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

作者： Elwirehardja, Gregorius Natanael Dominic, Nicholas Pardamean, Bens BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Research Center Bina Nusantara University Bioinformatics and Data Science Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

In the field of bioinformatics, the protein Post-Translational Modification (PTM) site prediction has been widely studied and Web Information Systems (WIS) has been deployed by researchers for this task. Through a literature review and benchmarking process, we identified the requirements which included quick predictions, efficient memory usage, and input validations. However, no detailed designs have been proposed so far, which may have contributed to some requirements not being implemented in some of the websites. Therefore, we propose a detailed WIS conceptual design, which can be used for predicting the sites of multiple PTM types, equipped with a validation algorithm and compared the usage of various string searching algorithms as well as file storage formats. Experiment results showed that the linear search algorithm is the fastest for this task and storing the protein data in npz format when performing multi-PTMs site prediction can assist in reducing memory usage. The proposed design can be implemented into user-friendly web tools that are both efficient in speed and memory usage in future studies. © 2022 IEEE.

关键词： Forecasting

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White rice stem borer pest detection system using image-based convolution neural network

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Procedia computer science 2024年 245卷 518-527页

作者： Akhmad Saufi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480 Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480

Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the required level for precise pest control, a huge portion of the pest population analysis process is still conducted manually. As a solution to this issue, the development of a White Rice Stem Borer pest detection system has been conducted by applying Convolutional Neural Network (CNN) technology to calculate the pest population count at the research location. This system has been specifically designed to detect the White Rice Stem Borer using available traps. The method involves training data from a direct dataset obtained from the field, categorized into two positive and negative classes of the White Stem Borer pests. Six models have been trained from this dataset, utilizing two different architectures. Out of the six trained models, four showed potential overfitting, one exhibited underfitting, and one model demonstrated optimal results. The highest accuracy in image detection achieved by the most optimal CNN model was 97.35%, with a training accuracy of 98.54%. This best-performing model utilized an architecture with three Convolution layers, 50 Epochs, and an automatic data split with an 80:20 training-validation data ratio. From the research findings, it is concluded that this study can assist in automatically analyzing the quantity of White Stem Borer pests in a specific area without directly counting the number of pests from existing traps. However, the study still encounters a limitation—the detection process still requires substantial server resources and cannot be directly processed on the Raspberry PI device installed in the trap. Consequently, the detection relies on transmitting image data from the field device to the server before the detection process can occur.

关键词： White Rice Stem Borer Stem Borer White Stem Borer Detection Pest Detection Convolutional Neural Network (CNN)

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Development of Sundanese Stemmer Based on Morphophonemics

Development of Sundanese Stemmer Based on Morphophonemics

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International Conference on ICT For Smart Society (ICISS)

作者： Aries Maesya Yulyani Arifin Amalia Zahra Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Binus Nusantara University Jakarta Indonesia

The Stemming Process is used to get the base word by removing the word affixes. The purpose of this study is to improve the algorithm in previous research by incorporating the Stemming Process into a morphophonemic approach for language processing based on rules (Rule Based) which is named AM Sundanese Stemmer (AMS). The AM Sundanese stemmer algorithm will utilize morpheme information to perform stemming on Sundanese words and analyze the phonemes in a word to identify the morphemes therein contained. The experimental process was carried out with 11,885 existing data and a sample of 546 words were taken to be stemmed. The test was carried out by comparing the algorithms of Nazief and Adriani, UG 18 and AMS. The test results showed that the AMS Algorithm was better than the Nazief Andriani Algorithm and the UG18 Algorithm with a high F-1 score of 87.52% and the comparison of the execution time of the AMS Stemmer Algorithm was 0.041403 seconds faster than the Nazief Andriani and UG 18 Algorithms.

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BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text

BERT and ULMFiT Ensemble for Personality Prediction from Ind...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Halimawan, Noptovius Suhartono, Derwin Gema, Aryo Pradipta Yunanda, Rezki Binus Graduate Program-Master Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Epigene Labs Paris France

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT © 2022 IEEE.

关键词： Classification (of information)

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Leveraging BERT with Extractive Summarization for Depression Detection on Social Media 23

Leveraging BERT with Extractive Summarization for Depression...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： William, David Achmad, Said Suhartono, Derwin Gema, Aryo Pradipta Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Epigene Labs United States

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

In our current time, the well-being of a person is not only determined by the physical health, but also by their mental health. A lot of focus and effort have been spent into raising the awareness of this issue. One such effort comes from the field of computer science by utilizing data from social media to provide additional information in detecting these mental disorders. In this research, authors proposed the Bidirectional Encoder Representations from Transformers (BERT) with extractive summarization to preprocess data obtained from popular social media platform such as Reddit and Twitter. The proposed method evaluated by compare it to several other methods in text classification to Figure out which method is best classifying whether someone is depressed or not. This research also contributes to evaluate whether applying extractive summarization to a document prior to feeding the data into BERT will contribute to an increase in performance when compared to the current method of data preprocessing, by measuring the accuracy, precision, recall and F1-score of each model. The evaluation result found that our proposed method by applying extractive summarization to reduce the sequence length achieved a better result compared to BERT base model. © 2022 IEEE.

关键词： Text processing

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Challenges and Recommendations for Improving Logistic Skills Through Simulation-Based Learning Technology

Challenges and Recommendations for Improving Logistic Skills...

