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Informatica (Slovenia) 2023年第4期47卷 537-544页

作者： Tsani, Eggi Farkhan Suhartono, Derwin Computer Science Department Binus Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Social media growth was fast because many people used it to express their feelings, share information, and interact with others. With the growth of social media, many researchers are interested in using social media data to conduct research about personality identification. The identification result can be used as a parameter to screen candidate attitudes in the company's recruitment process. Some approaches were used for research about personality;one of the most popular is the Big Five Personality Model. In this research, an ensemble model between BERT and RoBERTa was introduced for personality prediction from the Twitter and Youtube datasets. The data augmentation method also introduces to handling the imbalance class for each dataset. Pre-trained model BERT and RoBERTa was used as the feature extraction method and modeling process. To predict each trait in the Big Five Personality, the voting ensemble from BERT and RoBERTa achieved an average f1 score 0,730 for Twitter dataset and 0,741 for Youtube dataset. Using the proposed model, we conclude that data augmentation can increase average performance compared to the model without data augmentation process. © 2023 Slovene Society Informatika. All rights reserved.

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Rice Commodity Supply Chain Issues in Indonesia: Exploring Blockchain Technology as an Alternative Solution

Rice Commodity Supply Chain Issues in Indonesia: Exploring B...

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Technology Innovation Management and Engineering science International Conference (TIMES-iCON)

作者： Rohmat Taufiq Harco Leslie Hendric Spits Warnars Haryono Soeparno Tanty Oktavia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program Master of IS Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350362602

ISBN: (纸本)9798350362619

Agriculture has long been recognized as pivotal to economic development, offering avenues to alleviate poverty, foster prosperity, and sustainably meet the food demands of a burgeoning global population projected to reach 9.7 billion by 2050. However, persistent inefficiencies within agricultural supply chains, particularly in sectors like rice production, pose formidable challenges. Addressing these inefficiencies is imperative to avert a surge in hunger and malnutrition. Failure to do so could condemn 815 million people to hunger and leave 3 out of 10 individuals malnourished. Thus, investigating solutions to enhance the resilience of rice supply chains is paramount. This research endeavors to identify and analyze challenges inherent in tracing the rice supply chain, offering a conceptual model proposal as a potential remedy. Employing a methodology centered on scholarly discourse and extensive literature review, our study reveals various impediments facing rice commodity supply chains in Indonesia. Furthermore, this study leverages blockchain technology, specifically the Sawtooth Hyperledger platform, to streamline the tracing process for rice commodities and rationalize the supply chain by reducing intermediary actors. From the proposed model, there is great hope that it can provide implications for the rice supply chain to run more efficiently.

关键词： Economics Supply chains Sociology Production Innovation management Blockchains Proposals

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Automatic question generation for bahasa indonesia examination using copynet

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Procedia computer science 2024年 245卷 953-962页

作者： Matthew Martianus Henry Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

In educational institutions, an educator is responsible for assessing the student's knowledge grasp through examination. Creating exam questions, even the low-level factoid questions, is time-consuming, especially for inexperienced educators. Therefore, this study aims to create a sequence-to-sequence model using CopyNet by exploiting its copying mechanism advantage to automatically generate Bahasa Indonesia factoid questions to ease the educator's burden. Indonesian records in the TyDi QA dataset are used as the model input. GRU and Bi-GRU are employed as the CopyNet encoder, while LSTM is used as the CopyNet decoder. The model that utilizes GRU as the encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.28, 0.19, 0.14, 0.1, and 0.32, respectively. Bi-GRU utilization as the model encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.26, 0.17, 0.12, 0.09, and 0.30, respectively. Models using either encoder still achieve low scores. However, compared with the previous work, the result is still on par regarding the BLEU score. Further examination found that the generated questions do not adhere to semantic and syntactical correctness. Adding more records to the dataset and utilizing a more advanced architecture like CopyBERT are encouraged to improve the model performance in future work. Despite the result, this study has shown that CopyNet, primarily designed for text summarization or single-turn dialogue, can be tailored for factoid question generation.

