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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data

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Procedia computer science 2021年 179卷 202-210页

作者： Arif Budiarto Bharuno Mahesworo Alam Ahmad Hidayat Ika Nurlaila Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Genomics study, as opposed to socio-anthropology, has been demonstrated as an excellent tool to picture biological relatedness and disease risk factors. To analyze the data obtained from the study, Genome-wide Association Study (GWAS) has been more than decades known as the mainstay approach., is the most popular approach in analysing genomics data. The confounding variables selection, being that ancestry estimation or population stratification, is substantial to maintain the quality of GWAS. Researchers have developed various methods in extracting the population stratification information from high dimensional genomics data, especially Single Nucleotide Polymorphisms (SNPs) data. In the present study, we proposed an implementation of Principal Component Analysis (PCA)-complemented Gaussian Mixture Model (GMM) as an unsupervised model to estimate population stratification from samples. The results derived from this approach was further compared to that resulted from K-means and from the commonly used ancestry estimation software, fast STRUCTURE. We figured out that our recent improved approach outperformed the two later mentioned as shown by the average cluster and population scores. Furthermore, it was able to generate the probability distribution of each sample across all population, despite its limited quality. These intriguing results worth further investigations with much more comprehensive population coverage and more advanced algorithm.

关键词： Population Stratification Ancestry Estimation Gaussian Mixture Model Genomics SNPs

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Designing Wireless Sensor Network Routing on Agriculture Area Using the LEACH Protocol 3

Designing Wireless Sensor Network Routing on Agriculture Are...

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3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

作者： Gitakarma, Made Santo Priyambodo, Tri Kuntoro Suyanto, Yohanes Sumiharto, Raden Wijayanto, Danur Universitas Gadjah Mada Post Graduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Master Program of Computer Science Yogyakarta Indonesia

ISBN: (纸本)9781728184067

Microelectronic technology that supports the establishment of wireless sensor networks (WSN) has brought hope to the ease of Internet of Things (IoT) technology that can generate smart environments. A WSN consists of a collection of sensor nodes that are small, intelligent, and inadequate in storage, energy, and processing power. One of the well-known routing techniques in WSN is LEACH. There have been many LEACH studies and their development with random node distribution. Here we designed several scenarios in WSN routing with the position of the sensor nodes specified in the mapping area. MATLAB simulation results show network lifetime can be extended by reducing data packet size. In five experiments, the number of 40 nodes was the optimal number to extend network lifetime. Each scenario has advantages and disadvantages that can be referenced in developing the LEACH protocol. © 2020 IEEE.

关键词： Internet of things

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Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm 1

Predicting Relegation Clubs in Italian Serie A with Method b...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Syukmana, F. Wahyudi, E. Gata, W. Wahono, H. Febianto, N.I. Kuntoro, A.Y. Nitra, R.O. Effendi, L. Saputra, D.D. Sulaeman, O.R. Computer Science Postgraduate Program STMIK Nusa Mandiri Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Indonesia

The purpose of this study is to help small clubs from Italian Serie A in finding the minimum targets to avoid relegation into Serie B competition (below Serie A league). Relegation will reduce the club's income from TV revenues and the decline of enthusiastic supporters. Based on the data from the final standings (seasons 2006 until 2018), this can be explained by the Decision Tree method using the C4.5 algorithm. The methods used in this study are data collection, data pre-processing, model proposal, model testing, and model validation. In this study, it is expected that the value of accuracy exceeds 85% to achieve a proper classification. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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XSS Attack Detection With Machine Learning and n-Gram Methods

XSS Attack Detection With Machine Learning and n-Gram Method...

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International Conference on Information Management and Technology (ICIMTech)

作者： Gulit Habibi Nico Surantha BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.

关键词： Machine learning Uniform resource locators Support vector machines Training data Cross-site scripting Machine learning algorithms Feature extraction

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Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

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International Conference on Informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

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Performance Evaluation of Queue Algorithms for Video-on-Demand Application

Performance Evaluation of Queue Algorithms for Video-on-Dema...

