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6th International Conference on Information Technology, computer and Electrical Engineering, ICITACEE 2019

作者： Kusuma, Gede Putra Harjono, Kristopher David Dwi Putra, Muhammad Taufik Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728126890

Weighted Longest Increasing Subsequence (WLIS) and its improvement, Best Increasing Subsequence (BIS) are two methods that has been proposed for pair verification in object instance recognition using local features. Tested on the Stanford Mobile Visual Dataset (SMVS), the BIS achieves better performance than WLIS on most categories, except for the 'video frames' category. In this paper we propose several modifications to BIS which resulted in a better overall performance compared to the WLIS and the basic BIS approaches. On average, the proposed Best Score Increasing Subsequence (BSIS) performs 4.53% better than the BIS and 9.43% better than the WLIS. © 2019 IEEE.

关键词： Object recognition

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Prediction Diabetes Mellitus Using Decision Tree Models

Prediction Diabetes Mellitus Using Decision Tree Models

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2019 International Congress on Applied Information Technology, AIT 2019

作者： Sunge, Aswan Supriyadi Warnar, Harco Leslie Hendric S. Heryadi, Yaya Abdurachman, Edi Soewito, Benfano Gaol, Ford Lumban BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728158624

Health is an essential thing in human life, but Indonesian people are still far from the word healthy lifestyle. One disease that can be caused by an unhealthy lifestyle is diabetes mellitus that can cause many deaths. So far, there are many data in the hospital, but the data can not be maximized well even if it can be used to predict diabetes. The need for accuracy to produce better results. In conducting this test, the results obtained using C 4.5 only algorithm is 72.08%. © 2019 IEEE.

关键词： Data mining

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Scoring Urban Sustainability as a Guidance and Self-Learning for City Planners

Scoring Urban Sustainability as a Guidance and Self-Learning...

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2019 International Congress on Applied Information Technology, AIT 2019

作者： Herlawati, H. Heryadi, Yaya Trisetyarso, Agung Suparta, Wayan Kang, Chul Ho Abbas, Bahtiar Saleh BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728158624

City planners worldwide have tried to develop their cities following the concept of sustainable development. To allocate land use properly, the zoning method has been widely used, especially in densely populated cities, namely a kind of sustainable urban form with considers the mix of use/diversity of land use to minimize travel distance. This study proposes a method for calculating how much a city meets the sustainable urban forms based on compact city criteria, i.e. compatibility, dependency, and compactness. After the survey filling in compatibility and dependency scores for each land use, and compactness was calculated for each sub-district. Fuzzy C-Means clustering was used to cluster the result to classify other areas. In addition, this method can be used by planners to check whether their plans meet the concept of sustainable development or not. © 2019 IEEE.

关键词： Land use

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Six Sigma Based Performance Measurement of Tax Return Processing Improvement (Case Study: Directorate General of Taxes for Republic of Indonesia) 6

Six Sigma Based Performance Measurement of Tax Return Proces...

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Joint Conference of the 6th Annual Conference of Industrial and System Engineering 2019, ACISE 2019 and 1st International Conference on Risk Management as an Interdisciplinary Approach 2019, ICRMIA 2019

作者： Yuliyono, A.D. Baehaki Luqman, D. Utama, D.N. Wibowo, A. Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Directorate General of Taxes for Republic of Indonesia has an internal unit named data processing centre (DPC) that has main duty to process paper tax return delivered from tax office. DPC practically implemented new tax return processing system to replace the old system, however the performance of the new system was not on a par with the old one, at the beginning. Then, DPC made several improvements to increase new system's performance. This study aims to measure the improvements made by DPC by using Six Sigma approach. The approach is functioned to assess data processing performance through DMAIC concept: i.e. define, measure, analyse, improve, and control. With the DMAIC concept, improvement exposes better result, the average value of six sigma significantly escalated by 0.97 to 2.56. © Published under licence by IOP Publishing Ltd.

关键词： Six sigma

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Count time series modelling of Twitter data topic modelling: A case of Indonesia flood events

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. A. Hidayat B Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Twitter data provide rich and powerful information to leverage the dynamics of public perception to establish situational awareness and disaster mitigation strategies during critical times. In this paper, we perform topic modeling via Latent Dirichlet Allocation to extract topics from a collection of tweets related to Indonesia flood events in February 2021 with the query: “banjir”. The extracted topics are used as one of the features to build a generalized linear count time series model with Negative Binomial distribution. We find seven major topics from the model in which tweets containing a topic about the government’s role in handling the situation dominate the conversation. Taking into account a simple intervention analysis, we demonstrate a statistically significant change in the users’ behavior before and after the severe Jakarta flood on 20 February 2021. Moreover, a metric evaluation demonstrates that a covariate that describes the turning point of the Jakarta flood event is convenient to build a more accurate count time series model of the tweets.

