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A review of traffic violation detection technology in reporting mechanism

IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： A Alaydrusl W K Putra Y Nugroho Muhammad N Surantha Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Traffic Violation Detection system using radio frequency identification (RFID) has been applied to detect vehicles with limited power sources through RFID Tag. Camera sensors are also applied to identify a vehicle and its plate number through image and video processing known as computer vision. On the other hand, vehicular ad hoc network (VANET) to gain information about location, speed, and so on through the vehicle-to-vehicle (V2V) connection. Lastly, the internet-of-things that is backed-up by cloud computing helps to store various information from each of these technologies and process them to get results. Many researchers have proposed and developed algorithms or technology with astonishing experimental results of them. However, there has been no review of the integration and reporting mechanism that correlates them. There has also been no review about how to connect the information to the authority and violator. Therefore, the review was made to explain each technology's method with their experiment's results and issues. Moreover, the future challenge also had been given to further research.

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FHC-Optimization Model for Deciding the Objective Hajj Pilgrims to Restricted Quota (Case Study: Hajj Pilgrimage Procedure in Indonesia) 1

FHC-Optimization Model for Deciding the Objective Hajj Pilgr...

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1st International Conference of Artificial Intelligence and Information Technology, ICAIIT 2019

作者： Utama, Ditdit Nugeraha Faturrahman, Muhammad Rusdi, Methamazid Saputra, Ibnu Yahya Isnaeni, Fuji Suci Waspodo, Bayu Computer Science Department BINUS Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System UIN Syarif Hidayatullah Jakarta Indonesia

ISBN: (纸本)9781538684481

In Indonesia, owing to limited quota, Hajj pilgrim determination to go to Mecca is a challenging activity to realistically do. The registered candidates of Hajj pilgrim cannot become Hajj pilgrims all 100%, several of them have to wait in list until years. Based on scientific view, this study was conducted. The objective of study is to construct a computer model based on optimization conception to objectively clarify the current-year Hajj pilgrims. By methodically employing the hill-climbing (HC) method in optimizing process and also applying fuzzy-logic concept in parameterizing parameters, a scheme of 'double-stages fuzzy hill climbing (FHC) optimization' was constructed. Through bearing in mind seven selected parameters, the Hajj pilgrims in restricted quota were determined. The paper provides the detailed configuration of an optimization schema and constructed model. It also delivers the constructed model's simulation result regarding the determined 155,200 Hajj pilgrims (according to 2016 Indonesia Hajj pilgrim quota) for 33 Indonesias provinces from more than 220 thousand dummy (experimental) data of Hajj pilgrim candidates. © 2019 IEEE.

关键词： Optimization

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Structural analysis based on unsupervised learning: Search for a characteristic low-dimensional space by local structures in atomistic simulations

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Physical Review B 2022年第7期105卷 075107-075107页

作者： Ryo Tamura Momo Matsuda Jianbo Lin Yasunori Futamura Tetsuya Sakurai Tsuyoshi Miyazaki International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba 305-0044 Japan Graduate School of Frontier Sciences The University of Tokyo Chiba 277-8568 Japan Department of Computer Science University of Tsukuba Tsukuba 305-8573 Japan Center for Artificial Intelligence University of Tsukuba Tsukuba 305-8573 Japan Master's/Doctoral Program in Life Science Innovation University of Tsukuba Tsukuba 305-8577 Japan

Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art atomistic simulations. However, it has become increasingly difficult to understand what is actually happening and mechanisms, for example, in molecular dynamics (MD) simulations. We propose an unsupervised machine learning method to analyze the local structure around a target atom. The proposed method, which uses the two-step locality preserving projections (TS-LPP), can find a low-dimensional space wherein the distributions of data points for each atom or groups of atoms can be properly captured. We demonstrate that the method is effective for analyzing the MD simulations of crystalline, liquid, and amorphous states and the melt-quench process from the perspective of local structures. The proposed method is demonstrated on a silicon single-component system, a silicon-germanium binary system, and a copper single-component system.

关键词： Amorphous materials Machine learning Molecular dynamics

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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Feasibility study of Information Technology Investment (a case study of ODOO ERP: Project Management Module Implementation in Indonesia Based Company) 10

Feasibility study of Information Technology Investment (a ca...

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Jingga, Fredy Kosala, Raymond Supangkat, Suhono Harso Ranti, Benny School of Information Systems Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta11480 Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9781728148809

IT Governance are one of the needs in managing Enterprise Level IT. This study shows part of the decision domain of IT Governance Help, which are IT Investment and Prioritization. The purpose of this study is to determine feasibility of implementation open source ERP system Odoo project management module that replaces current system based on operational, economic, and technical aspects. The method used are Fit/Gap Analysis and Cost Benefit model using economic impact worksheet. The analysis is done by comparing the requirement with features owned by current system and Odoo ERP system. This research resulted that Odoo ERP System is feasible in the 3 aspects, the operational feasibility Odoo reaching 72% Fit, the economic feasibility with ROI of 84%, and technical feasibility indicating the technical requirement can be fulfilled by leasing AWS server. The conclusion is the open source ERP system Odoo project management module is feasible to be applied in terms of 3 aspects that have been studied, those are operational, economic, and technical. © 2019 IEEE.

