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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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Protocol for a Graph Database of NTDs for Drug Repurposing 18th

Protocol for a Graph Database of NTDs for Drug Repurposi...

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18th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2024

作者： Giordani, André Ruiz, Duncan D. Graduate Program in Computer Science – PPGCC Pontifical Catholic University of Rio Grande do Sul – PUCRS Porto Alegre Brazil

ISBN: (纸本)9783031878725

Neglected tropical diseases (NTDs) are a significant health burden globally, contributing to 1–2% of all mortality. The quest for new pharmacological interventions for NTDs is hampered by the substantial time and financial resources required for drug development, alongside a marked disinterest from laboratories due to the geographic and economic profiles of affected regions. This study presents a novel protocol for leveraging graph database architectures to elucidate potential connections between NTDs and existing pharmaceuticals that may be repurposed for treatment. Focusing on tuberculosis as a case study, we constructed a graph database integrating comprehensive disease and drug data. By removing established links between tuberculosis and known treatments, we employed a Graph Convolutional Neural Network (Graph-CNN) algorithm to unearth previously unrecognized relationships. This computational approach not only facilitates the discovery of new drug-disease associations but also streamlines the repurposing process for existing medications. Our findings demonstrate the utility of advanced data engineering and machine learning techniques in identifying promising therapeutic candidates for NTDs, potentially accelerating the availability of effective treatments for these underserved diseases. The study’s contribution is underscored by a comparative analysis of state-of-the-art algorithms within our graph database framework, highlighting the efficacy of our protocol in the broader context of drug repurposing research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction 9

Examining the impact of attitude, availability, price, and q...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

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

ISBN: (纸本)9798350370126

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions. © 2023 IEEE.

关键词： Quality of service

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Spatial Temporal Changes in Mangrove Forests on Tarakan Island, North Kalimantan, Indonesia 3rd

Spatial Temporal Changes in Mangrove Forests on Tarakan Isla...

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3rd International Conference on Radioscience, Equatorial Atmospheric science and Environment, INCREASE 2023

作者： Giofandi, Eggy Arya Sekarjati, Dhanu Arshad, Mazlan Rahman, Henzulkifli Tjahjono, Boedi Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Graduate Program of Coastal and Ocean Resource Management Faculty of Fisheries and Marine Science IPB University Bogor Indonesia Graduate Program of Tropical Biodiversity Conservation Faculty of Forestry and Environment IPB University Bogor Indonesia Department of Soil Science and Land Resources Faculty of Agriculture IPB University Bogor Indonesia

ISBN: (纸本)9789819707393

Mangroves play an important role in providing essential ecosystem services and resources for fisheries, offering protection against climate change and coastal erosion. Understanding the changes in mangrove forests is essential for the sustainability of the coastal ecosystem. The study aims to analyze the spatiotemporal changes of mangrove forests in Tarakan Island using Sentinel-1 and Sentinel-2 images from 2018 and 2023. The analysis shows changes in land cover, with an increase in aquaculture land and a decrease in other land types. The results of the accuracy assessment show good results, with an accuracy value of 0.82 and a Kappa value of 0.78 in the mangrove areas in 2023. These values indicate that the classification of land cover in the study area was reliable and accurate. The conversion of mangrove forests into aquaculture land poses a challenge for their conservation. Therefore, preserving and conserving mangrove forests in the study area is essential for maintaining the ecological balance and supporting the livelihoods of local communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aquaculture

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Control and Data Integrity are Important Factors of Data Governance Technology 10

Control and Data Integrity are Important Factors of Data Gov...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Chandra, Yakob Utama Prabowo, Harjanto Gaol, Ford Lumban Purwandari, Betty School of Information Systems Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia BINUS Business School Bina Nusantara University Doctor of Research Management Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350339543