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Tamsir Hasudungan Sirait Meyliana Yulyani Arifin Budi Yulianto Binus Graduate Program Doctor of Computer Scienceept Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Simulation-based learning is one of the most powerful tools to support the educational process. This learning offers an opportunity to learn based on experiences that are very similar to real-world environments. In the context of logistics, which can be considered a complex system, significant efforts are required to enhance students' understanding and practical skills. For that reason, simulations are used so that benefits such as skills and knowledge in the field of logistics can be achieved. However, there are currently several challenges in developing simulation-based learning in higher education. These challenges include the need for simulations that are appropriate for the current learning context, where the students are generally from Generation Z, and how to improve their learning quality in the era of Industry 4.0. To address these challenges, more suitable simulations need to be developed, and higher education institutions must carefully examine how to develop them, including identifying the essential components in the development of simulations for logistics learning. In this research, a systematic literature review using the PRISMA method and also creating a conceptual model of simulation that better meets the current needs of logistics education will be proposed. The results of this research can be used as a basis for developing updated simulations to better prepare students for the challenges of Industry 4.0.

关键词： Analytical models Computational modeling Education Prototypes Data models Fourth Industrial Revolution Proposals Logistics Systematic literature review Investment

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Automatic Speech Recognition in Indonesian Using the Transformer Model

Automatic Speech Recognition in Indonesian Using the Transfo...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Ilvico Sonata Computer Science Department Binus Graduate Program Computer Sciiene Bina Nusantara University Jakarta Indonesia

Automatic Speech Recognition (ASR) is useful for converting speech into text. ASR is needed to display automatic subtitles on movies or when conducting video conferencing. The use of deep learning in ASR applications is currently still dominated by Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). Several previous studies used the Transformer deep learning model in building the ASR model. The accuracy results obtained are better than the LSTM or RNN models. However, ASR research using the Transformer model is still very limited. This paper will discuss the use of the Transformer model for ASR in Indonesian. The dataset used is the Indonesian language private speech dataset. The experimental results show that the Word Error Rate (WER) and Character Error Rate (CER) produced by the proposed model using the Indonesian language primary dataset are almost the same as using the English language public dataset where the resulting WER and CER are 27.34 and 7.96 for Indonesian and 25.28 and 6.02 for English.

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A Comparative Analysis of User Experience and Educational Outcomes in Math Memory Games

A Comparative Analysis of User Experience and Educational Ou...

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International Conference on computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Nicholas Martin Raffel Nathanael Doni Jeriyanto Eric Savero Hermawan Janshen Fung Francisco Maruli Panggabean Computer Science Department Game Application & Technology Program School of Computer Science Bina Nusantara University Jakart Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

This research explores multiple varieties of effective and child-friendly learning approaches for mathematics and memorization among young children in today's increasingly digital age considering their specific developmental needs. This type of approach includes in-person teaching, engaging online videos, interactive textbooks, and innovative educational games. This research employs a detailed comparative analysis, considering both ‘User Experience’ and measurable learning outcome factors, and spans a range of age groups from preschoolers to elementary school children. Through highlighting the strengths and weaknesses of each method, this research aims to inspire the adoption of efficient, child-centric, and novel teaching techniques by educators and institutions, thereby advancing the quality of education in Indonesia. The results show that using serious game in education presents high usefulness score alongside positive user experience, suggesting its viability for educational purposes.

关键词： Measurement Video games Education Games Educational technology Information age User experience Mathematics Usability Systematic literature review

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Light Wasserstein Generative Adversarial Network with Gradient Penalty (LWGAN-GP) for Batik Nitik Synthetic

Light Wasserstein Generative Adversarial Network with Gradie...

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

作者： Almuzhidul Mujhid Antoni Wibowo Ricky Reynardo Siswanto Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The development of research in the field of image generation can now be one of the tools to introduce a country's artistic culture. Indonesia is known as one of the countries with cultural diversity. Batik is one of Indonesia's most iconic cultural arts. GAN studies on batik have produced good images but still have a long training process. Several GAN techniques have been developed to create synthetic batik, one of the pioneering ones is BATIK-GAN which proposes a double flow model. Several GAN techniques have been developed to create good images, one of the best known and most widely used as a model is DCGAN due to its lightweight training and ability to produce good synthetic images. In this research we propose the Light Wasserstein Generative Adversarial Network model with Gradient penalty (LWGAN-GP) to be able to generate batik motif images. This model is proposed to produce a model that can perform a faster training process but can still produce good images. The experimental results of LWGAN-GP compared with DCGAN show that the computation time of DCGAN is faster than that of LWGAN-GP but LWGAN-GP is superior to the variety of images produced and is able to remain stable (without collapse mode).

关键词： Training Seminars Art Image synthesis Computational modeling Generative adversarial networks Data models Cultural differences Intelligent systems Information technology

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Indonesian Automatic Speech Recognition with XLSR-53

arXiv

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arXiv 2023年

作者： Arisaputra, Panji Zahra, Amalia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using the XLSR-53 pre-trained model, the XLSR stands for cross-lingual speech representations. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve a competitive Word Error Rate (WER). The total amount of data used in this study is 24 hours, 18 minutes, and 1 second: (1) TITML-IDN 14 hours and 31 minutes;(2) Magic Data 3 hours and 33 minutes;and (3) Common Voice 6 hours, 14 minutes, and 1 second. With a WER of 20%, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 8% using a language model, resulted in WER from 20% to 12%. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data. © 2023, CC BY-SA.

关键词： Statistical tests

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