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检索条件"机构=Computer Science Program Computer Science Department School of Computer Science Bina Nusantara"

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Prediction of Oil Palm Conditions Using Deep Learning Based on the Visible Atmospherically Resistant Index on UAV Imagery

Prediction of Oil Palm Conditions Using Deep Learning Based ...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Mimboro, Prasetyo Soewito, Benfano Soeparno, Haryono Budiharto, Widodo Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

Artificial neural network (ANN)-based computer vision techniques are becoming increasingly popular for palm oil disease detection and classification. Deep learning models' capacity to automatically learn and extract relevant image features has enabled accurate and efficient detection and classification of palm oil diseases. In this research, research was conducted to test the deep learning method to predict the condition of oil palm plantations based on the visible atmospherically resistant index on the Unmanned Aerial Vehicle Image. Some diseases that can attack oil palm trees are root disease or oil palm root rot (blast disease), basal stem rot (ganoderma), bud rot (spear rot), yellow line disease (patch yellow). This study aims to predict the condition of oil palm trees based on the VARI so that the process of detecting the spread of disease in oil palm trees can be accelerated. In this study, the prediction model for the condition of oil palm trees using the ANN algorithm succeeded in predicting the condition of oil palm trees and provided satisfactory prediction results, namely an accuracy rate of 94.7% and a loss of 21.58%. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic

Machine learning's model-agnostic interpretability on the pr...

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作者： Miranda, Eka Aryuni, Mediana Rahmawati, Mia Ika Hiererra, Siti Elda Dian Sano, Albert Verasius Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Surabaya Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Background: COVID-19 prompted a global shift to online learning, including video conference-assisted online learning (VCAOL), which necessitated educators understanding students' perspectives. Objective: This study aims to develop machine learning (ML) model-agnostic interpretability that could predict students' academic performance in VCAOL. Material and methods: Synthetic Minority Over-sampling Technique (SMOTE) and data augmentation were used to handle imbalanced data from small-scale datasets. The prediction model was developed using Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). SHAP model-agnostic interpretability was used to interpret and comprehend prediction findings. The data was gathered from September 2022 to January 2023, resulting in 361 records. The research variables included students' academic performance as the dependent variable, and the video conference application (VC), learning material (LM), internet connection (IC), students' ability to learn (SL), and student knowledge (SK) as independent variables, which were mapped into 28 attributes. Result: The SMOTE improved the performance of three algorithms, with RF outperforming SVM and GNB in almost all tests, achieving an accuracy of 79.45%, precision of 75.71%, and recall of 79.45%. SHAP bar plots ranked attributes by importance demonstrated that "Performance," "Frequency Constraint," and "Increase Value" had a significant impact on prediction results. When we mapped the three attributes to our study perspective, we determined that SK and SL were the most important views for students to perform well in VCAOL. SHAP's beeswarm revealed students' performance in VCAOL was positively correlated with "Performance", "Increase Value", "Completing Project", "Adequate Method", "User Interface", and "Feature". As we mapped the three attributes to our study perspective, we found that SK, LM, SL, and VC were positively related to students' performance in VCAOL. Conclusion: T

关键词： Adversarial machine learning

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Jakarta Flood Map: Map Visualization for Flood Area in Jakarta Using Spatio-Temporal Analysis

Jakarta Flood Map: Map Visualization for Flood Area in Jakar...

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2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Wijaya, Annabelle Sharon Wijaya, I Nyoman Wahyu Wijaya, Stefanova Anderson, Keane Deovan Kurniadi, Felix Indra Riccosan Pramudya, Fabian Surya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Mathematics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350331172

Floods are a frequent occurrence in Indonesia, particularly in DKI Jakarta. Flood disasters cause significant losses to human health, the economy, and environmental damage. This research aims to assist government data collection efforts and raise public awareness through Map Visualization of flood-prone areas in Jakarta using ArcGIS. The data processing begins with data collection and data cleaning methods applied to a dataset encompassing flood quantities from 2021 to 2022. The processed dataset is then used for modeling in ArcGIS to generate map visualizations and analyze them with the spatiotemporal analysis method. The research results are expected to effectively inform the government and community to collaborate in future flood prevention initiatives in Jakarta. © 2023 IEEE.

关键词： Floods

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Comparative Analysis of ARIMA and Prophet Algorithms in Bitcoin Price Forecasting 8

Comparative Analysis of ARIMA and Prophet Algorithms in Bitc...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Angelo, Michael David Fadhiilrahman, Ilhas Purnama, Yudy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The purpose of this research is to compare ARIMA and Prophet algorithms and find the best algorithm for forecasting bitcoin prices. The dataset is two years historical bitcoin data between February 2019 and 2021. The data is segmented into daily, weekly, and monthly period category. Both algorithms are built into a univariate model that only receive 2 features for training the model. Several ARIMA models is developed for each dataset interval. After that, the parameter of each model will be cross-referenced to each other to obtain the best parameter combination. Meanwhile, Prophet model will be developed using automatic and manual tuning. Then again parameter value of each model will be cross-referenced to each other to obtain the best parameter combination. Evaluation of the training model is done by calculating the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Rooted Mean Squared Error (RMSE). The results showed that the best model for the daily and weekly data category was the Prophet algorithm, while for the monthly data category was the ARIMA algorithm. © 2023 The Authors. Published by Elsevier B.V.

关键词： ARIMA Bitcoin Comparison Forecasting Prophet Time Series

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Fresh Food Product Insurance Model with Blockchain Technology 4

Fresh Food Product Insurance Model with Blockchain Technolog...

