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International Conference on Informatics and Computing (ICIC)

作者： Dedy Prasetya Kristiadi Muhaimin Hasanudin Sutrisno Jennifer Salim Michael Glenn Gunawan Angeline Laurence Antoni Chandra Harco Leslie Hendric Spits Warnars Teddy Mantoro Raharja University Tangerang Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Science and Technology Sampoerna University Jakarta Indonesia

We as a human can not avoid the disease, and when we are sick, we need to see a practical doctor. However, in term of location and patient's right for changing the practical doctor, then the patient when moving from one practical doctor to others will have similar and repeated questions such as medicine allergy, history of diseases and so on. Moreover, become a problem for a vulnerable patient such as unconscious patient, disabled or elderly when seeing a new practical doctor and at the end of the day, it will become malpractice. Few of the causes of the problem above are the lack of health education, delay in diagnosing/misdiagnosis, and inaccessible healthcare facilities and with the increasing popularity of telemedicine and IoT across the world, then we need to apply IoT in order to help the patient. In this paper, a framework is created and designed for an IoT-integrated mobile application for the patient where this mobile application is expected to be implemented.

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Development Conceptual Model and Validation Instrument for E-Learning Succes Model at Universities in Indonesia: Perspectives influence of Instructor's Activities and Motivation

Development Conceptual Model and Validation Instrument for E...

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International Congress on Applied Information Technology

作者： Titan Erick Fernando Dina Fitria Murad Harco Leslie Hendric Spits Warnars Kristianus Oktriono BINUS Online Learning Bina Nusantara University Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Language Center Faculty of Humanities Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728158624

ISBN: (纸本)9781728158631

The purpose of this study is to develop conceptual models and validation instruments for the success of e-learning systems in Indonesian universities from factors that influence instructor activity and user motivation. Model development is based on the IS success model of DeLone and Mclean by adding instructor activity and motivational variables based on related literature studies. Evaluation is done by testing the reliability and validity of the instrument. Data collection by distributing instruments online with the snowball method, respondents obtained were 234 used as data analysis with SmartPLS 2.0 software. The results of this study produced a conceptual model consisting of 9 variables and 83 instruments which were validated according to the provisions. From these results, it can be implemented in future research by increasing larger respondents to get results that represent the success of e-learning systems at universities in Indonesia.

关键词： Electronic learning Information systems Instruments Reliability Learning systems Organizations Computational modeling

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Autonomous car using CNN deep learning algorithm

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Journal of Physics: Conference Series 2021年第1期1869卷

作者： I Sonata Y Heryadi L Lukas A Wibowo Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta Indonesia 11480 Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta Indonesia 12930

Autonomous cars have become an interesting discussion in recent years. Several major automotive industries such as Tesla, GM and Nissan are trying to become pioneers in autonomous car technology. Giant technology companies such as Google Waymo, Baidu and Aptiv also developing autonomous car technology. Several technological approaches are carried out in implementing autonomous cars for recognizing surrounding situations, such as radar, lidar, sonar, GPS, and odometry. An automatic control system is used to control navigation based on the data obtained from these sensors. This paper will discuss the use of CNN deep learning algorithm for recognizing the surrounding environment in creating the automatic navigation required by autonomous cars. The system designed will create and learn the data set that will be taken in advance and the learning outcomes will be implemented in an open simulation system. This simulation shows high accuracy in learning to navigate the autonomous car by observing the surrounding environment.

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Measurement of activeness lecturers in SmartLecturer based learning using fuzzy Mamdani and Sugeno

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Sfenrianto Sfenrianto Muhammad Yasin Kaman Nainggolan Mesra Betty Yel Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Informatics Management Politeknik LP3I Jakarta Indonesia 10450 Master of Computer Science - Postgraduate Programs STMIK Nusa Mandiri Faculty of Computer Science STIKOM CKI Jakarta Indonesia

The success of learning in a classroom that uses supporting media demands lecturer activity. The level of activity of lecturers in class and using learning media such as Smart lecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the Mamdani method indicates low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

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Comparing CART and C5.0 algorithm performance of human development index 3

Comparing CART and C5.0 algorithm performance of human devel...

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3rd International Conference on Informatics and Computing, ICIC 2018

作者： Assiroj, Priati Warnars, Harco Leslie Hendric Spits Fauzi, Ahmad Dept. of Information System Faculty of Technology and Computer Science Buana Perjuangan University Karawang Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Dept. of Computer Science Faculty of Technology and Computer Science Buana Perjuangan University Karawang Indonesia

ISBN: (纸本)9781538669204

Human Development Index (HDI) is a reference to how far the administration can develops their people. The development of a territory is related to a prosperity of the people. This research is focused on Human Development Index in West Java, a Province of Indonesia. Research identifies variables which are influence and very important to HDI and compares the model of CART and C5.0 algorithm based on HDI concept that has developed by United Nation (UN). Research uses 14 variables which are commonly used for measuring HDI and found that there are two variables that most influencing to HDI, MYS-index and EDU-Index. By knowing the MYS and EDU-Index research can predict and classify each territory to its HDI. Research could be conducted to predict HDI in the other territory. The results evaluated by measuring the accuracy and confusion matrix, 91.35 for CART algorithm and 90.80 for C5.0 algorithm. © 2018 IEEE.

