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RHC: A Dataset for In-Room and Out-Room Human Counting

Procedia computer science 2021年 179卷 33-39页

作者： Bens Pardamean Hery Harjono Muljo Faizal Abid Herman Albert Susanto Tjeng Wawan Cenggoro Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In recent years, research in human counting from CCTV (Closed Circuit Television) images have found an increasing demand to be deployed in real-world applications. The applications have been implemented in various settings, both indoor and outdoor. In the case of indoor setting, we found a type of room setting that conveys a problem to human counting model if we need to count only humans inside a room. With this respect, we present RHC (Room Human Counting) dataset, which images are captured in the aforementioned setting. The dataset can be used to develop a robust model that can differentiate between humans inside and outside a room. The dataset is publicly available at https://***/datasets/vt5c8h6kmh/1.

关键词： dataset human counting crowd counting in-room out-room

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Customer churn prediction in telecommunication company

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AIP Conference Proceedings 2023年第1期2510卷

作者： Dota Biomantoro Gede Putra Kusuma Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Churn prediction methods are widely used to anticipate customer churn from services provided by a company for some reasons. This study aims to develop an optimal churn prediction model based on customer data from a telecommunication company in Indonesia. The model development and evaluation processes are performed by following the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consist of business understanding, data understanding, data preparation, modelling, and evaluation. Various combination of data preparation and modelling methods have been evaluated. The evaluation results show that the combination of feature selection and prediction model yields better results compared to prediction model without feature selection. The highest accuracy is achieved by Random Forrest at 97.82%, which is followed by Decision Tree at 97.06%, and Naive Bayes at 90.62%. This result indicates that a prediction model can be reliably used to predict customer churn in a telecommunication company.

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OP0379-HPR Tracking Step Counts in Adolescents with Juvenile Idiopathic Arthritis and Familial Mediterranean Fever: A 3-Month m-Health Monitoring Study Using Smartwatches

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Annals of the Rheumatic Diseases 2025年 84卷 305-306页

作者： A. Albayrak N. Arman A. Yekdaneh I. Donmez Y. Acikgoz G. Filiz T. Cakar N. Aktay Ayaz Istanbul Kent University Physiotherapy and Rehabilitation Istanbul Türkiye Istanbul University-Cerrahpasa Institute of Graduate Studies Physiotherapy and Rehabilitation Doctorate Program Istanbul Türkiye Istanbul University-Cerrahpasa Faculty of Health Sciences Department of Physiotherapy and Rehabilitation Istanbul Türkiye Fenerbahçe University Vocational School of Health Services Physiotherapy English Program Istanbul Türkiye Istanbul University-Cerrahpasa Institute of Graduate Studies Physiotherapy and Rehabilitation Master of Science Program Istanbul Türkiye EF University Institute of Graduate Studies Computer Engineering Doctorate Program Istanbul Türkiye MEF University Faculty of Engineering Department of Computer Engineering Istanbul Türkiye Istanbul University Istanbul Faculty of Medicine Department of Pediatric Rheumatology Istanbul Türkiye

Background: Physical inactivity is a significant public health issue among both healthy adolescents and those with chronic rheumatic diseases. Despite the benefits of physical activity, most adolescents do not meet recommended activity levels [1, 2]. In this context, wearable activity trackers have emerged as a solution to reduce inactivity. These devices can encourage autonomous motivation by allowing self-monitoring of physical activity behaviors, ultimately increasing physical activity levels [3]. Objectives: The aim of the study was to monitor physical activity in adolescents with Juvenile Idiopathic Arthritis (JIA) and Familial Mediterranean Fever (FMF) over a period of 3 months using a smartwatch, to evaluate step counts and to compare these with their healthy controls. Methods: 47 adolescents aged 12-18, diagnosed with JIA and FMF, and 274 healthy adolescents, were included in the study. Step counts of all adolescents were monitored for 3 months using a wearable activity device and the Pedi@ctivity Mobile Application, which operates on the Android system was developed by our team. The Pedi@ctivity Analysis Mobile Application comprises two modules as User Module and Supervise Module. Pedi@ctivity User Module where the user's health data is obtained through a smartwatch, and Pedi@ctivity Supervisor Module, where the evaluation data of the user is recorded and the smartwatch data can be viewed and managed by the supervisor. The data collected from smartwatch devices is accessed through the Google Fit API, which enables the retrieval of data from wearable technologies or smartphones including historical data. Thus, the data obtained from adolescents' smartwatch usage is transferred to the Pedi@ctivity Mobile Application for further analysis and management. Statistical analyses were performed using Python (version 3.9) with libraries including NumPy, SciPy, and pandas for data manipulation, and statsmodels or scikit-learn for advanced statistical and regression an

