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

作者： Isnan, Mahmud Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNetB0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues. © 2023 The Authors. Published by Elsevier B.V.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events 8

Count-Data Mixed Models of Topical Tweets: A Case of Indones...

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

作者： Hidayat, Alam Ahmad Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data. © 2023 The Authors. Published by Elsevier B.V.

关键词： count data disaster surveillance floods mixed models Twitter

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Ensuring Success in Quick Commerce by Evaluating Its Application Development Capability Maturity Using COBIT 2019 8

Ensuring Success in Quick Commerce by Evaluating Its Applica...

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8th International Conference on Information Management and technology, ICIMTech 2023

作者： Heruatmadja, Chandra Hermawan Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Computer Science Department Depok Indonesia

ISBN: (纸本)9798350326093

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level. © 2023 IEEE.

关键词： Mobile commerce

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Identification of Aedes Mosquitoes with Audio Data Using Gammatone Filter

Identification of Aedes Mosquitoes with Audio Data Using Gam...

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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Mariyanto Pardede, Hilman Ferdinandus Nusa Mandiri University Nusa Mandiri University Graduate School in Computer Science Faculty of Information Technology Jakarta Indonesia National Research and Innovation Agency Indonesia Nusa Mandiri University Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Studies show that mosquitos could be identified using their recording during their flights with machine learning. However, differences in environmental conditions and the use of various recording devices can cause significant variations in audio channels making their performances drop. Models created with Machine Learning are usually less robust to audio channel variations. This research aims to increase the robustness of the model in identifying Aedes Aegypti and Aedes Albopictus through audio spectral analysis. The approach used involves audio manipulation techniques such as normalization, channel equalization, and histogram equalization. Modeling is performed using various machine learning architectures, including Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Gated CNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Feature extraction involves ShortTime Fourier Transform (STFT), Filter Bank (FBank), MelFrequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), and Power Normalized Cepstral Coefficients (PNCC). Test results show that the combination of histogram equalization, MLP, and GFCC produces optimal performance. On the Wingbeats dataset, the highest accuracy rate reached 93.82%. Apart from that, this model also shows good performance on the Humbugdb dataset with an accuracy of 83.08% and on the Dryad dataset with an accuracy of 75.86%. These findings provide a valuable contribution in developing a robust model and can be a solid basis for supporting efforts to monitor and control diseases caused by Aedes Aegypti and Aedes Albopictus mosquitoes. © 2024 IEEE.

关键词： Long short-term memory

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Classification of Alzheimer's Disease using Random Oversampling and Albumentations on Convolutional Neural Network 8

Classification of Alzheimer's Disease using Random Oversampl...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Kamardi, Calvin Laksana, I Kadek Perry Bagus Anggreainy, Maria Susan Fitriawati, Nora Yonatan, Michiko Amaya Mangkang, Tesalonika Abigail Eikwine Pengesa, Vianny Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350342604

Alzheimer's Disease (AD) is a brain disorder that causes dementia and affects the memory, cognitive, and behavioral function. Early detection for AD can help to reduce the symptoms and slow down AD progression. Deep learning, particularly Convolutional Neural Network (CNN), a component of artificial intelligence, has demonstrated efficacy in resolving image-related issues and has gained widespread use in the analysis of medical images. CNN works well on large datasets, however, in imbalanced datasets, it can perform poorly by misclassifying minority classes. In this study, the random over-sampling technique is used to address the imbalance in the dataset, while Albumentations is used for image augmentation to prevent overfitting. The results demonstrate that the combination of these techniques leads to improved classification performance compared to traditional approaches. The proposed method has the potential to improve the accuracy of AD diagnosis and contribute to the development of effective treatments for this debilitating condition. © 2023 IEEE.

关键词： Convolutional neural networks

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A Case Study on the Effects of Auditory Cues in Influencing Spatial Memory and Representation for a Person who is Blind 1

A Case Study on the Effects of Auditory Cues in Influencing ...

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1st International Workshop on Brain-computer Interfaces (BCI) for Multimedia Understanding, BCIMM 2024, co-located with ACM Multimedia 2024

作者： Nguyen, Minh Zhu, Howe Sun, Huiyuan Nguyen, Vincent Singh, Avinash Jin, Craig Lin, Chin-Teng School of Computer Science University of Technology Sydney Sydney Australia School of Electrical and Information Engineering The University of Sydney Sydney Australia Graduate School of Health University of Technology Sydney Sydney Australia

