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Customer churn prediction in telecommunication company

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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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Identification of Meteorological Parameters Affecting Dengue Hemorrhagic Fever Incidences

Identification of Meteorological Parameters Affecting Dengue...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Faisal Asadi Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Tropical disease is one of the infectious diseases that affect Indonesia. Many people die because of tropical diseases, such as dengue hemorrhagic fever (DHF), chikungunya, leprosy, lymphatic, and filariasis. The Indonesian government's lack of public policy is the main problem in treating and preventing tropical diseases. The paper aims to describe the analysis results of the incidence of DHF in Jakarta, Indonesia. The analysis used the DHF incidences data from the Jakarta Health service surveillance office representative and combined it with the meteorological parameters data. The meteorological parameters were collected from the Bureau of Meteorological Climatology and Geophysics (BMKG). The statistical analysis of this research used Pearson's correlation. The results show that the meteorological parameters affecting DHF incidences are temperature, humidity, and wind speed.

关键词： Correlation Statistical analysis Wind speed Surveillance Multimedia systems Sociology Humidity

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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A Design of Childhood Stunting Assessment Feature with Agile UX Approach

A Design of Childhood Stunting Assessment Feature with Agile...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Reza Rahutomo Anzaludin Samsinga Perbangsa Faisal Asadi Rudi Nirwantono Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665476652

multidisciplinary collaboration between public health, system engineering, and UX is able to generate a solution in healthcare problem like stunting. The principle of Agile UX gathers requirements to generate an application design. Currently, various website and mobile applications are available to be utilized for stunting research. StuntingDB is one of many web-based applications that specialized in stunting data management. The application required several types of users to operate hierarchically. This research focused on Public Health Officers’ workflow that require a solution to in the form of assessment module for stunting DBMS by using Agile UX principle. The method collaborates agile team and UX team to solve Public Health Officer's difficulty in changing stunting status at pre-saved child patient's profile. Collaboration of both agile team and UX team successfully proposed an application design that system design.

关键词： Human computer interaction Pediatrics Collaboration Companies Mobile applications Public healthcare System analysis and design

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The Technology Behind Genomic Database

The Technology Behind Genomic Database

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joko Pebrianto Trinugroho Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser Database and the Rat Genome Database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser Database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them.

关键词： Proteins Databases Genomics DNA Rats Biology Organisms

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Ensemble-Stacking Machine Learning Model for Multi-Class Skin Diseases Identification

Ensemble-Stacking Machine Learning Model for Multi-Class Ski...

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International Conference on Biomedical Engineering (IBIOMED)

作者： Dennis Tandelon David Lawrence Christiono Nur Afny Catur Andryani Srie P. Gondokaryono July I. Rahardja Retno Danarti Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Doctor of Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Indonesia Dermatovenereology Department Faculty of Medicine Indonesian Society of Dermatology & Venereology Padjadjaran University Indonesia Dermatovenereology Department Faculty of Medicine Indonesian Society of Dermatology & Venereology Gadjah Mada University

ISBN: (数字)9798350389265

ISBN: (纸本)9798350389272

Algorithm development on skin diseases identification with high cardinality classification is still very challenging. In addition, the high similarity appearance among diseases makes the computer vision approach meet high difficulties on providing clear distinct feature representation. In general, some literature shows that the performance of single machine learning or single deep learning techniques can be further improved when merged by ensemble mechanism. This approach has been applied on binary skin's cancer diseases diagnosis prediction and successfully improve the prediction ability. This paper elaborates the potential of ensemble machine learning approaches using stacking techniques on conducting feature extraction and classification model for identifying more general skin diseases based on dermoscopy images and mobile phone images. The proposed model combines three different CNN architectures, ResNet50, Xception, and VGG16 to extract features from the image then stacked as input for the model trained using five different algorithms, which are logistic regression, support vector machines, multi-layer perceptron, k-nearest neighbors, random forests and the combination of these algorithm in stacking classifier as the base estimators and final estimator. The simulation results present the proposed ensemble - stacking model succeeds to improve the identification ability in all utilized datasets. In addition, the proposed ensemble - stacking models show better recall which indicates good sensitivity on handling false negative. It is beneficial for medical application.

关键词： Support vector machines Sensitivity Biological system modeling Simulation Stacking Feature extraction Skin Classification algorithms Diseases Skin cancer

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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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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