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Database management system design for automatic weather information with twitter data collection 6

Database management system design for automatic weather info...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Purwandari, Kartika Perbangsa, Anzaludin Samsinga Sigalingging, Join W. C. Krisna, Adhi Asta Anggrayani, Suci Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Meteorological Climatological and Geophysical Agency Database Center Division of Bmkg Jakarta10720 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Improvement of data management about weather in Indonesia is very supportive of the need for weather information. Currently, the weather information that is disseminated to the public is only based on sensor location points that are spread across Indonesia. However, supporting data generated from these sensors is only limited to certain areas. We hence develop a database system that can help collect weather information automatically via Twitter. This facilitates the Indonesian people in knowing weather information based on the completeness of data from sensor data and Twitter. The steps from the database that are built will be very helpful in collecting data about the weather throughout Indonesia. The basic step that we use in our database are conceptual, logical and physical design. The information obtained in this study is very important for efficient and effective data collection to achieve real-time automation of weather information. © 2021 IEEE.

关键词： Database systems

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Tweets Sentiment Analysis during COVID-19 Pandemic

Tweets Sentiment Analysis during COVID-19 Pandemic

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Alanezi, Maha A. Hewahi, Nabil M. University of Bahrain Big Data Science Analytics Master Program Collage of Science Sakhir Bahrain University of Bahrain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the k-means clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3. © 2020 IEEE.

关键词： COVID-19

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Prediction of Stock Price and Direction Using Neural Networks: Datasets Hybrid Modeling Approach

Prediction of Stock Price and Direction Using Neural Network...

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2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Aradi, Mohamed Al Hewahi, Nabil Univestity of Bahain Sakhir Big Data Science Analytics Master Program Collage of Science Bahrain Univestity of Bahain Collage of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781728196756

Stock prices prediction is one of the most daunting tasks to achieve for day traders, investors, and data scientists. They are complex functions of a wide array of contributing factors that affects the movement dynamics. Politics, social outlook, the company's sales performance, and socio-economic factors all have an influence of how the stock is perceived globally and thus affecting the price movement based on the supply/demand balance. In this work a Framework is proposed to predict the price and movement direction by using multiple datasets that includes news sentiment, social sentiment, company earnings announcement, and technical indicators. A case study was applied on the Apple Inc. stock using Long short-term Memory Neural Networks (LSTM) and Deep Neural Networks (DNN). Results showed superiority of the LSTM model in both predicting stock value [75.4 MSE, +-2.52 PE] and the direction with [70.1% classification accuracy] over the DNN model which suffered from prediction lag and dependency on trend indicators achieving [53.1%] in predicting the direction of the stock. © 2020 IEEE.

关键词： Fruits

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Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

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Procedia computer science 2021年 179卷 47-54页

作者： Kartika Purwandari Join W.C. Sigalingging Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Now in the era of big data, many are applying information methods accurately especially by social media. The aims of this study to classify the weather based on Twitter automatically using text mining by using Support Vector Machine (SVM), MultinomialNaive Bayes (MNB), and Logistic Regression (LR) method. The experimental results show that SVM substantially outperforms various other machine learning algorithms for the task of text classification with an accuracy value of 93%. This result proves that SVM is very suitable for text categorization. We use clustering technique to read the pattern in customers’ opinion about the restaurant based on some measurement variables.

