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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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bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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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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Model of learning management system based on artificial intelligence in team-based learning framework 6

Model of learning management system based on artificial inte...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Pardamean, Bens Suparyanto, Teddy Cenggoro, Tjeng Wawan Sudigyo, Digdo Anugrahana, Andri Anugraheni, Indri Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Sanata Dharma University Elementary School Teacher Education Yogyakarta Indonesia Satya Wacana Christian University Elementary School Teacher Education Salatiga Indonesia

ISBN: (纸本)9781665449373

The presence of various innovations and technology of Artificial Intelligence (AI) has now begun to be adopted by Indonesia in many aspects including in the industrial sector. The utilization of AI also can offer a new paradigm in learning and teaching strategies. The development of learning strategies continues also performed to obtain the variations of learning that involve student activity. Team-Based Learning (TBL) is one of the teaching strategies that can improve the quality of the learning process and student activity in groups. By starting to integrate digital-based learning resources in the learning and teaching process, the TBL strategy can potentially be an alternative to new learning strategies that are relevant to be applied in the era of the educational revolution 4.0. The main focus of this research is to develop and evaluate the application of AI in a digital-based TBL strategy that will be implemented in the form of personalized learning about student learning styles. This research was developed based on the learning strategy development of the research method. However, this article more focuses on developing the AI application design that will be applied. The application design was developed based on the agile development method, especially SCRUM. The result of this study is an additional feature that was implemented on a Learning Management System (LMS) based on the Modular Object-Oriented Dynamic Learning Environment (MOODLE). © 2021 IEEE.

关键词： Students

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Preprocessing Methods and Tools in Modelling Japanese for Text Classification 4

Preprocessing Methods and Tools in Modelling Japanese for Te...

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4th International Conference on Information Management and Technology, ICIMTech 2019

作者： Rahutomo, Reza Lubis, Febrian Muljo, Hery Harjono Pardamean, Bens Bioinformatics Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Kejepang.co.id Jakarta Indonesia Accounting Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

As a subset of Artificial Intelligence, Natural Language Processing (NLP) is a breakthrough in surpassing language barrier. Japanese language characteristics bring its own challenge in morphological analysis due to the uniqueness of Japanese grammatical system. By the rapid development of NLP tools, many Japanese NLP tools developed with limited ability yet specialized in running certain preprocessing methods. In this paper, the compilation of various methods and newly discovered tools for preprocess Japanese text are delivered to help people decide which Japanese NLP tools needs to be utilized to run some preprocessing methods. All of the Japanese preprocessing methods and tools are collected through literature review. It is concluded that depending on one NLP tool is not recommended since combination of Japanese NLP tools is required to finish Japanese preprocessing phase. © 2019 IEEE.

关键词： Text processing

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Deep Learning for Imbalance data Classification using Class Expert Generative Adversarial Network

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Procedia Computer science 2018年 135卷 60-67页

作者： Fanny Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.

关键词： imbalance data classification deep learning generative adversarial network

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A Fast and Accurate Model of Thoracic Disease Detection by Integrating Attention Mechanism to a Lightweight Convolutional Neural Network

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Procedia Computer science 2021年 179卷 112-118页

作者： Branden Adam Sangeroki Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

The utilization of Deep Learning, especially the Convolutional Neural Network (CNN), is currently the best approach for thoracic disease detection from Chest X-Ray images. However, CNN typically has a slow runtime, hence potentially can introduce a bottleneck in the healthcare that uses the technology. To answer the challenge, we proposed a model that integrates an attention mechanism to a lightweight CNN. The proposed model can run faster than the state-of-the-art thoracic disease detection model while having the second-best performance among the thoracic disease detection models.

关键词： chest x-ray thoracic disease deep learning attention mechanism convolutional neural network

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Feature Pyramid Networks for Crowd Counting

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Procedia Computer science 2019年 157卷 175-182页

作者： Tjeng Wawan Cenggoro Ayu Hidayah Aslamiah Ardian Yunanto Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

In this paper, we propose the use of Feature Pyramid Networks (FPN) for Crowd Counting problem. FPN previously has been used for retinanet, the state-of-the-art model for object detection. By using FPN, our proposed crowd counting model achieved a state-of-the-art performance for UCF CC 50 dataset with MAE 136.4 and MSE 223.6. The proposed model is also achieved a state-of-the-art MSE value of 7.6 for ShanghaiTech Part B dataset. The code can be accessed at https: //***/wawancenggoro/fpncc.

关键词： Crowd Counting Scale-Aware Deep Learning Feature Pyramid Networks

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Design of Water Information Management System in Palm Oil Plantation 1

Design of Water Information Management System in Palm Oil Pl...

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1st International Conference on Computer science and Artificial Intelligence, ICCSAI 2021

作者： Krisdiarto, Andreas Wahyu Julianto, Eddy Wisnubhadra, Irya Suparyanto, Teddy Sudigyo, Digdo Pardamean, Bens Institute of Agriculture STIPER Faculty of Agricultural Technology Yogyakarta55282 Indonesia Atma Jaya Yogyakarta University Faculty of Industrial Engineering Yogyakarta55281 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

The water level on peatlands is a critical factor in the production of oil palm plantations on peatlands because oil palm requires water but should not be inundated. The optimal water depth from the surface should be controlled from 70 to 80 cm by opening or closing the drain gate. Currently, most measurements are made with a piezometer. Then the opening and closing of the sluice gate at the end of the primary channel are done manually. In many cases, the distance between the plantation block and the floodgate is quite far and is limited by inaccessible infrastructure (roads), so opening or closing take a lot of time and money. Currently, a water level measurement system has been designed automatically using a microcontroller. This study aims to develop a design of a drainage management information system in oil palm plantations. The information system includes the water level of peatlands data in oil palm plantations taken from the water level sensor in the drainage system. The system uses the data from the water level sensor to manage the drainage system's sluice gates. This system was developed using the SDLC waterfall method. The final result of this research was a design of a water information management system that can regulates automatic and real-time peatland oil palm plantations water level. © 2021 IEEE.

关键词： Water levels

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Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks

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Procedia Computer science 2024年 245卷 710-719页

作者： Francis Alexander Panarto Tyrone Matthew Phanghegar Vincent Gunardi Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia

Travelers usually check information about the destination before they decide to go. However, sometimes the information is too much to handle and causes confusion . A filtering mechanism is needed to help them make their choices . The solution is to implement a recommendation system; some common techniques that are used for this are Collaborative Filtering and content- based filtering, but both techniques have their flaws. Therefore, we propose a solution to combine those two methods through Graph Neural Network architectures to help fix their flaws . The proposed method will be used for recommending hotels in Indonesia using the HotelRec dataset as the base dataset and hotel features acquired from the TripAdvisor API. We use several GNN architectures, such as GCN, LightGCN, GraphSAGE, and GAT, to implement our approach. Then, we compare it to several baseline models, such as SVD and NeuMF, which are common baselines in recommendation systems. The performance of these models is calculated based on three metrics: HitRate@K, NDCG@K, and Recall@K. Based on the result of this experiment, we found that incorporating hotel features into the learning process of the model, accompanied by user-item interactions, boosted the model's performance by at least 20% in all metrics, especially in datasets with a smaller number of interactions.

关键词： Recommendation Systems Graph Neural Network Hotel Recommendation Collaborative Filtering Content -Based Indonesia

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