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arXiv 2024年

作者： Song, Hae Jin Krawczuk, Patrycja Huang, Po-Hsuan Department of Computer Science University of Southern California United States Neuroscience Graduate Program University of Southern California United States

Geospatial data come from various sources, such as satellites, aircraft, and LiDAR. The variability of the source is not limited to the types of data acquisition techniques, as we have maps from different time periods. To incorporate these data for a coherent analysis, it is essential to first align different "styles" of geospatial data to its matching images that point to the same location on the surface of the Earth. In this paper, we approach the image registration as a two-step process of (1) extracting geospatial contents invariant to visual (and any other non-content-related) information, and (2) matching the data based on such (purely) geospatial contents. We hypothesize that a combination of β-VAE-like architecture [2] and adversarial training will achieve both the disentanglement of the geographic information and artistic styles and generation of new map tiles by composing the encoded geographic information with any artistic style. Copyright © 2024, The Authors. All rights reserved.

关键词： Image registration

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Private Data Protection as Countermeasure Against Misuses of Medical Intelligent Technology Data: A Literature Study 8

Private Data Protection as Countermeasure Against Misuses of...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Praseptiadi Dahlian Persadha, Pratama Susandi, Armi Mantoro, Teddy State Intelligence College Medical Intelligence Post-graduate Program Bogor Indonesia Sampoema University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

Medical Technologies develop along with the development of the Industrial Revolution 4.0 (I.R. 4.0). In I.R. 4.0, a collaborative ecosystem is formed between cyber technology and automation technology to be able to create intelligent systems to facilitate various human activities, including activities related to health. The most important thing in the system is data since it is the basis for the development of various key icons technologies in the I.R. 4.0: Cloud Computing, Internet of Things (IoT), Big Data, Artificial Intelligence (Al), Augmented Reality, and Cyber Security. Unfortunately, with the variety of I.R. 4.0 products, the data produced by these products is also scattered everywhere. Misuse of this scattered data - which the owner may not even know where the data are stored - can harm the individual concerned, and even the very existence of the state. Healthcare services now are undergoing Digital Transformation in which these data were needed to build high-quality services. The Health Digital Transformation strategy changes the direction of health services to be simpler and easier to use by the public, increasing efficiency, and thus providing a higher quality health service system so that it can trigger better economic growth. On the regulatory aspect, The Private Data Protection Bill was proposed by the Indonesian government to protect the data of the Indonesian people. Literature studies are used to obtain knowledge between the three: technology development, digital transformation in healthcare, and private data protection. This study seeks how important private data protection regulation is to realize digitally transformed healthcare. The study conducted indicates Private Data Protection Bills have the means to guarantee the rights of the data owner and prevent their data (including medical intelligence data) to be misused. © 2022 IEEE.

关键词： Intelligent systems

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Analysis of Sofware Accounting Effectiveness on Data Sales in Bliss Kitchen 11

Analysis of Sofware Accounting Effectiveness on Data Sales i...

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11th International Conference on Communication and Network Security, ICCNS 2021

作者： Meiryani, Meiryani Franciska, Hanny Widuri, Rindang Liawatimena, Suryadiputra Daut, Zaidi Mat Ulinnuha, Hana Accounting Department Faculty of Economics and Communication Bina Nusantara University Jakarta11480 Indonesia Accounting Department Binus Graduate Program Master of Accounting Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Tourism Destination Faculty of Economic and Communication Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450386425

The development of the increasingly advanced business world will certainly always be related to technological developments, especially in the current 4.0 era. In Indonesia, there are various types of companies such as services, manufacturing, food, and others. The development of the company will cause more and more complex transactions. It is very unlikely that the company has to record all forms of operations with a manual system, especially for large companies with so much data that they need a system to help process data quickly so that all transactions can be processed into reliable information. complete and accurate so that the company's goals can be realized. The purpose of this study was to determine the effectiveness of accurate accounting software for recording sales reports in one of the companies that engaged in food & beverage, namely Bliss Kitchen. The research that the writer did used is a quantitative approach with a survey method. This study finds that Accurate software that applied in Bliss kitchen is effective. Evidence shows the results of the questionnaire from Bliss Kitchen employees is positive about Accurate. © 2021 ACM.

关键词： Sales

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Implementation of expert systems in potassium deficiency in cocoa plants using forward chaining method 7

Implementation of expert systems in potassium deficiency in ...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hafizal, Muhammad Titan Putra, Dian Pratama Wirianata, Herry Nugraha, Nanda Satya Suparyanto, Teddy Hidayat, Alam Ahmad Pardamean, Bens Faculty of Agriculture STIPER Institute of Agriculture Yogyakarta55282 Indonesia Faculty of Forestry STIPER Institute of Agriculture Yogyakarta55282 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As one of the largest exporters in the world, cocoa (Theobroma cacao L.) production in Indonesia provides an important contribution to the plantation sector that can, directly and indirectly, attribute to the national economic development. However, recent data shows a decline in cocoa productivity in Indonesia. This is arguably caused by various factors, including poor agricultural management and practices in cocoa plantations, that may lead to lower fertility in the soil and an increased risk of diseases and pests. Potassium deficiency is a major contributing factor to the low soil fertility that affects cocoa yields. Therefore, in this work, we implement an application with an expert system utilizing the forward chaining method to detect potassium deficiency in cocoa plants and then give a fertilization-based recommendation based on the plants' condition. The system employs a set of rules to identify symptoms related to the deficiency on the sample photo of a cocoa leaf according to the channels of red, green, and blue of the image. The sample images of cocoa leaves are submitted to the application with an easy-To-use interface that can show the scanning result and proceed to display the suggested quantity of fertilizers to prevent potassium deficiency. Implementing the system can contribute constructive impacts to improve current practices in the overall cropping system of cocoa plants. © 2022 Elsevier B.V.. All rights reserved.

