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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Rochadiani, Theresia Herlina Arifin, Yulyani Suhartono, Derwin Budiharto, Widodo Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350364101

The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer learning approach has emerged as one of the primary approaches for improving the accuracy and durability of classification systems. This study conducts a comprehensive comparative analysis to determine the effectiveness and performance of this method in environment sound classification. This current investigation focuses on environmental sound classification using VGGish and YAMNet pre-trained models with the ESC-50 and BDLib2 datasets. In the ESC-50 dataset, VGGish improves accuracy to 372.22%, while YAMNet improves accuracy to 383.33% when compared to baseline models. Similarly, in the BDLib2 dataset, accuracy increases significantly to 221.43% with VGGish and 246.43% with YAMNet. Transfer learning exhibits remarkable effectiveness in enhancing model performance, with significant accuracy boosts observed in both datasets. YAMNet, designed specifically for sound classification tasks, surpasses VGGish in improving environmental sound classification performance, potentially due to its architecture's adaptability and diverse training on environmental sounds. © 2024 IEEE.

关键词： Deep learning

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： computer vision

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Ginger infusion increases diet-induced thermogenesis in healthy individuals:A randomized crossover trial

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Food Bioscience 2022年第6期50卷 314-319页

作者： Ana Paula da Costa Soares Natália Cristina de Faria Guilherme Fonseca Graciano James Almada da Silva Vitoria Shievila dos Santos Gonçalves Virginia Del Carmen Trancoso Valenzuela Maria Isabel Toulson Davisson Correia Lucilene Rezende Anastácio Post-Graduate Program in Food Science Pharmacy SchoolUniversidade Federal de Minas GeraisBrazil Surgical Post-Graduate Program Alfa Institute of GastroenterologyHospital das ClínicasUniversidade Federal de Minas GeraisBrazil Pharmacy Department Universidade Federal de SergipeBrazil Fundação Ezequiel Dias Brazil

The purpose of this study is to evaluate the acute effect of ingesting ginger infusion on energy metabolism,appetite responses,and food intake in healthy *** is a clinical(RBR-9ZJGQM),randomized,open,and crossover study with a minimum interval of seven days between *** drinks,ginger infusion(2 g)and water(control)were served with a standard *** has 21 volunteers of both sexes(18–40 years old,18.5–26 kg/m^(2)).Consumption of ginger tea increased on average 95.5±27.4 kcal/day the non-nitrogen energy expenditure after 240 min of ingestion(p=0.002).There was a significant effect of ginger on diet-induced thermogenesis(difference between ginger tea treatment ***:113.0±43.0 kcal/day,p=0.016).However,appetitive responses and prospective food intake did not differ between ginger tea and water intake.

关键词： Zingiber officinale Ginger Indirect calorimetry Energy metabolism

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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Energy Sector Stock Price Prediction Using the CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using the CNN, GRU & LS...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sulistio, Bambang Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks.. © 2023 IEEE.

关键词： Long short-term memory

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Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset 6

Implementation of Dynamic Image for Facial Expression Recogn...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Iswanto, Irene Anindaputri Chowanda, Andry Soeparno, Haryono Budiharto, Widodo Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389692

In recent times, there has been considerable attention directed towards Facial Expression Recognition (FER) due to its extensive utility across diverse domains. However, the universality of facial expressions has been challenged by studies suggesting that cultural backgrounds significantly influence the perception and recognition of emotions. This paper addresses the need for culturally specific datasets in FER tasks, particularly in underrepresented regions like Indonesia. The study introduces dynamic images as an alternative input representation for facial expression recognition tasks, aiming to assess their efficacy using the Indonesian Mixed Emotions Dataset (IMED). Through experimentation using EfficientNet model, the performance of dynamic images is compared with static image and video inputs. Results indicate that dynamic images exhibit promising performance, with an accuracy of 94.28%. These results outperform static image datasets and nearly match the performance of video-based models, which achieved an accuracy of 97.93 %, despite using fewer data. Nonetheless, challenges such as data imbalance and the quality of generated dynamic images persist, suggesting avenues for further research and model refinement. This study provides valuable insights into methodological advancements in FER, particularly in limited dataset conditions, laying the groundwork for future developments in dynamic image-based facial expression recognition algorithms. © 2024 IEEE.

关键词： Dynamic Image EfficientNet Facial Expression Recognition IMED

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Analyzing Company's Share Purchase Value Based on Financial Reports Using Artificial Neural Network (ANN) 9

Analyzing Company's Share Purchase Value Based on Financial ...

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

作者： Shawn, Jeremy Gunawan, Jonathan Hao, Chen Zi Anggreainy, Maria Susan Kurniawan, Afdhal 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

ISBN: (纸本)9798350370126

Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor's results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting. © 2023 IEEE.

关键词： Backpropagation

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest 15

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

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

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

ISBN: (纸本)9798350383829

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. © 2023 IEEE.

关键词： Forecasting

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Analysis of the Cyber Attacks in Indonesia's Health Sector in Industry 4.0 Era 8

Analysis of the Cyber Attacks in Indonesia's Health Sector i...

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8th IEEE International Conference on Computing, Engineering and Design, ICCED 2022

作者： Hidayat, Arif Mantoro, Teddy Susandi, Armi Indonesian State Intelligence College Medical Intelligence Post-Graduate Program Bogor Indonesia Sampoema University Media-Tech Lab Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665453899

At this time the world has undergone considerable changes, where the world has entered the era of the industrial revolution 4.0. With these changes, there are many positive impacts or opportunities that can support human activities in the economic, political, socio-cultural, health, and other fields. And alsothe author tries to analyze about industry 4.0 era for Indonesia's health sector with SWOT Analysis to know about Strenght, Weaknesses, Opportunities, and Threats. However, the challenges of technological advances can pose a threat to countries that are not ready to facethem, one of which is cyber attacks that can threaten data security. Therefore, it is necessary to be prepared in terms of data security by cooperating with various parties involved in it. © 2022 IEEE.

关键词： Industry 4.0

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Detection of dos attacks using naive bayes method based on internet of things (iot) 3

Detection of dos attacks using naive bayes method based on i...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Setiadi, F.F. Kesiman, M.W.A. Aryanto, K.Y.E. Department of Computer Science Graduate Program Universitas Pendidikan Ganesha Singaraja Bali Indonesia

Internet of Things (IoT) is a technology that is currently on a trend. Interconnecting networks on IoT are useful in the automation process, but it has vulnerabilities to network-based disruptions and attacks;such as Denial of Service (DoS). This study aimed at implementing the Naive Bayes algorithm to predict attribute classes using training data-sets from NSLKDD with the KDD99 format and obtained testing-data from the logging process of DoS attacks on IoT-based devices. The advantage of using Naive Bayes is that this method only requires a small amount of training data to determine the estimated parameters needed in the classification process. The results of the conducted research could detect attacks on IoT devices by using the help of snort tools to capture traffic logs. The results from the log were then converted into KDD99 format and processed by the Naive Bayes method. This research uses a training dataset from NSLKDD with KDD99 format which is widely used in various studies and testing data obtained from the IDS log process on the Raspberry Pi 3. The attributes used are 9 attributes namely;service, flag, src_bytes, dst_bytes, srv_serror_rate, same_srv_rate, diff_srv_rate, dst_host_srv_diff_host_rate and dst_host_srv_serror_rate. The results of the research analysis showed an accuracy of 64.02%. These results were smaller than previous results, but some aspects were still different from the actual results because the testing data and training data were taken from two different data-sets, thereby they had different characteristics. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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