关键词： Cost benefit analysis

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MVC Architecture: A Comparative Study Between Laravel Framework and Slim Framework in Freelancer Project Monitoring System Web Based

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Procedia computer science 2019年 157卷 134-141页

作者： Andri Sunardi Suharjito Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Currently, the work of freelancers is very much in demand. Because freelancers can work anywhere and anytime without being bound by a contract with a company or person. But freelancers have difficulty managing their tasks and projects because there is no system to monitor and manage the project. Therefore, the solution is to make the project freelancer monitoring system by implementing the MVC (Model View Controller) architecture model with the PHP Laravel and Slim framework. MVC design patterns are well-known patterns and are used for interactive software system architectures. The way the MVC method works is to separate the main components such as data manipulation (model), display/interface (View) and the process (Controller) so that it is more neat, structured and easily developed. The purpose of this study also compares the MVC Laravel and Slim framework architecture with a performance comparison method on load/stress testing on the dashboard page using Apache JMeter tools with 3 scenarios from samples 1, 100, and 500. Tests are done offline and report format results of performance tests is a Summary Report. The results obtained from performance comparisons using Apache JMeter are that the Slim framework is faster and better than Laravel's framework.

关键词： Web Application Mvc Architecture Laravel Framework Slim Framework Compare Apache Jmeter Project Monitoring System

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Automatic Sleep Stage Classification using Weighted ELM and PSO on Imbalanced Data from Single Lead ECG

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Procedia computer science 2019年 157卷 321-328页

作者： Oei Kurniawan Utomo Nico Surantha Sani Muhamad Isa Benfano Soewito Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Sleep stage classification is one of important aspects in sleep studies, which can give clinical information for diagnosing sleep disorder and measuring sleep quality. Due to the difference in sleep stage proportion for every person, the collected sleep stage data are imbalanced naturally, which can lead to high probability of misclassification. Various learning method has been developed to classify sleep stage based on electrocardiogram (ECG) signal. However, to the best of our knowledge, there are no researches which consider the imbalanced dataset problem for sleep stage classification. In this research, a classification model of sleep stage based on ECG signal was developed using Weighted Extreme Machine Learning (WELM) to deal with imbalanced learning dataset and Particle Swarm Optimization (PSO) for feature selection. The research will use the MIT-BIH Polysomnographic Database, which contains 10154 sleep stage annotated ECG data which consist of 17.79%, 38.28%, 4.76%, 1.78%, 6.89%, and 30.5% data of NREM1, NREM2, NREM3, NREM4, REM, and awake stage respectively. From each ECG record, a total of 18 features were extracted and the feature selection process resulted in 10 features which highly affect the sleep stage classification. The proposed model successfully obtained a mean accuracy of 78,78% for REM, NREM and Wake stage classification and 73.09% for Light Sleep, Deep Sleep, REM, and Wake stage classification.

关键词： Sleep stage Classification Machine learning Weighted extreme learning machine

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Air Pollution Mapping using Mobile Sensor Based on Internet of Things

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Procedia computer science 2019年 157卷 638-645页

作者： Eduard Pangestu Wonohardjo Gede Putra Kusuma Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Air pollution is hazardous to our health, especially carbon monoxide. It can cause diseases such as cough, runny nose, eye irritation, and even death. The main objective of this research is to create a device capable of detecting carbon monoxide pollution levels by using mobile sensors and map the results into heatmaps overlayed on Google Maps. We have implemented an integrated pollution monitoring and mapping system that consists of MQ-7 sensor, GPS, GSM, display module, Arduino board, and web-server. We also evaluated two sampling methods, time-based and distance-based sampling. Based on our experiments, the distance-based sampling method produced well-distributed data and closer to the expected between-samples distances compared to the time-based method. We have also shown that our system can run in real time to monitor the carbon monoxide pollution levels.

关键词： CO pollution Pollution sensor Pollution mapping Mobile sensor Internet of things

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Early Detection of Diabetes Mellitus using Feature Selection and Fuzzy Support Vector Machine

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Procedia computer science 2019年 157卷 46-54页

作者： Rian Budi Lukmanto Suharjito Ariadi Nugroho Habibullah Akbar Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The number of patients that were infected by Diabetes Mellitus (DM) has reached 415 million patients in 2015 and by 2040 this number is expected to increase to approximately 642 million patients. Large amount of medical data of DM patients is available and it provides significant advantage for researchers to fight against DM. The main objective of this research is to leverage F-Score Feature Selection and Fuzzy Support Vector Machine in classifying and detecting DM. Feature selection is used to identify the valuable features in dataset. SVM is then used to train the dataset to generate the fuzzy rules and Fuzzy inference process is finally used to classify the output. The aforementioned methodology is applied to the Pima Indian Diabetes (PID) dataset. The results show a promising accuracy of 89.02% in predicting patients with DM. Additionally, the approach taken provides an optimized count of Fuzzy rules while still maintaining sufficient accuracy.

关键词： Diabetes Mellitus Feature Selection Fuzzy Logic Support Vector Machine

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Systematic Literature Review on Decision-Making of Requirement Engineering from Agile Software Development

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Procedia computer science 2019年 157卷 274-281页

作者： Reginald Putra Ghozali Herry Saputra M. Apriadin Nuriawan Suharjito Ditdit Nugeraha Utama Ariadi Nugroho Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This paper presents a systematic literature review of agile software development at decision making method for requirement engineering. Presently, agile software development method is operated to cope with requirements that changes dynamically. This study seeks to find out and discuss what types of method that have been exploited for decision making on managing feasible requirements and challenges of decision making in agile software development. Papers reviewed in this study are published from 2017 to present. Resulting 8 papers that have been identified of presenting decision making methods. Using these papers, 11 methods and 7 challenges of decision making identified. This study contributes a review of requirement management and engineering by providing decision making methods on agile software development and the challenges of decision making for requirement engineering.

关键词： Systematic Literature Review Agile Software Development Decision Making Requirement Engineering

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