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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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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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Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

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Procedia computer science 2021年 179卷 344-351页

作者： Bharuno Mahesworo Arif Budiarto Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

关键词： Polygenic Risk Score Cross Population Colorectal Cancer

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Improvement of Jakarta's air quality during large scale social restriction 2

Improvement of Jakarta's air quality during large scale soci...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Rahutomo, R. Purwandari, K. Sigalingging, J.W.C. Pardamean, B. Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather Data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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Convolutional Neural Networks for Scops Owl Sound Classification

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Procedia computer science 2021年 179卷 81-87页

作者： Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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A Convolutional Neural Network-based Ancient Sundanese Character Classifier with Data Augmentation

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Procedia computer science 2021年 179卷 195-201页

作者： Alam Ahmad Hidayat Kartika Purwandari Tjeng Wawan Cenggoro Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With an increasing interest in the digitization effort of ancient manuscripts, ancient character recognition becomes one of the most important areas in the automated document image analysis. In this regard, we propose a Convolutional Neural Network (CNN)-based classifier to recognize the ancient Sundanese characters obtained from a digital collection of Southeast Asian palm leaf manuscripts. In this work, we utilize two different preprocessing techniques for the dataset. The first technique involves the use of geometric transformations, noise background addition, and brightness adjustment to augment the imbalanced samples to be fed into the classifier. The second technique makes use of the Otsu’s threshold method to binarize the characters and only uses the usual geometric transformations for the data augmentation. The proposed network with different data augmentation processes is trained on the training set and tested on the testing set. Image binarization from the second technique can outperform the performance of the CNN-based classifier upon the first technique by achieving a testing accuracy of 97.74%.

关键词： ancient sundanese characters convolutional neural network data augmentation document image analysis Otsu’s threshold method

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Development of Buffer Zone into Living Lab - Garuda Smart City Framework: Systematic Review

Development of Buffer Zone into Living Lab - Garuda Smart Ci...

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International Conference on ICT For Smart Society (ICISS)

作者： Gunadi Emmanuel Suhono Harso Supangkat Ford Lumban Gaol Benny Ranti Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Fakultas Sains dan Teknologi Univ. Katolik Musi Charitas Palembang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Computer Science Department Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab - Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019–2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues.

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Smartphone Application for the Deaf and the Deaf Caring Community

Smartphone Application for the Deaf and the Deaf Caring Comm...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Pinnus Pryandi Muhammad Bayu Dewantara Harco Leslie Hendric Spits Warnars Arief Ramadhan Nurulhuda Noordin Fahriza Hanis Abdul Razak Information Systems department School of Information Systems. Bina Nusantara University Jakarta Indonesia Computer Science department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Telkom University Bandung Indonesia Faculty of Computer and Mathematical Sciences Universiti Teknologi Mara (UiTM) Selangor Malaysia

In life, various challenges and problems faced by deaf people such as communication skills and other problems, including emotional, mental, and societal development However, technology is needed that can help the process. This Smart Application try to build communication between the deaf to share thoughts in group chats, including getting information about the deaf, so that the communication learning process for the deaf becomes easier. The proposed model is using Unified Modeling Languages (UML) diagrams, such as case diagrams for the current proposed idea process and class diagrams for relational tables or database stores. In addition, the User interface (UI) is implemented in this research work, Personal Home Pages (PHP) as a web server programming and MySQL database as an open-source database.

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Oil Palm Fruits Dataset in Plantations for Harvest Estimation Using Digital Census and Smartphone

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Scientific data 2025年第1期12卷 972页

作者： Martinus Grady Naftali Gregory Hugo Muhammad Reza Azhar Priyadi Muhammad Asrol Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 10480 Indonesia. Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta 10480 Indonesia. Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia.

This article presents a dataset of oil palm Fresh Fruit Bunches (FFBs) images from commercial plantations in Central Kalimantan, Indonesia, focusing on five maturity stages: Unripe, Underripe, Ripe, Flower, and Abnormal. The data collection involved smartphone video recordings of unharvested trees from multiple angles under varying conditions. Video frames were extracted and expertly annotated using computer Vision Annotation Tool (CVAT), with annotations exported in Common Objects in Context (COCO) format suitable for object detection tasks. It has 10,207 images in its training set, 2,896 in the validation set, and 1,400 in the test set, which are supplemented using data augmentation to handle class imbalance and increase variation. These images have real-world complications arising from partial visibility, low contrast, occlusion, and blurriness. It forms the basis that will support the development of deep learning models for detection and classification of FFB, particularly for monitoring of harvest times, yield prediction, and optimization of resources in plantation operations.

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