关键词： weather classification twitter machine learning support vector machine multinomial naive bayes logistic regression

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Genome data mining approach to identify potential protein in crop plants

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AIP Conference Proceedings 2024年第1期3026卷

作者： Joko Pebrianto Trinugroho Faisal Asadi Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

There is a need to produce more crop plants to meet the future global demand. However, the climate change has affected the global crop yield. Consequently, finding an alternative approach to improve crop yield becomes essential. The development of sequencing techniques, as well as information technologies, have enabled us to perform genome data mining. Using genome data mining approach, it is possible to identify or discover a protein which has a particular characteristic. This study aims to identify a protein, which could potentially improve crop yield, using genome data mining approach. D1 protein was used as the target, as this protein is highly involved in photosynthesis. Then, protein sequences of various crop plants were collected from biological database. After conducting data trimming and filtering, sequence analysis was performed. The analysis was used to construct phylogenetic tree and create a 3D protein model. Sequence analysis displayed variation in amino acid sequence in D1 protein. Protein modelling located the variations, which scattered within D1 protein. Furthermore, we highlighted the amino acid residues that are the targets for genetic engineering. The research findings may provide a reference to improve crop production through genome mining approach.

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Industrial revolution 4.0 on big data adoption factors on social media users and popularity brands 2

Industrial revolution 4.0 on big data adoption factors on so...

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2nd International Conference on Lesson Study of science Technology Engineering and Mathematics 2020, ICOLSSTEM 2020

作者： Sardjono, W. Rama Putra, G. Selviyanti, E. Cholidin, A. Salim, G. Information Systems Management Department Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia Health Department Politeknik Negeri Jember Jember Jawa Timur68124 Indonesia Law Studi Program Faculty of Law University of Muhammadiyah Jakarta Indonesia Post Graduate Program Faculty of Economic Science University of Trisakti Jakarta Indonesia

The purpose of writing this paper is to look at how factors of big data adoption affect the users of social media and brand popularity. The method used in the writing of this paper is by using path analysis and also by doing literature study to find reference material. The results of the analysis indicate that there is influence of big data adoption factors that affect the popularity of the brand. With this research, we get big data model that influence the brand popularity. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Big data

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Inappropriate Benefits and Identification of ChatGPT Misuse in programming Tests: A Controlled Experiment

arXiv

引用

arXiv 2023年

作者： Toba, Hapnes Karnalim, Oscar Johan, Meliana Christianti Tada, Terutoshi Djajalaksana, Yenni Merlin Vivaldy, Tristan 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 Media Culture Department Faculty of Information Sciences and Arts Toyo University Kawagoe Japan EC-Council TampaFL United States

While ChatGPT may help students to learn to program, it can be misused to do plagiarism, a breach of academic integrity. Students can ask ChatGPT to complete a programming task, generating a solution from other people’s work without proper acknowledgment of the source(s). To help address this new kind of plagiarism, we performed a controlled experiment measuring the inappropriate benefits of using ChatGPT in terms of completion time and programming performance. We also reported how to manually identify programs aided with ChatGPT (via student behavior while using ChatGPT) and student perspective of ChatGPT (via a survey). Seventeen students participated in the experiment. They were asked to complete two programming tests. They were divided into two groups per the test: one group should complete the test without help while the other group should complete it with ChatGPT. Our study shows that students with ChatGPT complete programming tests two times faster than those without ChatGPT, though their programming performance is comparable. The generated code is highly efficient and uses complex data structures like lists and dictionaries. Based on the survey results, ChatGPT is recommended to be used as an assistant to complete programming tasks and other general assignments. ChatGPT will be beneficial as a reference as other search engines do. Logical and critical thinking are needed to validate the result presented by ChatGPT. © 2023, CC BY.

关键词： Students

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