ISBN: (纸本)9798400711893

Navigation is a coordinated and goal-oriented movement through the environment, in which vision is an integral part of acquiring spatial information. People with blindness and vision impairment rely on alternative senses to navigate in daily life. Auditory cues are critical in building a spatial representation and effective navigation. Our work investigates the neurophysiological response to explore how auditory cues can shape a person’s spatial representation and memory of an environment. In our experiment, a person who is blind was presented with verbal and non-verbal cues, either at close-reaching distance or globally. Electroencephalography, walking trajectories and tactile drawing data were collected to evaluate spatial representation and memory formed by different auditory cues and the role of theta oscillations in such relationships. Our results indicated a consistent increase in theta power at turning and starting points, a higher cognitive load with verbal strategies, and enhanced path recollection with global strategies. These findings emphasise the role of theta as a key indicator of cognitive workload and spatial memory processing. The preliminary results contribute to a broader understanding of the underlying processing mechanism of auditory information processing to support spatial navigation. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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Lighter Student Engagement Recognition in a Classroom Environment Using Skeletal Keypoints

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第6期52卷 1997-2014页

作者： Tarigan, Gabriel Asael Elwirehardja, Gregorius Natanael Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department Binus Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta1140 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Student retention is crucial for educational institutions, influencing reputation, finances, and ranking metrics. Engagement, reflecting a student’s connection, interest, and effort, plays a vital role in learning, fostering critical thinking, and supporting retention. Recent advancements use students’ poses to predict engagement, providing valuable insights without disrupting the teaching-learning dynamic. The prevailing research trend leans toward employing multi-modal approaches, such as a combination of pose detection with object detection. However, current methods use out-of-date object detection methods and manual dataset creation, which is cumbersome, requiring thousands of manually annotated data. This problem is then addressed by proposing a novel method of student engagement detection, using a combination of You Only Look Once Version 8 Mini (YOLOv8m) and MediaPipe as state-of-the-art alternatives to improve both object detection and human pose estimation. The (YOLOv8m + MediaPipe) method surpasses the baseline (YOLOv4 + OpenPose) with higher accuracy (0.70 vs. 0.41) and lower cross-entropy loss (0.40 vs. 0.60) on the test set, confirmed by a statistically significant paired t-test. It also exhibits a remarkable speed advantage, around 16 times faster than the baseline in pose detection data collection rates. Despite not being designed for it, the proposed method achieves multiple keypoint detection, matching the baseline’s amount. © (2025), (International Association of Engineers). All rights reserved.

关键词： Object detection

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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes 8

Systematic Literature Review on Statistics and Machine Learn...

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

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan 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 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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HCNNet: hybrid convolution neural network for automatic identification of ischaemia in diabetic foot ulcer wounds

HCNNet: hybrid convolution neural network for automatic iden...

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作者： Das, Sujit Kumar Namasudra, Suyel Sangaiah, Arun Kumar Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Computer Science and Engineering National Institute of Technology Agartala Tripura India International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Electrical and Science Engineering Lebanese American University Byblos Lebanon

The diabetic foot ulcer (DFU) is a significant medical complication for diabetic patients, which often leads to lower limb amputation. The manual identification of ischaemia in DFU is laborious, time-consuming, and costly. This study aims to develop an efficient deep learning (DL) method for the early identification of ischaemia in DFU. Therefore, a novel Convolutional Neural Network (CNN) architecture (HCNNet) is proposed by integrating multiple hybridised blocks from inception, residual, and dense modules along with appropriately placed Squeeze-and-Excitation (SE) block and intermediate transition layers. The proposed HCNNet is trained several times using various optimizer and learning rate settings to optimise its performance. It achieves promising Area Under the ROC Curve (AUC) scores of 0.999 for ischaemia identification. The experimental results show that the proposed HCNNet outperforms existing State-Of-The-Art (SOTA) methods. © 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Medical imaging

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IEEE Access 2023年 11卷 11089-11101页

作者： Hsieh, Chi-Lin Chang, Kuei-Chung Chen, Tien-Fu National Yang Ming Chiao Tung University Department of Computer Science Hsinchu30010 Taiwan National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Yunlin64002 Taiwan

With the success of deep learning in recent years, lots of different AI models have been applied to the real world. At the same time, how to train a model with good performance becomes a problem people have to face. One of the most important things is hyperparameter tuning since it determines the setting of training flow. However, most of the conventional hyperparameter algorithms are inefficient, because they usually search from scratch for each new task, and that's why they require large search trials to find a good combination of hyperparameters. In this paper, we present a systematic hyperparameter tuning framework which utilizes prior knowledge with a suggestion algorithm and an adaptive controller to improve its efficiency rather than search from scratch for each task. In this way, our proposed method can achieve a better performance with the same budget. In the experiments, we applied our methods to tens of popular datasets, and the results show that our proposed methods can outperform than other approaches. © 2013 IEEE.

关键词： Budget control

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