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International Conference on Information Management and Technology (ICIMTech)

作者： Yoni Irawan Nico Surantha BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728170718

ISBN: (纸本)9781728170725

Nowadays, there are real-time internet applications that require low latency and high bandwidth usage. In order to satisfy this requirement, many active queuing algorithms are offered, such as RED, BLUE, and controlled delay (CoDel). The number of publications aimed at evaluating the performance of the active queue management (AQM) algorithm is still limited by using a network simulator and only comparing two algorithms. The combination of AQM and connection types (fairness and no-fairness) in real network implementation has not been discussed much. In this paper, we offer a comparison of the performance of three queuing algorithms, namely DropTail, BLUE, and CoDel, with two types of connections, namely fairness and no-fairness using real network devices and internet video streaming access. Experiments carried out in the real network environment using Sandvine PL8840's bandwidth management device. The quality of services (QoS) parameters that will be compared are throughput, latency, and packet loss. The comparison results show that fairness connections on all queuing algorithms can maintain low latency values; throughput is smaller than no-fairness connections. The absence of packet loss in streaming video based on the TCP protocol does not guarantee that the video being played runs well because it depends on the available bandwidth capacity.

关键词： Bandwidth Delays Packet loss Throughput Streaming media Quality of service

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Transformative Sport science Using Social Media as a Collaboration Tool 7

Transformative Sport Science Using Social Media as a Collabo...

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

作者： Oktavia, Tanty Gaol, Ford Lumban Hosoda, Takaaki Syahir, Arsyan 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 Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Sports science can have a significant and positive impact on coaches and athletes, as well as for the professional sports organization. According to this situation, more and more organizations are competing to implement sports science as the monitoring systems. Along with the development of technology, it becomes easier for humans to carry out their activities, including communication with each other. Advances in the field of communication technologies have led to the use of social networks to support all activity. With social media, all kinds of information that people want to search for can be found easily. Social networks have their uniqueness in their characteristics. Like LINE has free calling and video calling features, while Instagram has features like Instagram Story, where users can document their activities in the form of photos or recordings. By all the characteristics of social networks, it will support the activity of sports science. The methodology of this research focused on quantitative research based on the T-test approach. The result shows that social media involvement can significantly support collaboration in sports science, so it can increase people's motivation to interact with other participants in sport organizations. © 2020 IEEE.

关键词： Sports

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Autonomous car using CNN deep learning algorithm 2

Autonomous car using CNN deep learning algorithm

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Sonata, I. Heryadi, Y. Lukas, L. Wibowo, A. Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta12930 Indonesia

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. © Published under licence by IOP Publishing Ltd.

关键词： Autonomous vehicles

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A prototype of Baby Monitoring Use Raspberry Pi 7

A prototype of Baby Monitoring Use Raspberry Pi

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

作者： Rudyansyah, Aldy Hendric Spits Warnars, Harco Leslie Lumban Gaol, Ford Matsuo, Tokuro Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Graduate School of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665404228

The security system in monitoring the room or something suspicious activity that can cause crimes in the field of information and technology as in private homes is indispensable. Aims to keep valuables from theft and loved ones like babies from kidnapping or reducing the risk of an accident in an unguarded infant activity. The use of CCTV still has constraints because some functions are still done manually such as should always move the record of the CCTV data every day to different memory so that there is waste in using memory. Moreover, the position of the camera can not be adjusted freely in the placement so that it can easily be destroyed by irresponsible people, and also ordinary CCTV needs electricity that is considerable in its use. This research is done to improve and facilitate the process of monitoring of baby activity and the room where the baby is called sleep or rest, all monitoring activities can be done using only a Web browser that can be accessed by a smartphone or desktop. The monitoring system is created using a program called RPi Cam that is installed on the Raspberry Pi with a Raspberry Camera module as a monitoring camera and a DHT22 sensor module as a room temperature monitor. © 2020 IEEE.

关键词： Security systems

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