关键词： Automatic question generation Bahasa Indonesia Copying mechanism Natural language generation Sequence-to-sequence

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Hybrid approach sentiment analysis using Transformer-LSTM in the Indonesian language

Hybrid approach sentiment analysis using Transformer-LSTM in...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Angel Karentia Ferdinand Winaya Derwin Suhartono Computer Science Department BINUS Graduate Program – Master of Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Sentiment analysis is widely used as a tool to find valuable insight from texts without explicitly expressed. Lots of techniques have already been used to get it but there still have shortcomings in data source or the model strategy itself. Indonesian language approximately has 199 million speakers across the world where 44 million speakers natively. Even with that great number, the resources for the Indonesian language's natural language processing are still limited, and hard to find the perfect way to define sentiment analysis in the Indonesian language. The state-of-the-art sentiment analysis method uses LSTM on a small corpus while the best in town is Transformer where it's easy to transfer learning from the Transformer pre-trained model into specific tasks. From this potential, combining the Transformer pre-trained model with LSTM can be an innovative strategy. This research compared the hybrid model of Transformer-LSTM build using three Indonesian languages’ Transformer-based pre-trained model on sentiment analysis task which can surpass the Transformer model with the highest increased 2.40% accuracy and 3.02% on F1 score.

关键词： Seminars Sentiment analysis Analytical models Accuracy Law Soft sensors Transfer learning Medical services Transformers Long short term memory

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Hyperparameter Tuning of U-Net for Active Fire Segmentation in Peatlands of Indonesia Using Landsat 8 Data

Hyperparameter Tuning of U-Net for Active Fire Segmentation ...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Irsyad Kharisma Sani Muhamad Isa Computer Science Binus Graduate Program Universitas Bina Nusantara Jakarta Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This study focuses on fire segmentation in peatland areas using the U-Net model, involving hyperparameter tuning combined with data masking developed using a modified ToPeCAl algorithm. The data masking process utilizes TIRS-1 band, band 7, and band 6, while the training data consists of composite images from bands 7, 6, and 4 (764) and 7, 6, and 2 (762) for comparison. Input data includes image patches sized 128×128, 256×256, and 512×512, derived from the 764 and 762 composites, respectively. The study reveals that the use of the leakyReL U activation function, a dilation rate strategy, a learning rate schedule, 50% image patch overlap, and a patch size of 128×128, improves the model's performance for fire segmentation compared to the baseline model. The final evaluation metrics demonstrate Precision, Recall, F1 Score, and IoU values of 88.85%, 89.70%, 89.08%, and 77.36%, respectively.

关键词： Measurement Image segmentation Schedules Training data Landsat Data models Informatics Tuning

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A review: Data pre-processing techniques used for diabetes prediction

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Procedia computer science 2024年 245卷 667-676页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cmputer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification.

关键词： Diabetes Classification Data Pre-Processing Machine Learning Imbalanced Dataset

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The Impact of Information System and Technology of Courier Service During Pandemic 7th

The Impact of Information System and Technology of Courier S...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Siahaan, Andre Timiko Pinajung, Bagas Rizkyka Kennidy Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutra Tangerang13960 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819951659

Courier services are a means of transport that can be used to deliver orders to customers or claim orders from them, and customers can use technology from the courier service like track order to track their order. The use of courier service nowadays is common that every day, people use courier service as their daily means of transport for delivering goods. However, in situations like pandemic, the courier services need to put more effort into their services, so that customers can use courier services with trust. The courier service must be more careful to deliver customer orders or send orders to customers. For this research methodology, the data collection technique using Google Forms and collecting data directly from people about their opinion toward the research have been employed. The result of the data collecting technique is depicted as the usage of the courier service during COVID-19 pandemic starting from March 2020. The results vary every month because not everyone uses the courier service every day. Moreover, the conclusion on why the months starting from March to June do not show high usage of courier service is that the pandemic has not been that worse than the later months, and so, people were going out to deliver or to do activities like shopping or buying groceries on their own. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information systems