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4th International Conference on Electrical Engineering and computer science, ICECOS 2024

作者： Inayatulloh Kusumadi Hartono, Indra Yasri School of Information System Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368253

One of the important components of the food industry is the availability of fresh food. The long supply chain for fresh food, from farmers to consumers or processed food companies, has an impact on the high risk of damage to fresh food products. Several parties involved in the fresh food supply chain transfer their risks to insurance companies to reduce the risk of loss. Meanwhile, there is currently no transparent insurance mechanism that provides transparent information for parties involved in fresh food insurance. Fresh fruit producers do not receive valid and accurate information about the reasons for the rejection of insurance claim submissions. Likewise, insurance companies do not receive valid and accurate information about the reasons why fresh fruit producers do not pay insurance premiums. Blockchain is a technology that has the advantage of creating transparent transactions with the concept of peer-To-peer validation. Premium payment transactions and claim submissions will be stored on the blockchain network if the transaction has passed the peer-To-peer validation process. Thus, this research aims to create a fresh food insurance model using blockchain technology. The research method uses a qualitative approach by studying the fresh food supply chain, insurance processes, and blockchain technology. The result of this research is a model and simulation with EVM for blockchain adoption in fresh food insurance. © 2024 IEEE.

关键词： Food industry

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Mobile Application to Help Abandoned Pets 3

Mobile Application to Help Abandoned Pets

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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An innovative artificial intelligence-based visualization and rendering for e-commerce startup booth design 9th

An innovative artificial intelligence-based visualization an...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kalleya, Calista Pramono, Andi Angelina, Chelsea Putri Cuarista, Wilbert Alvin Prabowo, Riefky Maulana, Fairuz Iqbal Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Marketplace development is increasing, as is the number of people with active internet access. Unive is a startup that runs an e-commerce platform connecting Student Activity Units with consumers. Focuses on fostering student entrepreneurship through practical business experience. For its development, this startup needs an interior for business escalation. Designers need software integrated with artificial intelligence (AI) in interior design. This study explores the integration of AI-powered rendering tools in the design of e-commerce platforms, focusing on how these tools enhance the user experience and satisfaction. The research employs a mixed-method approach, combining qualitative and quantitative analyses. The author collected data through user surveys, expert interviews, and performance metrics of AI rendering tools. The author based the evaluation criteria on adherence to interior design principles and the quality of rendering results. Findings indicate that selecting furniture and supporting components that adhere to interior design principles results in optimal rendering outcomes. These outcomes are close to user expectations, significantly improving user engagement and satisfaction. Detailed analysis shows the effectiveness of AI rendering in various design scenarios. The discussion highlights the implications of integrating AI rendering tools in e-commerce platforms. It addresses the potential of these tools to revolutionize user interaction and provides insights into future developments and applications in the field. This study demonstrates the value of AI-powered rendering tools in enhancing e-commerce platform design. By adhering to interior design principles, these tools can achieve rendering results that meet user expectations, ultimately leading to improved user experiences. © 2024 The Authors.

关键词： Marketplaces

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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats. © 2023 IEEE.

关键词： Cybersecurity

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Air Pollution Prediction using Random Forest Classifier: A Case Study of DKI Jakarta

Air Pollution Prediction using Random Forest Classifier: A C...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Muljana, Richie Ayuningtyas, Lintang Diah Daksa, Rayhan Prawira Djamhari, Simen Ferdinand Fiezayyan, Muhammad Ariiq Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

This research paper analyzes the Air Pollution Standard Index (APSI) in Jakarta, Indonesia, using the Random Forest Classifier (RFC). The study aims to predict the APSI level in Jakarta based on the concentrations of various pollutants, including particulate matter (PM), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). The data used in the study was collected from a public website called Jakarta Open Data from January to December 2020. The SMOTE-Tomek technique was used to handle an imbalanced dataset in this work. The results show that the RFC model accurately predicts the APSI level in Jakarta with an accuracy of 95%. In addition, RFC can identify ozone (O3) and particulate matter (PM) are the most critical factors influencing the APSI level. This study provides valuable insights into the factors influencing air pollution in Jakarta and can be used to inform city decision-making regarding air quality management. This paper discusses the finding's significance and potential future research directions. © 2023 IEEE.

关键词： Carbon monoxide

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Identify the Freshness of the Fruit by Using CNN and SVM 4

Identify the Freshness of the Fruit by Using CNN and SVM

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Yashadhana, Rashawn Conrad, Jason William Hemanda, Nathan Raditya Lucky, Henry Iswanto, Irene Anindaputri School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

This paper presents a novel approach to fruit freshness classification using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The aim is to automate the process of determining the freshness of fruits, which is traditionally done manually, and thus prone to human error. The study utilizes a dataset composed of images of fresh and rotten fruits, including apples, bananas, and oranges. The images are processed and used to train a pre-trained CNN model, ResNet50, which extracts high-level features from the images. These features are then used as input to an SVM classifier, which predicts the freshness of the fruits. The performance of the models is evaluated using various metrics, including accuracy, precision, and F1 score. The results show that the SVM model consistently outperforms the CNN model across all tested hyperparameters, demonstrating its effectiveness in this task. The findings of this study have significant implications for the food industry, as they suggest that machine learning techniques can be effectively used to automate and improve the accuracy of fruit freshness classification. However, future research could explore alternative fusion strategies to combine the information from different layers of the ResNet50 model. Techniques such as late fusion, early fusion, or attention mechanisms can be investigated to improve the integration of features from different ResNet50 layers, potentially leading to enhanced classification performance. © 2023 IEEE.

关键词： Fruits

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