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Information Technology (ACIT)

作者： Dina Fitria Murad Mohamad Toha Hendra Mayatopani Meta Amalya Dewi Riyan Leandros Silvia Ayunda Murad Yaya Heryadi Bambang Dwi Wijanarko Information Systems Department Binus Online Learning Bina Nusantara University Jakarta Indonesia Universitas Terbuka Jakarta Indonesia Department of Information System Pradita University Tangerang Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Computer Science Bina Nusantara University Jakarta Indonesia

MOOCs are one of the developments or impacts of e-learning or online learning. Massive Open Online Course (MOOC) is one of the choices for students to increase their knowledge through non-formal channels. Our research aims to determine the level of readiness of students regarding MOOC learning and opportunities for utilizing recommendation systems in students' decision-making regarding selecting MOOCs that suit their interests and needs. We conducted a survey regarding the constraints and needs of learner readiness through 5 aspects, namely 1) technical aspect, (2) social aspect, (3) communication aspect, (4) lecture readiness aspect, and (5) online discussion. The results of this research show that the main challenges that MOOC participants may face in a MOOC environment are Technical Aspects (28.25%), Social Aspects (21.47%), and Communication Aspects (18.93%). Meanwhile, the greatest strength of online learning in the MOOC environment is the social aspect (21%), the most influential and continued with technical and other aspects. Meanwhile, the lecture readiness aspect is quite good, and the online discussion aspect is the biggest aspect that influences the success of MOOC learning. Other results of this research indicate the importance of a recommendation system for MOOC students to make it easier for students to choose the right MOOC according to their interests and needs.

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Recommendation News For Joumalist Based on Twitter Data Using CNN

Recommendation News For Joumalist Based on Twitter Data Usin...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ikrar Harvy Abba Suganda Girsang Melva Hermayanty Saragih Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods.

关键词： Training Convolution Social networking (online) Neural networks Blogs Training data Motion pictures

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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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Smart Mobile Phone Charging Stations for Public Service as Smart Cities Component 7

Smart Mobile Phone Charging Stations for Public Service as S...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Pramono, Andi Febriantono, M. Aldiki Primadani, Tiara Ika Widia Purnomo, Agung Maulana, Fairuz Iqbal Agustina, Ira Audia School of Design Bina Nusantara University Interior Design Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The need to charge a smartphone is because the battery on a smartphone is designed not to have long enough power, especially the need for the smartphone for browsing using a data plan. Mobile phone charging stations in public areas are generally open, and some have also implemented a manual lock on each charging box. Some boxes lose their keys due to someone's carelessness, resulting in the box being unable to be used by others. This research aims to make a prototype mobile charging station equipped with a fingerprint security system. The method used in this research is a case study by making a prototype of a mobile phone box charging station. The security system in the charging station author utilized Arduino Mega connected to the solenoid controlled by fingerprint to open and lock each box. The device successfully registered each user's fingerprint based on the results of creating a prototype of a security system using a fingerprint. The device can read the correct fingerprint to open and close the solenoid in the box. In the case of unrecorded or mismatched fingerprints, the device will issue a warning and refuse to allow access to open and close the box. As a result, this security system is ideal for use in a prototype mobile charging station. © 2022 IEEE.

关键词： Smartphones

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Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecting the Ripeness of Oil Palm Fruit Bunch

Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecti...

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International Conference on ICT For Smart Society (ICISS)

作者： Muhammad Reza Azhar Priyadi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs_ loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4.

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Weather Prediction in Agriculture Yields with Transformer Model

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Procedia computer science 2024年 245卷 750-758页

作者： Yulyani Arifin Ilvico Sonata Maryani Elizabeth Paskahlia Gunawan Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia Information System Department School of Information System Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta 11480 Indonesia

Agriculture has a lot of relations with SDG from United Nations especially in end hungers and sustainable agriculture. One of factor important in agriculture is weather. Weather prediction is very important in agriculture. Weather greatly influences agricultural yields and the types of crops to be planted. Weather predictions, especially temperature and humidity, can be done using time series data analysis. Current developments in deep learning enable more accurate predictions of temperature and humidity based on time series dataset. Some deep learning models can be used to predict data based on training using time series data. The deep programming models used include Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The Transformer deep learning model, which was first introduced in 2017, is a sequence-to-sequence model which is very suitable for predicting time series data. This paper will explain temperature and humidity prediction using the Transformer deep learning model. Experimental results show that the Transformer model can predict temperature and humidity based on training results using a time series dataset. The accuracy obtained by the Transformer deep learning model is better compared to the LSTM deep learning model as indicated by lower MAE, MSE and RMSE values compared to the LSTM model.

