morphology Opening and Median Filter methods, the values obtained are 39.187, 22.252, and 0.952, respectively. Meanwhile, the Morphology Opening and Gaussian Filter methods obtained values of 38.717, 23.917 and 0.956. Based on these results, it can be concluded that both methods are able to improve image quality well. © 2022 IEEE.

关键词： Image quality

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