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Factors Affecting the Willingness to Share Data in Mobile Health (mHealth) Apps in Indonesia 25

Factors Affecting the Willingness to Share Data in Mobile He...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Rahayu, Anita Adi, Puput Dani Prasetyo Puspitasari, Chasandra Persada, Satria Fadil Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Surabaya60132 Indonesia Research Center for Telecommunication Bandung Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350378573

This study investigates the factors influencing user willingness to share data in mobile health (m-health) applications in Indonesia, integrating the Technology Acceptance Model, Theory of Planned Behavior, and Privacy Calculus Theory through Structural Equation Modeling (SEM). Data were collected via an online questionnaire from 103 Indonesian active users of m-health platforms, aged 15-55, focusing on applications that provide online consultations and health-related information. The SEM analysis, validated by indices including the Goodness of Fit Index and Root Mean Square Error of Approximation, reveals a strong model fit. The findings highlight that perceived benefits, such as improved health outcomes and personalized advice, are the primary drivers for users' willingness to share data, significantly outweighing concerns over privacy risks. Perceived usefulness and trust in the application also significantly affect data-sharing behavior, while ease of use and social influences have a moderate to minor influence, respectively. These insights are critical for application developers and policymakers in enhancing app utility and user engagement while addressing privacy concerns. The study's limitations include its demographic scope and reliance on self-administered questionnaires, pointing to directions for future research to broaden demographic representation and deepen qualitative insights. © 2024 IEEE.

关键词： data sharing mHealth privacy concerns SEM analysis structural equation modeling

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Comparison of Spelling Error Correction Algorithms for the Indonesian Language

Comparison of Spelling Error Correction Algorithms for the I...

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Information and Education Technology (ICIET), International Conference on

作者： Yanfi Yanfi Reina Setiawan Haryono Soeparno Widodo Budiharto BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Edit distance as a string measurement metric is often used to help detect misspellings in languages. This paper aims to compare two string spelling error correction algorithms for the Indonesian language. The N-gram, Jaro-Winkler distance, and Levenshtein distance algorithms are used to determine whether they can accurately correct typological errors in the Indonesian language. Moreover, this study utilized KNIME tools to process the data from beginning to end. The data was retrieved from news in Indonesia. After the experiment on N from 1 to 12, the results obtained for the comparative analysis proved that Jaro-Winkler distance performed better than Levenshtein distance for comparing smaller strings like words and names. However, Levenshtein distance performs as well as Jaro-Winkler distance started from four strings. Finally, both Jaro-Winkler distance and Levenshtein distance algorithm got the best performance accuracy for eight strings with an accuracy of 99.52 percent. The result of this study is also presented that both algorithms can support word error correction for the Indonesian language.

关键词： Measurement Education Error correction

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Enhancing cybersecurity resilience through advanced red-teaming exercises and MITRE ATT&CK framework integration: A paradigm shift in cybersecurity assessment

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditya Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

As cybersecurity threats evolve alarmingly, conventional defense strategies are becoming increasingly ineffective. In response to this urgent challenge, our study presents a transformative approach to red-teaming exercises by integrating the MITRE ATT&CK framework. This innovative integration leverages real-world attacker tactics and behaviors to create highly realistic scenarios that rigorously test defenses and uncover previously unidentified vulnerabilities. Our comprehensive evaluation demonstrates a significant enhancement in the realism and effectiveness of red-teaming, leading to improved vulnerability identification and the generation of actionable insights for proactive remediation. This study uniquely contributes by providing a structured, data-driven methodology that aligns current defenses with emerging threat tactics and promotes continuous improvement in cybersecurity resilience. Such advancements are essential for organizations seeking to stay ahead of attackers and maintain robust cyber defenses in dynamic threat environments. The significance of this study lies in offering a structured approach to strengthen cybersecurity resilience against evolving threats and providing a model for continual defense enhancement. Future research should investigate the impact of this integration on long-term cybersecurity strategies. © 2024 The Authors

关键词： Continuous defense improvement Cyber resilience Cybersecurity MITRE ATT&CK framework Red-teaming exercises

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