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The Effect of Management Accounting Information Systems and Decision Making on Managerial Performance 5

The Effect of Management Accounting Information Systems and ...

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5th International Conference on Software and e-Business, ICSEB 2021

作者： Meiryani, Meiryani Gilberta, Gisela Arshanty, Risky Lindawati, A.S.L. Liawatimena, Suryadiputra Department of Accounting Faculty of Economics and Communication Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781450385831

This purpose of this study is find empirically The effect of management accounting information systems and decision making on managerial performance. The research method used is quantitative research methods with primary data obtained from questionnaire data which is measured using a likert scale. The result found that management accounting information system and decision making influence managerial performance. The results showed that the smaller the management accounting information system and decision making owned by the company, the smaller the percentage of managerial performance carried out in the company. The higher the management accounting information system, the higher the company's managerial performance. © 2021 ACM.

关键词： Decision making

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Analysis of the Effect of Internet of Things Implementation on the Taxation Area in Indonesia 5

Analysis of the Effect of Internet of Things Implementation ...

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5th International Conference on Advancement in Data science, E-Learning and Information Systems, ICADEIS 2023

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Cholidin, Achmad Bina Nusantara University BINUS Graduate Program Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia State University of Jakarta Post Graduate Program of Management Science Jakarta13220 Indonesia University of Muhammadiyah Jakarta Law Study Program Faculty of Law Indonesia

ISBN: (纸本)9798350303414

Technology in the tax field is evolving by the minute. Digital technology has many implications for development in various fields. One of them is the field of economics. One of the latest technologies is the Internet of Things, which has attracted the attention of various organizations and governments, more precisely the tax authorities. This research tries to illustrate the impact of the Internet of Things on taxation. The researcher used qualitative research with literature survey methods. Data were collected from various journals, media publications, articles, and previous research papers. The study's findings indicated that the Internet of Things (IoT) is crucial to the development of control technologies. This supports the Director General of Taxes' (DGT) goal of using big data analytics to optimize the potential of digital taxes through his two mechanisms: predictive analytics and tax ranking. © 2023 IEEE.

关键词： Taxation

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging

A Systematic Literature Review of Deep Learning Application ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Philips Bens Pardamean Gregorius Natanael Elwirehardja Mahmud Isnan Computer Science Department BINUS Graduate Program - Master of Compuer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance.

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Systematic Literature Review: An Intelligent Pulmonary TB Detection from Chest X-Rays 1

Systematic Literature Review: An Intelligent Pulmonary TB De...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Jimmy Cenggoro, Tjeng Wawan Pardamean, Bens Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Tuberculosis (TB) is one of the top ten reasons for death from an infectious agent. Although TB is curable and preventable, delay in diagnosis and treatment can lead the patient to death. Advancements in computer-Aided diagnosis (CAD), particularly in medical images classification, significantly contribute to early TB detection. The current state-of-Art CAD for medical images classification applications using a method base on deep learning techniques. The problem faced in this deep learning technique is that, in general, it only uses a single modal for the model. In contrast, in medical practice, the data used for TB analysis not only focuses on images but also includes clinical data such as demographics, patient assessments, and lab test results. This systematic literature review describes different deep learning methods using single modal or multimodal techniques that combined images with other clinical data. We conducted a systematic search on Springer, PubMed, ResearchGate, and google scholar for original research leveraging deep learning for Pulmonary TB detection. © 2021 IEEE.

关键词： computer aided diagnosis

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Fake News Classifier with Deep Learning

Fake News Classifier with Deep Learning

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Industrial Electrical and Electronics (ICIEE), International Conference on

作者： Michael Febrianto Lu Renaldy Vincent Ciptadi Reyhan Nathanael Kevin Sam Andaria Abba Suganda Girsang Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665486231

In this decade, social media has become more frequently used because it is easy to see some news on the social media for example Instagram, Facebook, and other social media. However, the problem that often arises in social media problems is the accuracy of the news. There are many fake news circulating on social media. This paper aims to classify the fake news using random forest. This research uses data Kaggle “Fake News Dataset Combined Different Sources”. The accuracy of the Fake News Classifier using this model is is 0.9403, the Classification Report on this model is True Recall Score: 0.88 and the Fake Recall score is 0.98 while the Precision Score is 0.96 and 0.93 for each true and fake result, F1-Score is 0.92 and 0.95. At the end the model using self-inputted data is tested

关键词： Deep learning Social networking (online) Multimedia Web sites Forestry Real-time systems Data models Fake news

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems

Implementation of Face Patterns and Smile Recognition for In...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Miftakhurrokhmat Teddy Suparyanto Gregorius Natanael Elwirehardja Shofiyati Nur Karimah Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Informatics Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

Students “attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students” attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification.

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Classification Prediction of Heart Disease Using Machine Learning Techniques

Classification Prediction of Heart Disease Using Machine Lea...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Kevis Setiawan Jonathan Putu Satria Beratha Maria Susan Anggereainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School Of Computer Science Bina Nusantara University Jakarta Indonesia

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector Machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively.