关键词： Forecast LSTM SDG Goals Time series Transformer Weather

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Vegetation Coverage and Urban Amenity Mapping Using computer Vision and Machine Learning

Vegetation Coverage and Urban Amenity Mapping Using Computer...

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3rd International Conference on Image Processing and Vision Engineering, IMPROVE 2023

作者： Karkut, Nicholas Kiriluk, Alexey Zhang, Zihao Zhu, Zhigang Computer Science Department The City College of New York - CUNY New YorkNY10031 United States Graduate Landscape Architecture Program The City College of New York - CUNY New YorkNY10031 United States PhD Program in Computer Science The Graduate Center - CUNY New YorkNY10016 United States

ISBN: (纸本)9789897586422

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. Image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection;(2) vegetation analysis, bench detection and depth estimation;and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Vegetation mapping

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A Systematic Literature Review of Recent Trends and Challenges in Digital Twin Implementation

A Systematic Literature Review of Recent Trends and Challeng...

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International Conference on ICT For Smart Society (ICISS)

作者： Ikrar Adinata Arin Meyliana Harco Leslie Hendric Spits Warnars Dina Fitria Murad Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia

This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology.

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Comparative Analysis of SAAS Model and NPC Integration for Enhancing VR Shopping Experiences

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Applied sciences (Switzerland) 2024年第15期14卷 6573页

作者： Doungtap, Surasachai Wang, Jenq-Haur Phanichraksaphong, Varinya International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei10608 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei10608 Taiwan

This article examines the incorporation of the Shopping Assistance Automatic Suggestion (SAAS) model into Virtual Reality (VR) environments in order to improve the online shopping experience. The SAAS model employs sophisticated deep learning methods to offer customized product recommendations, which are conveyed by non-player characters (NPCs) via voice-based interactions. Our goal is to develop an interactive shopping experience that replicates real-life interactions by integrating AI-powered recommendations with immersive VR technology. We gather and standardize data from several open commerce databases, such as Amazon Product and Customer Reviews. The SAAS model, in conjunction with GPT-3, BERT, and T5, undergoes training and testing to evaluate its effectiveness across multiple criteria. The results demonstrate that the SAAS model surpasses other models in delivering contextually aware and pertinent recommendations. The integration process outlines the specific steps involved in capturing, processing, and transforming user interactions in virtual reality (VR) into vocal suggestions provided by non-player characters (NPCs). This strategy improves customization and utilizes the immersive features of virtual reality to effectively engage people. The results of our research establish a higher standard for e-commerce, with the goal of enhancing the user experience of online purchasing by making it more instinctive, engaging, and pleasurable. © 2024 by the authors.

关键词： Data mining

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A Comprehensive Analysis of: A Systematic Review 4th

A Comprehensive Analysis of: A Systematic Review

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4th International Conference on Electronics, Biomedical Engineering, and Health Informatics, ICEBEHI 2023

作者： Lestari, Dian Maulana, Fairuz Iqbal Purnomo, Agung Adi, Puput Dani Prasetyo Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia

ISBN: (纸本)9789819714629

This systematic review explores the rising use of virtual clinics, including telemedicine technologies like virtual visits and virtual clinics, in maternity care. These technologies enable continuous monitoring of expectant mothers, offering valuable and satisfactory services, especially in the post-COVID-19 pandemic era. The study aims to comprehensively analyze the applications, effectiveness, benefits, challenges, and overall impact of virtual clinics in gynecological and pregnancy care. A systematic review search encompassing the period from 2019 to 2023 was performed on Scopus and PubMed databases. Additional manual searches on reference lists were conducted for comprehensive coverage. Eight articles were included, comprising various study designs, such as experimental, cohort, and cross-sectional studies. Pregnant women, parents, and healthcare practitioners were the target users. Virtual clinics were utilized for virtual visits, consultations, monitoring, follow-up, and home care, resulting in highly satisfactory patient experiences. However, challenges, including poor internet connection, were identified. The reviewed studies reveal promising outcomes in patient and provider satisfaction. Virtual clinics have the potential to revolutionize maternity care by providing accessible, efficient, and personalized healthcare services. Despite technical challenges, the integration of telemedicine solutions, particularly virtual clinics, is expected to play a growingly significant role in obstetric care. These insights are valuable for healthcare providers, policymakers, and researchers, leading to improved maternal care outcomes through the adoption of virtual clinics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Telemedicine

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