The proliferation of data has become an inevitable consequence of the digital age as individuals across all domains increasingly rely on data for various purposes. Both companies and governments have a significant need for data. However, the sheer volume and complexity of this data, along with the potential risks associated with mishandling it, necessitate the establishment of a framework known as data governance. The primary objective of this research paper is to examine the factors that contribute to the effective implementation and utilization of Data governance. This study employs qualitative methods and adopts a systematic literature review approach to address the research inquiry: 'What are the important factors that determine the utilization of data governance technology?' Through the systematic literature review, a total of 41 key factors were identified from 20 research literatures that have been found to significantly impact the effective implementation of data governance. There are thirteen primary technological factors that hold significance in the realm of data governance. These factors encompass technology, application and use, storage of data, sharing, archiving, and preservation, big data algorithmic systems, data flow, deletion, diversity of data, mechanisms, size of data, control, and data integrity. © 2023 IEEE.

关键词： Digital storage

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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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2024 IEEE International Conference on Data and Software Engineering, ICoDSE 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Kurnianingrum, Dian Ratnapuri, Chyntia Ika Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331506407

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus. © 2024 IEEE.

关键词： Decoding

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Preventing Cyber Crime in Electronic Medical Records Using Encryption Data 1

Preventing Cyber Crime in Electronic Medical Records Using E...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Budiman Mantoro, Teddy Ratmono, Barito Mulyo Persadha, Pratama Dahlian National Intelligence College Post Graduate Program of Medical Intelligence Bogor Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

With the advancement of technology, information systems have become increasingly necessary in almost all areas, including healthcare. One of the technologies used to facilitate this progress is electronic medical records (EMRs). Although EMRs have improved service delivery and patient care in healthcare, they also pose risks to patient data due to cybercrime attacks, which can result in data theft and alteration of medical records. Medical identity theft has become a major issue in healthcare systems around the world, While technology has been beneficial in improving service delivery and patient care in healthcare, it has also introduced numerous risks to patient safety and ultimately impacted patient health. Cybersecurity issues have advanced with technology, thus increasing the rate at which medical identity thefts occur. The healthcare industry lags in curbing cybersecurity issues, and thus, it is vulnerable to many cyberattacks. To address these risks, techniques, and methods are needed to ensure the security of EMR systems against cybercrime attacks. This study used a literature review method and identified eleven articles that met inclusion and exclusion criteria. Through analysis of these articles, several techniques and methods were identified to secure the privacy system in EMRs, such as mIBE-AES, DES, and RSA methods. The literature review revealed that Encryption Data is the safest technique to use, as it is standardized, widely accessible, and cost-effective. Furthermore, mIBE-AES is the safest method for EMRs because it is both secure and efficient for storing medical record documents. Performance evaluations were conducted to test the level of security against cybercrime attacks. © 2023 IEEE.

关键词： Cryptography

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Augmented Reality in Chemistry Learning: A Systematic Literature Review 7

Augmented Reality in Chemistry Learning: A Systematic Litera...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Ariessanti, Hani Dewi Prabowo, Harjanto Gaol, Ford Lumban Meyliana Bina Nusantara University Binus Graduate Program - Information System Jakarta11480 Indonesia Management Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

This study reviews factors affecting AR-based chemistry learning with the BIM model. Although there have been many kinds of research explaining the application of augmented reality (AR) and building information modeling (BIM) in learning, no research has been found that discusses the factors affecting the integration of BIM in AR-based learning. This study adopted the SLR method with the PRISMA protocol by scanning articles written in English or articles written in Indonesian from accredited online databases, and fully accessible by the authors. The articles reviewed were related to AR, BIM, Virtual Reality (VR), and chemistry learning. The predefined strings were used to extract the 156 articles. Only 41 articles met the inclusion criteria that were used as the reference for this study. Six factors were found that affect the AR-based chemistry learning with the BIM model. They are AR work performance, accessibility, user interaction with the system, presentation of material, understanding, and models. This study implies that the factors extracted can be considered as a model in developing learning media to improve chemistry learning. The limitation of this study is the absence of further analysis of which critical factors should be addressed. Researchers recommend further research by reviewing more literature. © 2022 IEEE.

关键词： Augmented reality

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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