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Fuzzy Decision Support Model on Virtual Plant Computational Model of Water Spinach (Ipomoea aquatica. Forssk) in Farming Business

Fuzzy Decision Support Model on Virtual Plant Computational ...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ditdit Nugeraha Utama Ika Diah Agustia Rachmawati Heraldo Yusron Purwantono Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Using soil as a planting medium (conventional system) raises several problems, such as the need for large agricultural land, but the available land is limited. This problem is triggered by an increase in demand for nutrients needed by the body to support daily activities, one of which can be met by consuming green vegetables, such as Water Spinach. To solve this problem, a hydroponic wick system or a wick system can use that is simple and easy to care for. Water Spinach (Ipomoea aquatica. Forssk) vegetables are easy to grow and care for. Nutrients contained in 100 grams of Water Spinach, energy: 29 kcal, protein: 3 grams, Fat: 0.3 grams, carbohydrates: 5.4 grams, calcium: 73 mg, phosphorus: 50 mg, iron: 3 mg, vitamin A: 6300IU,vitamin B1: 0.07 mg, vitamin C: 32 mg. In addition, a combination of disciplines such as mathematics, computer science, plant science, biology, and statistics can be used. This combination of knowledge is known as Plant Computational Modeling (PCM), which uses the Growth Grammar Interactive Modeling Platform (GroIMP) with the Functional Structural Plant Modeling (FSPM) method. The virtual plant model was successfully built, the results were divided into two parts, such as single plant and multi-plant. Plant computational modeling and DSM can help deal with agricultural problems. By using a plant dataset, the GroIMP-FSPM platform, and fuzzy logic, the model could help researchers in making a better decision based on BEP evaluation. The minimum numbers of plants cultivated were 650 plants in one harvest period in order to gain a desirable profit. We recommend to cultivate even more plants in one harvest period to gain even higher profits.

关键词： Proteins Phase change materials Computational modeling Plants (biology) Soil Phosphorus Mathematical models

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Single Image Perspective Grid System on Floor Surface for 2D to 3D Transformation

Single Image Perspective Grid System on Floor Surface for 2D...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Darma Rusjdi Edi Abdurachman Yaya Heryadi Gede Putra Kusuma Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453905

Our study discusses the application of the perspective grid concept as the basis for the process of transforming the 2D coordinates of the image into 3D coordinates in real space for estimating the location of objects in a single image scene. In several other studies in the field of robotics, AR use methods and media in the form of a chessboard box as a reference for camera calibration and location estimation. However, it does not show accurate results for the size of objects in the room. We saw the potential areas of Alberti's perspective grid, which is often used as a reference in making sketches for the arts and various engineering fields so that they are visually precise with reality. Our study uses 6 reference points in a room arrangement with a floor grid pattern as a reference for the formation of a perspective grid. Based on the triangle comparison approach to determine the x-ordinate, the average error is 0.2193. And through 3 interpolation approaches to get the most accurate results with the Lagrange and Newton methods on the 6th order with the average difference between the actual and predicted points of 1.5 cm from the y-direction distance of the camera from 2 meters to 6.80 meters.

关键词： Meters Interpolation Three-dimensional displays Robot kinematics Robot vision systems Estimation Media

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Survey of Online Exam Proctoring Model to Detect Cheating Behavior based on Face Recognition

Survey of Online Exam Proctoring Model to Detect Cheating Be...

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International Conference on Systems and Informatics (ICSAI)

作者： Sucianna Ghadati Rabiha Iman Herwidiana Kartowisastro Reina Setiawan Widodo Budiharto Computer Science Department BINUS Graduate Program – Computer Science. Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science. Computer Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665474498

Exams are an important component of any educational program, including online education. In any test, there is a possibility of cheating, so its detection and prevention is important. This study aims to conduct an in-depth study of the online exam monitoring model approach based on facial recognition used to detect cheating. Based on the inclusion and exclusion criteria designed, 13 selected studies were obtained. From these studies, we conducted further analysis regarding the Face Detection Method, Face Recognition Method, Initial Feature, Behavior Analysis and Evaluation Metrics used in each study so as to provide answers to research questions. the most frequently used Face detection method was Viola-Jones with a presentation of 20%, then CNN and MTCNN with a total presentation of 21%. The most widely used face recognition method in selected studies is CNN and metrics Accuracy is one of the most frequently used evaluations with a percentage of 33%. While the features that are usually used to detect cheating during online exams include facial motion and head pose which occupies the first position. The second is eye movement, then multiple faces gaze estimation and facial expression is in third place. Other features that also play a role in analyzing cheating behavior are mouth detection, facial vector, landmark location, gesture and posture.

关键词： Measurement Analytical models Face recognition Mouth Estimation Feature extraction Behavioral sciences

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