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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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Code-Mixed Sentiment Analysis Using Machine Learning Approach - A Systematic Literature Review 4

Code-Mixed Sentiment Analysis Using Machine Learning Approac...

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4th International Conference on Informatics and Computational sciences, ICICoS 2020

作者： Tho, Cuk Warnars, Harco Leslie Hendric Spits Soewito, Benfano Gaol, Ford Lumban Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728195261

Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis. © 2020 IEEE.

关键词： Sentiment analysis

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Performance of news clustering using ant colony optimization

Performance of news clustering using ant colony optimization

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Jenson, T. Girsang, A.S. Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Humans are basically curious creatures. We always look for all the latest information through books or news. News usually contains tags or categories to make it easier to find similar news. It can be done manually by a human or with a machine to make it easier. Data mining techniques such as clustering and classification can be used to help the categorization process. Clustering is a technique that can be used to grouped news based on their similarity. There is some algorithm that can be used to clustering data. One of them is Ant Colony Optimization (ACO). ACO is a metaheuristic algorithm that mimics how ants bring food to the colony. ACO also can be used to solve the clustering problem. In this paper, we will use the ACO version for clustering to cluster news and find the performance of the said method. Then we compare the result with ground truth. © Published under licence by IOP Publishing Ltd.

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Lightweight End to end Pose-Robust face recognition system with Deep Residual Equivariant Mapping

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Procedia computer science 2021年 179卷 648-655页

作者： Kevin William Gunawan Noptovius Halimawan Suharjito Computer Science Department Binus Graduate Program - Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta 11480 Indonesia

In the face recognition field of study, pose-robustness and lightness of a model are few of the critical improvement factors of face recognition. However, these fields are still providing challenge for researchers. Even though pose variance is proven to drop the accuracy of deep learning-based models, pose-robustness is not studied often in lightweight face recognition models. Existing pose-robust models have heavier implementation costs compared to lightweight models. We propose a deep learning architecture that implements Deep Residual Equivariant Mapping (DREAM) to improve pose-robustness of a lightweight MobileFaceNets model as a solution to the underlying issue. In the proposed model, the DREAM block is stitched to the MobileFaceNets stem CNN architecture. The evaluation process compares the speed, file size, and accuracy on pose diverse datasets, such as the CFP and IJB-A dataset. The evaluation results of the proposed model show an accuracy improvement of 0.07% with verification speed difference of 0.17 ms. Both of the results show a better performance compared to the baseline naive model.

关键词： face recognition pose-robust lightweight deep learning

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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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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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Social Network Analysis Algorithms, Techniques and Methods

Social Network Analysis Algorithms, Techniques and Methods

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Sandra, Lidia Trisetyarso, Agung Ramadhan, Arief Abdurachnan, Edi Lumbangaol, Ford Isa, Sani Muhamad Krida Wacana Christian University Psychology Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Trisakti Institute of Trasportation and Logistic Trisakti University Jakarta Indonesia

ISBN: (纸本)9781665499699

Understanding how social structures with the use of a network have been an active field of study for academics in the past five years alone. The need to properly comprehends how Social Network Analysis (SNA) is being studied grows more and more in recent years. In this article, we propose a Systematic Literature Review (SLR) to the SNA to see how the algorithms, techniques, and methods are used also discuss their findings. We select thirty-one research studies on SNA. We found different algorithms and techniques that are being used. It is found that the selected research could be categorized into five different main topics which is academic, health, social media, communication, and technology. From all of the research paper discussed, it is also found that many algorithms and techniques are being used to enhanced the SNA, most of them are being machine learning algorithm such as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, Logistic Regression, K Nearest Neighbor, and Whale Optimization Algorithm. While the common features of the datasets used in the research comes as different arrays of user information from social media platforms, Tweets and posts from multiple platforms, also a photographic input such as self-images, portraits, and context related pictures. This article will serve as a single reference for future researchers to the discovery of the latest SNA findings. © 2021 IEEE.

关键词： Decision trees

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Implementation of naive bayes classifier algorithm in classification of civil servants

Implementation of naive bayes classifier algorithm in classi...

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International Conference on science and Technology Applications 2019, ICoSTA 2019

作者： Achmad, Rifki Girsang, Abba Suganda Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

As the executor of the transfer of civil servants, BKD (Badan Kepegawaian Daerah) is required for professionals in carrying out their duties in mutation positions for civil servants. This study implements a mutation system for civil servants by using Naive Bayes Classifier Algorithm. This study is implemented in Secretariat District Regency of Pelalawan, Riau Indonesia, This system uses some attributes such as age, total work period, education background and implemented on a web based application. This approach is an approximation to Bayes theorem which is the principle of statistical opportunity to combine previous knowledge with new knowledge. This system is very effective classification algorithm (get the right results) and efficient which can be a basic reason in mutation civil servants. © Published under licence by IOP Publishing Ltd.

关键词： Classifiers

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Hotel content-based recommendation system

Hotel content-based recommendation system

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International Conference on science and Technology Applications 2019, ICoSTA 2019

作者： Wahyudi, Kristian Latupapua, Johanes Chandra, Ritchie Girsang, Abba Suganda Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Content based recommendation system tries to recommend items similar to those a given user has likely in the past, whereas systems designed according to the collaborative recommendation paradigm identify users whose preferences are similar to those of the given user and recommend items they have liked. The proposed recommendation system is discussing about Data finitihotel recommendations for a place across U.S. The selected process for the recommendation is calculating the rating of hotel categories on the city. By calculating hotel categories from several city by combining two features namely categories and taken from the city of the selected hotelcategory as well. The result of process will be recommended hotel with highest ratingto the user. © Published under licence by IOP Publishing Ltd.

关键词： Hotels

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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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