关键词： Expert systems

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Code-Mixed Sentiment Analysis Using Machine Learning Approach - A Systematic Literature Review 4

Code-Mixed Sentiment Analysis Using Machine Learning Approac...

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4th International Conference on Informatics and Computational sciences, ICICoS 2020

作者： Tho, Cuk Warnars, Harco Leslie Hendric Spits Soewito, Benfano Gaol, Ford Lumban Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728195261

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis. © 2020 IEEE.

关键词： Sentiment analysis

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Text-based Depression Detection on Social Media Posts: A Systematic Literature Review

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Procedia computer science 2021年 179卷 582-589页

作者： David William Derwin Suhartono Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Due to the huge increase of awareness of mental health well-being, the detection of mental illness itself is starting to become a huge concern. Many psychiatrists found difficulties in identifying the existence of mental illness in a patient because of the complicated nature of each mental disorder, thus making it hard to give the appropriate treatment to the patient before it’s too late. However, due to the integration of social media into people’s daily life, this create an environment that may provide additional information regarding the mental disorder a patient bear. This study has been undertaken as a Systematic Literature Review (SLR), which is defined as a process of identifying, assessing, and interpreting the available resources to provide answers for a set of research questions. Analysis is made to answer questions regarding text-based mental illness detection based on the social media activity of people with mental disorders, and reveals that it indeed is possible to do early detection of depression on social due to the existence of a particular characteristics in the way these subjects use their social media. This SLR found that from the small amount of research using text-based approach, most studies use deep learning models such as RNN on the early detection of depression cases due to the limitation of data availability. However, this study will look to find method that may prove to be more effective.

关键词： Linguistic analysis Natural language processing Depression detection Social media

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Predicting Human Activity with LSTM Face Detection on Server Surveillance System

Predicting Human Activity with LSTM Face Detection on Server...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Alexander Nurenie Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Indonesia

Surveillance server technology was growth with new technology, effective, extra new features, human friendly, and human deals with big amount data, can't view and collect the data in the short time, and took time to analyze, playback video/picture to determine machine, human, vehicle or environment issues or performance, Surveillance Server Systems now which has the ability to face recognition, face detection, human detection, motion detection, license plate recognition, The authors perform this study that still new this research has never been done before to determine the efficacy of the LSTM in predicting human behavior (Long Short Term Memory) Face Detection on Server surveillance system, by taking log view data with a total of 91501 Face detection data downloaded from 10/18/2022~11/9/2022, the data will be processed using Python programming and training so that it can be used to predict the future regarding human activities that vary utilizing time series prediction LSTM include the number of daily activities, the highest and lowest numbers of days, and the maximum and minimum numbers of days. from the results of this study it was found to help to find out the days with the lowest number of humans and the days with the highest number of human activities, so that the owner can predict with sequence of the data the service would be provided when human activity is high in certain area or certain day, it can also can find out the maximum or minimum amount human counting day by day, and compare able some different date and location, the author will continue to do more in-depth research the others data related with prediction with deep learning server surveillance machine system interaction with human, vehicle behavior in the future studies.

关键词： Training Surveillance Time series analysis programming Behavioral sciences Servers Face detection

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia computer science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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Performance of news clustering using ant colony optimization

Performance of news clustering using ant colony optimization

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Jenson, T. Girsang, A.S. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Humans are basically curious creatures. We always look for all the latest information through books or news. News usually contains tags or categories to make it easier to find similar news. It can be done manually by a human or with a machine to make it easier. Data mining techniques such as clustering and classification can be used to help the categorization process. Clustering is a technique that can be used to grouped news based on their similarity. There is some algorithm that can be used to clustering data. One of them is Ant Colony Optimization (ACO). ACO is a metaheuristic algorithm that mimics how ants bring food to the colony. ACO also can be used to solve the clustering problem. In this paper, we will use the ACO version for clustering to cluster news and find the performance of the said method. Then we compare the result with ground truth. © Published under licence by IOP Publishing Ltd.

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GIS-Based MCDM for Central Business Suitability in a Small City 5

GIS-Based MCDM for Central Business Suitability in a Small C...

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5th International Conference on Informatics and Computing, ICIC 2020

作者： Herlawati, Herlawati Abdurachman, Edi Heryadi, Yaya Soeparno, Haryono Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728180472

Extended urbanization phenomenon in smaller-sized cities in Java should be considered by the government. However, the local governments can only use the plans from the central government. The negative effects of this, among others, are improper land use allocation, lack of facilities, and crimes that are difficult to handle. This study proposes a geographic information system (GIS)-based method to analyze the proper central business district in Karawang, a small district in Java, Indonesia. Multi-criteria analysis of factors affecting the candidate locations was used through a weighted sum method in a model. The spatial data were retrieved and analyzed using a GIS tool to classify region into urban, peri-urban, and rural. Some central business locations have been found after reclassification which is located near the toll gates. Two locations in the north of Karawang were classified have the potential to become the new central business locations. © 2020 IEEE.

关键词： Geographic information systems

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