关键词： Algorithm Classification Comparison EDU-Index Human Development Index (HDI) MYS-Index Territory

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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting 3

Single Layer & Multi-layer Long Short-Term Memory (LSTM) Mod...

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3rd International Conference on computer science and Computational Intelligence, ICCSCI 2018

作者： Salman, Afan Galih Heryadi, Yaya Abdurahman, Edi Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Science and Technology Sanata Dharma University Yogyakarta55282 Indonesia

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive weather observation data have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. The purposes of this research are to build a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport area and to explore the effect of intermediate weather variable related to accuracy prediction using single layer Long Short Memory Model (LSTM) model and multi layers LSTM model. The proposed forecasting model is an extension of LSTM model by adding intermediate variable signal into LSTM memory block. The premise is that two highly related patterns in input dataset will rectify the input patterns so make it easier for the model to learn and recognize the pattern from the training dataset. In an effort to achieve a robust model for learning and recognizing weather pattern, this research will also explore various architectures such as single layer LSTM and Multiple Layer LSTM (4 layers LSTM). The dataset is weather variable data collected by Weather Underground at Hang Nadim Indonesia airport. This research used visibility as predicted data and temperature, pressure, humidity, dew point as intermediates data. The best model of LSTM in this experiment is multiple layers LSTM and the best intermediate data is pressure variable. Using the pressure variable this model has gained the validation accuracy 0.8060 and RMSE 0.0775. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Long short-term memory

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Software development evaluation process using CMMI-Dev on limited resources company 3

Software development evaluation process using CMMI-Dev on li...

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3rd International Conference on science in Information Technology, ICSITech 2017

作者： Made Sugi Ardana, I. Suharjito Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781509058662

The quality of products is very important for a software development industry. The product quality is strongly influenced by the software development process. Software development company with limited resources is hard to improve the quality of the software produced. The method to be taken is to improve the software development process to achieve maturity level 2. In this study, CMMI-Dev (CMMI for Development) version 1.3 with continuous representation is used as a reference. SCAMPI-C with PST Tool was used to conduct the assessment of the 5 (five) process areas of CMMI level 2. Data were collected by interviews and observations of project documentation and the process of software development. Unapplied practice from assesment then be analyzed using Ishikawa Analysis to find root of the problem refers to the SPF (Software Process Framework) and subsequently use Pareto Analysis to determine the improvements priority. The process which have already good are in the process area PP (Project Planning), CM (Configuration Management), and most of the processes in the area of PMC (Project Monitoring and Control). Software development processes which are need to be improved in the process area with priority PPQA (Process and Product Quality Assurance), REQM (Requirements Management), then PMC (Project Monitoring and Control). The prior improvement from the SPF category were in the order of procedure category and then process category. © 2017 IEEE.

关键词： Software design

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IoT Solution for Intelligent Pond Monitoring

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IOP Conference Series: Earth and Environmental science 2020年第1期426卷

作者： Usin Darmalim Ferdinan Darmalim Sutristo Darmalim Alam Ahmad Hidayat Arif Budiarto Bharuno Mahesworo Bens Pardamean Mega Medica Pharmaceuticals Jakarta Indonesia 11840 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

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond.

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Key Successful Factors of Executive Information System Implementation in Higher Education: A Systematic Literature Review 3

Key Successful Factors of Executive Information System Imple...

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3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Winanti Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Gaol, Ford L. Computer Science Department Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Information Systems Department Bina Nusantara University School of Information Systems Jakarta11480 Indonesia Faculty of Computer Science Indonesia University Depok16424 Indonesia

ISBN: (纸本)9781538658215

Providing easy access for university executives that refers to key success factors is a must for universities. The information system integrates with all departments to obtain fast and accurate information for decision making. The purpose of this paper is to determine the factors that affect the application of executive information systems in universities in view of technological factors, data factors and internal factors of college. The results will be used by all stakeholders of higher education internally and externally. Internal side for the purposeof improving the quality and performance of universities, while the external side can be utilized for the purpose of the institution accreditation process. The method used is to collect research results relating to executive information systems in colleges and review based on predetermined keywords and analyze the results of reviews into a complete report. © 2018 IEEE.

关键词： Data warehouses

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Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram Data

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Procedia computer science 2018年 135卷 400-407页

作者： Bens Pardamean Tjeng Wawan Cenggoro Reza Rahutomo Arif Budiarto Ettikan Kandasamy Karuppiah Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 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 NVIDIA Corporation

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified.

关键词： Transfer learning Convolutional neural network Medical image analysis Chest X-ray Mammogram

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