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

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Automatic Multiple Choice Question Generation: A Systematic Literature Review 9

Automatic Multiple Choice Question Generation: A Systematic ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Nugroho, Kuncahyo Setyo Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark. © 2024 IEEE.

关键词： Semantics

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Utilization of Augmented Reality Technology in the Campus Environment 4th

Utilization of Augmented Reality Technology in the Campus En...

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4th International Conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Gaol, Ford Lumban Kamil, Mufti Ikhsan Iswardhana, Aria Muhammad Firnandes, Steven Gusda Fahrezi, Fazri Matsuo, Tokuro Hutagalung, Fonny Binus Graduate Program—Doctor of Computer Science Jakarta Indonesia Alam Sutera School of Information System Bina Nusantara University Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9789811974014

Technological advancements are accelerating in the modern day;one such advancement is augmented reality (AR) technology. Naturally, these advancements have an effect on various sectors of life, one of which is education. The education industry will be the most impacted if campuses implement and deploy augmented reality technology. One of these is through the use of augmented reality technology as an application-based learning medium for students in order to motivate them to learn and make it easier for them to improve their academic performance on campus. The issue at hand is the absence of utilization of augmentation reality technology as a learning medium on campus, which, of course, does not capitalize on technical advancements to improve student interest in learning. This study employs a research and development model. The method is divided into four phases: (1) planning;(2) data collecting;(3) analysis, discussion, and development;and (4) documentation. The findings of this study reveal that students enthusiastically support the usage of augmented reality technology as a learning medium in order to boost accomplishment and introduce new experiences through its use. However, it is required to have a system capable of serving as a technological medium, so that the construction of a system capable of serving as a medium for augmented reality may be easily accessed by students, who hope that this system would enable them to better their academic performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms

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International Journal of Advanced computer science and Applications 2024年第3期15卷 902-911页

作者： Antonius, Victor Hugo Fitrianah, Devi BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta11480 Indonesia

In this digital era, the use of e-commerce has expanded and is widely adopted by society. One of the reasons why people use e-commerce platforms is because of their convenience and ease of use. However, the rapid growth of e-commerce has led to a substantial rise in transactions within the platform, involving various business entities. Therefore, it is crucial to perform customer segmentation to group them based on their purchasing behavior. The implementation of data mining techniques, such as clustering, is highly beneficial in this case. Clustering helps process datasets and transform them into useful information. In this study, transaction data obtained from one of the e-commerce stores, i.e. MurahJaya888 and followed by analysis using various clustering methods such as K-means, K-medoids, Fuzzy c-means, and Mini-batch k-means. We also proposed a new model that will become the attributes cluster, namely, RFM + DP (Discount Proportion). The Discount Proportion Rate will provide more insights for customer segmentation as it helps understand purchasing behavior that is more responsive to discount utilization. Implementing these four clustering methods with RFM + DP model resulted in four clusters based on the optimal elbow method. Furthermore, the evaluation and performance metrics for each clustering algorithm indicate that Mini Batch K-Means achieved the highest silhouette score of 0.50. Meanwhile, K-Means obtained the highest CH index value compared to the other algorithms, which was 1056. © (2024), (science and Information Organization). All Rights Reserved.

关键词： Data mining

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest

Drug Demand Prediction Based on Epidemiology Factors Using R...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Gunadi Emmanuel Yulyani Arifin Ilvico Sonata Muhammad Zarlis Doctor of Computer Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Management Department Binus Graduate Program Bina Nusantara University Jakarta Indonesia

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017–2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases.

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Indoor Positioning System Classification with Wi-Fi Signal Data: A Machine Learning Approach

Indoor Positioning System Classification with Wi-Fi Signal D...

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International Conference on Knowledge and Smart Technology (KST)

作者： Ivan Alexander Gede Putra Kusuma Yaya Heryadi Widodo Budiharto Computer Science Departement Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement School of Computer Science - Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Indoor positioning systems (IPS) are critical for enabling accurate navigation, asset tracking, and emergency response in indoor environments where GPS fails. This study evaluates five machine learning algorithms-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree, and Random Forest-using two benchmark datasets, UJIIndoorLoc and UTSIndoorLoc. The results reveal that SVM achieves the highest accuracy, with 81.3 % on UJIIndoorLoc and 92 % on UTSIndoorLoc, followed by MLP with $\mathbf{7 8. 8 \%}$ and 90.9 %, respectively. Random Forest provides stable performance, with 77.6 % and 86.08 %, while KNN reaches 75 % and 89.7 %, performing well in structured environments. Decision Tree shows the lowest accuracy, 71.2 % and 76.54 %, highlighting its limitations with complex data. The UTSIndoorLoc dataset consistently yields higher accuracies, demonstrating its structured signal distribution. These findings underscore SVM and MLP as optimal algorithms for Wi-Fi fingerprinting IPS, offering robust and scalable solutions for indoor localization.