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Sport Field Reservation Based on Mobile Application 7

Sport Field Reservation Based on Mobile Application

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Claudinus, Tonto Prayoga Wicaksana, Made Kornelius Sitorus, Nicolas Ariqi Gustiandza, Marsa Oktavia, Tanty Lumban Gaol, Ford Hosoda, Takaaki Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665404228

This paper presents the sports apps based on a mobile application that has a function to help the provider or the owner of the sports field and sports vendor to promote their business in the apps. Due to the popularity and convenience that mobile applications had and high interest from many people in having a healthy life, it is a great opportunity to combine both of them. Therefore, this study focusses to build mobile sports application using object-oriented analysis and design method, that could meet the demand of lots of peoples that wants to do a sport especially a ball sport. Otherwise, the application can help and support the community and public, especially for ball sports fans, to make reservations for the sports field easier without having to come to the location and also meet their sporting needs. © 2020 IEEE.

关键词： Sports

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Multiple-Clustering Comparison in Spatial Data for Additional Accuracy Measurement in Land-use Classification 4

Multiple-Clustering Comparison in Spatial Data for Additiona...

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4th International Conference on Informatics and Computing, ICIC 2019

作者： Herlawati Heryadi, Yaya Hendric Spits Warnars, Harco Leslie Gaol, Ford Lumban Abdurachman, Edi Soewito, Benfano Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728122076

Satellite imageries have been widely used to analyze a region by planners. Data from the satellite usually have lower accuracy than other expensive methods e.g. drone, aerial view, etc. However, the data from satellite have wide range area and sufficient enough for modeling a land use. The accuracy assessment, therefore, becomes a vital task to ensure the model from the satellite imagery meets the minimum requirement. Validating the classification result by comparing to the real location or by other higher resolution images is needed. The paper proposed additional validation by comparing the classification result by another result in different date through the cross-tabulation method. Two satellite imageries in the same year were processed before classification to get the land-use and land-cover classification. Comparing two land-use classified images gave the accuracy statistics using cross-tabulation. The kappa statistic and accuracy showed the classification performance of 0.7592 that similar to the sampling-based accuracy assessment (0.75390). Therefore, the proposed method was appropriate as the accuracy assessment. © 2019 IEEE.

关键词： Satellite imagery

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Statistical analysis and data visualization of Indonesia and Malaysia SARS Cov-2 metadata

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AIP Conference Proceedings 2023年第1期2594卷

作者： D. Sudigyo A. Budiarto B. Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

SARS CoV-2 is a fascinating topic to investigate, especially in Indonesia and Malaysia, which share similar racial demographics. However, statistical analysis of information on the SARS CoV-2 from a database, especially GISAID, does not contain specific customizations related to virus comparisons between selected countries. Therefore, the researchers conducted statistical analysis and data visualization using the Python programming language to describe and investigate SARS CoV-2 Indonesia and Malaysia from the GISAID database. SARS CoV-2 metadata from Indonesia (N=117) and Malaysia (N=250), which were gathered during 2020, were compared. This comparison was aimed to investigate the discrepancies of COVID-19 cases in closely related populations. Firstly, data visualization was conducted using the Python Matplotlib library to create bar charts for clades and mutation comparison. Additionally, a series of boxplots were generated to show age discrepancies stratified by gender. Furthermore, the statistical tests showed that only the dominant Malaysian (G and O) clades were found to be significantly different compared to Indonesian cases (p-value=0.016). The proportion of two major mutations (G614D and NSP12 P323L) were also significantly different in the two countries caused by the dominant clade differences (p-value=0.007). Lastly, the differences in the age distribution of COVID-19 cases between the two countries were significant only in the male group (p-value=0.017).

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

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

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International Conference on Informatics and Computational sciences (ICICoS)

作者： Cuk Tho Harco Leslie Hendric Spits Warnars Benfano Soewito Ford Lumban Gaol BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia 11480

ISBN: (数字)9781728195261

ISBN: (纸本)9781728195278

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.

关键词： Support vector machines Sentiment analysis Systematics Social networking (online) Machine learning Regression tree analysis Logistics

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Model Decision Support System for Diagnosis COVID-19 Using Forward Chaining: A Case in Indonesia 8

Model Decision Support System for Diagnosis COVID-19 Using F...

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8th International Conference on Cyber and IT Service Management, CITSM 2020

作者： Henderi Maulana, Miftah Warnars, Harco Leslie Hendrie S. Setiyadi, Didik Qurrohman, Taufik University of Raharja Department of Informatics Engineering Tangerang Indonesia Postgraduate of Information Engineering University of Raharja Tangerang Indonesia BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Insani University Department of Informatics Engineering Bekasi Indonesia

ISBN: (纸本)9781728177014

The government in Indonesia and its staff work together to make tactical steps to prevent the spread of COVID-19 in the community. From the ministerial level to the heads of the provinces, regencies, and even the government. Therefore, this study aims to make a model decision support system to diagnose patients exposed to Covid-19, such as people in control, patients in oversight, and those who are positive for the Covid-19 Virus. Model decision support system development aims to provide information about the development of COVID- 19 and help the community in diagnosing themselves related to COVID-19 infection. In this study, the authors use the forward chaining method in application to get conclusions from the symptoms of the Covid-19. This research resulted in an application that patients exposed to the Covid-19, and it's also provided a solution for healing from patients. And this could be a reference for patients before consulting further with the doctor. © 2020 IEEE.

关键词： Decision support systems

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