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International Conference on Information Management and Technology (ICIMTech)

作者： Chandra Hermawan Heruatmadja Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science Universitas Indonesia Depok Indonesia

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level.

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Pyramid Scene Parsing Net Model for Automated Paddy Field Map using SPOT 6 Satellite Images 5

Pyramid Scene Parsing Net Model for Automated Paddy Field Ma...

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5th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Heryadi, Yaya Irwansyah, Edy Miranda, Eka Soeparno, Haryono Herlawati Hashimoto, Kiyota Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Faculty of Technology and Environment Phuket83120 Thailand

ISBN: (纸本)9798350398212

Food sustainability is still one of the main priorities for many countries as it contributes to the economy and stability of the nation. For government in many countries whose peoples consumes rice as its staple food, food self-sufficiency initiatives highly depend on accurate prediction of paddy field map. Mapping paddy field task is a challenging problem which cannot be handled manually especially when the paddy fields are spread out in very wide geographical areas such as those in Indonesia. Fortunately, wide availability of satellite imagery and the advent of deep learning technology in the past ten years have made it possible to improve efficiency of most parts of those manual works involving image semantic segmentation tasks. However, satellite image-based semantic segmentation is a challenging task. High object complexity, cloud partial occlusion, larger image size than a computer memory can stored can hinder accuracy of the image segmentation results. This paper presents a method for paddy field map generating using semantic image segmentation approach in which Pyramid Scene Parsing Net model is used for segmenting satellite imagery. The generated paddy map can be used as a basis for decision-making, especially in the agricultural sector. Analysis of local land use/land cover dynamics. The results of his experiments using SPOT 6 satellite imagery from the Pahung region of Central Kalimantan achieved average training accuracy, best training accuracy and test accuracy of 0.85, 0.86 and 0.89 respectively. These results indicated that the semantic segmentation model is suitable for addressing the same task in different crops. © 2022 IEEE.

关键词： Image enhancement

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases Data mining Information technology

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A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

A Platform for Monitoring Student Commuting in the Use of Sc...

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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Da Costa Rocha, Jesse De Souza, Marcela Alves Cardoso, Evelin Helena Silva Vijaykumar, Nandamudi De Araujo, Jasmine Priscyla Leite Frances, Renato Lisboa Graduate Program in Electrical Engineering Belém Para Brazil Information and Communication Technology Center Marabá Para Brazil Department of Computer Science Capitão Poço Para Brazil Coordination of Applied Research and Technological Development São Paulo Brazil

ISBN: (纸本)9798350302523

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics. © 2023 IEEE.

关键词： Students

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Location Prediction Using Named Entity Recognition for Indonesia Natural Disasters in Data Twitter

SSRN

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SSRN 2022年

作者： Girsang, Abba Suganda Noveta, Bima Krisna Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Indonesia is in an area prone to natural disasters, there were 4,650 natural disasters that occurred in 2020. However, the community does not have spatial data regarding the location of the disasters that occurred. The purpose of this study is to provide the location of natural disasters that are poured into maps to the public by utilizing Twitter data which has a fast distribution with the fifth most users in the world. This study proposes a system for geolocation of types of natural disasters obtained from Twitter, which are classified into eight classes of natural disasters using the Support Vector Machine (SVM) method. Then the Twitter text is extracted to find the location in the Twitter text using Named Entity Recognition (NER). NER uses six special classes based on the regional level in Indonesia with an accuracy rate of 85.65%. The result of geolocation using ArcGIS maps is 87,5%. It is hoped that with this research, people will get spatial data to see the points of natural disasters that occur using maps. So, the public can anticipate future disasters, and the authorities can send aid to the location. © 2022, The Authors. All rights reserved.

关键词： Location

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Indonesian Automatic Speech Recognition with Xlsr-53

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SSRN 2022年

作者： Arisaputra, Panji Zahra, Amalia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using XLSR-53 pre-trained model. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve competitive WER. The total amount of data used in this study is 26 hours: (1) TITML-IDN 14,5 hours;(2) Magic Data 3,5 hours;and (3) Common Voice 8 hours. With a WER of 22 %, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 10% using a language model, resulting in a WER of 12 %. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data. © 2022, The Authors. All rights reserved.

关键词： Statistical tests

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Smart village design using enterprise architecture framework model 6

Smart village design using enterprise architecture framework...

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

作者： Faisal Suharjito Bina Nusantara University BINUS Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

This paper describes the design of smart village that can be describe as an indicator for the smart village in Indonesia, where the Indonesian population is now more than 250 million peoples. with several islands as much as 17, 504 islands, consisting of 53.3% were in urban areas, the rest spread in rural areas, but it will turn around if there is no solution currently to address the disparity in the village in terms of infrastructure and technology. One of the concepts can be applied to overcome these problems is "Smart Village". Conducting a study of four villages on the island of Java in Indonesia, which was awarded the village of Information Technology, the study aims to research the indicators that make the village as the village of Information Technology. By using the TOGAF Enterprise Architecture analysis, the result of study are the components that exist in the ICT village winners and what the other components needs to complement the design of smart village which can be a reference in Indonesia in future. © 2021 IEEE.

关键词： Rural areas

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Comparison of Gaussian Hidden Markov Model and Convolutional Neural Network in Sign Language Recognition System 1

Comparison of Gaussian Hidden Markov Model and Convolutional...

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

作者： Gunawan, Herman Suharjito Pratama, Devriady Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Sign language Recognition is the study to help bridging communication of deaf-mute people. Sign Language Recognition uses techniques to convert gestures of sign language into words or alphabet. In Indonesia, there are two types of sign languages which are used, Bahasa Isyarat Indonesia (BISINDO) and Sistem Isyarat Bahasa Indonesia (SIBI). The purpose of this research is comparing sign language recognition methods between Gaussian Hidden Markov Model and Convolutional Neural Network using indonesian sign language SIBI as a dataset. The dataset comes from 200 videos from 2 signers. Each signer performs 10 signs with 10 repetitions. To improve the recognition accuracy, modified histogram equalization is used as an image enhancement. Skin detection was used to track the movement of the gesture as input features in the Gaussian Hidden Markov Model and fine tuning was used in Convolutional Neural Network using transfer learning, freeze layer, and dropout. The results of the research are the Gaussian Hidden Markov Model provides accuracy value of 84.6% and Convolutional Neural Network provides accuracy value of 82%. © 2021 IEEE.

关键词： Hidden Markov models

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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An Overview of the Design and Development for Dynamic and Physical Bar Charts

An Overview of the Design and Development for Dynamic and Ph...

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International Conference on Information Visualisation (IV)

作者： Thiago Augusto Soares de Sousa Walbert Cunha Monteiro Tiago Davi Oliveira de Araújo Carlos Gustavo Resque dos Santos Bianchi Serique Meiguins Computer Science Graduate Program Federal University of Pará Belém Brazil

ISBN: (纸本)9781665490085

This paper aims to present the design process of dynamic data physicalization for bar charts and their variants, simple bars, stacked bars, and clustered bars. The physical artifact has 12 bars that are automatically configured according to the dataset by electromechanical components controlled by an Arduino board. The models of the printed 3D parts, the electromechanical components used and their connection scheme, the design aspects to set up the dynamic physical visualization according to the data, how the users choose the dataset and the kind of physical chart, usage scenarios with different types of charts and physicalization parameters are presented in detail. Finally, some strengths and difficulties in creating dynamic physical visualizations are highlighted and future works are proposed.

关键词： Solid modeling Three-dimensional displays Data visualization Data models Bars

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