关键词： Support vector machines Accuracy Machine learning algorithms Nearest neighbor methods Fingerprint recognition Decision trees IP networks Random forests Wireless fidelity Indoor positioning systems

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Measurement of Web-Based Merchant Application Portal (MAP) Using Function Point Analysis and Constructive Cost Model II 4

Measurement of Web-Based Merchant Application Portal (MAP) U...

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4th Asia Pacific Conference on Research in Industrial and Systems Engineering: Building Business Resilience to Face the Challenge in Pandemic Era, APCORISE 2021

作者： Santoso, Irvan Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Abdurachman, Edi Soewito, Benfano Cyber Security Program Computer Science Department School of Computer Science Computer Science Program Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Program Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781450390385

The development of technology and the internet is one of the critical factors that must be considered by companies especially those engaged in e-commerce. The web-based application is one of the tools used by e-commerce in making it easier for users to conduct transactions and data processing. The application that developed must be calculated carefully so the effectiveness and efficiency can be appropriately maintained. In this research, the calculation of the size of the software, effort, time, staff, and the total cost needed to work on an application was calculated using the Function Point Analysis (FPA) and Constructive Cost Model II (CoCoMo II) methods. The application that has been analyzed and estimated is Merchant Application Portal (MAP) which is an application designed by one of the companies in Indonesia. The estimation results obtained, scilicet the size of software amounted to 10,02972 KLOC, effort amounted to 48.521 Person Month, time development of 13 months, staff needed as many as four staff, and the estimated cost amounted to IDR 13,828,852.79 or $ 971.54. In addition, this calculation can be used by other companies to find out the resources needed in making a software to be more effective and efficient. © 2021 ACM.

关键词： Portals

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Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

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

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

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Optimized LightGCN for Music Recommendation Satisfaction 6

Optimized LightGCN for Music Recommendation Satisfaction

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6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Hansel, Aaron Christian Adrianus Pradana, Lunardi Suganda, Abba Girsang Nugroho, Ariadi Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

With the large amount of information available on the internet, recommendation tasks have grown to be more crucial than ever. Businesses that store digital media on the internet such as video streaming and music streaming platforms, benefit a lot from recommendation systems. A simple yet powerful recommendation system that can give better recommendation performance is always being sought after. Light Graph Convolution Network (LightGCN) is a simplified version of Graph Convolution Network (GCN) for collaborative filtering in recommendation systems. LightGCN architecture includes only the most essential part of GCN for collaborative filtering that is the neighborhood aggregation, it removes the feature transformation and nonlinear activation because both of them contribute little to no effect to the recommendations. The focus of this research is to optimize LightGCN by tuning the hyperparameters using exhaustive search (grid search). The optimized LightGCN model is able to out-perform LightGCN by more than 140% in music recommendation © 2022 IEEE.

关键词： exhaustive search hyperparameter tuning LightGCN music recommendation Recommendation system

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Integrated Framework for IT Asset Physical Security: A Risk Management Approach Using NIST and ISO Standards

Integrated Framework for IT Asset Physical Security: A Risk ...

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Ricky Alamsyah Yulyani Arifin Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

This study presents a comprehensive approach to IT asset security risk management in physical access control systems, integrating NIST SP 800-116, 800-53, 800-30, ISO 31000, and 27002:2022 standards. Using a mixed-method approach, we conducted interviews, discussion forums, and observations with IT and security professionals in manufacturing and insurance sectors in Indonesia. Our findings reveal seven key factors crucial for physical security of IT assets, including policies, access authorization, and environmental controls. The study proposes a risk assessment model that determines authentication factor levels based on risk severity. Results indicate discrepancies between current practices and recommended security levels, particularly in server rooms and production areas. We provide a set of control recommendations aligned with ISO 27002:2022 to enhance physical security measures. This research contributes to both theory and practice by offering a holistic framework for managing physical security risks in IT environments, particularly relevant for organizations in emerging economies.

关键词： Authorization Adaptation models Technological innovation ISO Standards Prototypes Production Organizations NIST Risk management Servers

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