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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Ariessanti, H.D. Gaol, F.L. Supangkat, S.H. Ranti, B. Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Esa Unggul University Jakarta11510 Indonesia

This study defines the problem of how understanding health protocols during a pandemic is correctly conveyed according to age levels. This problem for the Government to implement gamification by modifying game media through snake and ladder game that can socialize health protocols for pre-school. Measuring the acceptance of a snake and ladder game media can be considered as a learning medium from the Government which will refer to four components (game content, ease of play, appearance, and active player involvement). The purpose of this study was to modify online-based games as a learning media and to measure children's active interaction with parents in practicing health protocols during game play. This study will use a quantitative approach with data techniques through playing together experiments and filling in online research questionnaires from 115 samples as participants (7 Kindergartens). The results show that the snake and ladder digital game can have a significant effect on changes behavior in implementing health protocols. This research can provide a contribution that can be used as a reference for the Government to design a strategy for socialization through gamification. © Published under licence by IOP Publishing Ltd.

关键词： Health

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Development of Detection Kidney Function Stand Alone System and Design based on Oral Ammonia Levels

Development of Detection Kidney Function Stand Alone System ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： William Natasha Angeline Hansen Yosica Mariana Elioenai Sitepu Winda Astuti Melinda Siswanto Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Product Design Program Industrial Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Biotechnology Engineering Department Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The development of detection kidney function system at the first prototype have problem with the dependence on the internet, which make not practical in the development of the system and the initial design was not ergonomic. In this work the development of the second step is done with the improvement of accuracy compared with other methods. In this work the new design of the embedded system is also applied and in term of algorithm, the system applied for woman patient. Three different types of machine learning techniques, namely K-nearest neighbors (KNN), Decision Tree and Random Forest are applied and compared. The AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 90% and 91% for training and testing accuracy, respectively. The design of the embedded system also renews with stylist, simple and practical.

关键词： Training Accuracy Machine learning algorithms Embedded systems Nearest neighbor methods Vectors Decision trees Kidney Random forests Testing

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SIBI Sign Language Recognition Using Convolutional Neural Network Combined with Transfer Learning and non-trainable Parameters

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Procedia computer science 2021年 179卷 72-80页

作者： Suharjito Narada Thiracitta Herman Gunawan Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Sign Language Recognition (SLR) is a complex classification problem to be solved. Every language has their own syntax and grammar. New model combination and dataset is required to developed new SLR system for different syntax and grammar. In this study, we implemented a Convolutional Neural Network (CNN) model which is Inflated 3D model combined with transfer learning method from ImageNet and Kinectic dataset to overcome small dataset problems. There is no public dataset available for SIBI Dataset. Therefore, we collected the dataset by our-self using a mobile phone camera with the following specification. Samsung Galaxy S6 Camera, 16-megapixel Sony Exmore RS IMX240 sensor. The camera is in static position. We used 200 videos as our dataset with 10 words (classes) and 2 signers. We split the dataset into 3 parts (training, validation, testing). After several training and testing with 5 different froze layer combination. The highest validation accuracy was 100% and the highest testing accuracy is 97.50%. The best result was obtained by using a model with the most froze inception module.

关键词： Sign Language Convolutional Neural Network Transfer Learning Inflated 3D Model

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning 7

Impact of CLAHE-based image enhancement for diabetic retinop...

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Hayati, Mira Muchtar, Kahlil Roslidar Maulina, Novi Syamsuddin, Irfan Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-Aided diagnosis of diabetic retinopathy. © 2022 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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Mobile application for paid road use for the reduction of traffic congestion 5

Mobile application for paid road use for the reduction of tr...

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Harliano, Muhammad Rizqiko Afif, Mohammad Ramadhani, Hafiz Iwan, Tanto Harsono Koswara, Rian Warnars, Hareo Leslie Hendrie Spits School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665412728

Technology has evolved a lot in the last few years. E-toll becomes the only payment method available for highway roads, especially in Indonesia. This system of payment is expected to enhance the efficiency of payment from using the previous payment method. However, there are still some weaknesses addressed by the implementation of e-toll. It still takes time because the car's owner should tap the card first, and the sequence time between each person to tap is different. Thus, the queue results still long, and the traffic congestion on the highway road is not fully solved. This paper aims to address a better solution by adding a more efficient option of payment. So, traffic congestion is reduced and leads to better operation in toll gates. This research work has proposed a mobile application and route prediction model based on the Dijkstra Algorithm, where the price will be decided on which type of car will be passed by the gate, and the algorithm predicts the best possible routes based on the number of cars on the highway. Payment is conducted on the application using the balance from the application. This application eliminates time consumed during toll payment at any toll gate. © 2021 IEEE.

关键词： Smart city

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Weather Prediction in Agriculture Yields with Transformer Model 9th

Weather Prediction in Agriculture Yields with Transformer Mo...

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

作者： Arifin, Yulyani Sonata, Ilvico Maryani Gunawan, Elizabeth Paskahlia Computer Science Department Binus Graduate Program Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jl. K.H. Syahdan No.9 Kemanggisan Palmerah Jakarta11480 Indonesia

Agriculture has a lot of relations with SDG from United Nations especially in end hungers and sustainable agriculture. One of factor important in agriculture is weather. Weather prediction is very important in agriculture. Weather greatly influences agricultural yields and the types of crops to be planted. Weather predictions, especially temperature and humidity, can be done using time series data analysis. Current developments in deep learning enable more accurate predictions of temperature and humidity based on time series dataset. Some deep learning models can be used to predict data based on training using time series data. The deep programming models used include Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The Transformer deep learning model, which was first introduced in 2017, is a sequence-to-sequence model which is very suitable for predicting time series data. This paper will explain temperature and humidity prediction using the Transformer deep learning model. Experimental results show that the Transformer model can predict temperature and humidity based on training results using a time series dataset. The accuracy obtained by the Transformer deep learning model is better compared to the LSTM deep learning model as indicated by lower MAE, MSE and RMSE values compared to the LSTM model. © 2024 The Authors.

关键词： Prediction models

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Development of Portable Temperature and Air Quality Detector for Preventing Covid-19 1

Development of Portable Temperature and Air Quality Detector...

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

作者： Budiharto, Widodo Irwansyah, Edy Dewanti, Retno Santoso Gunawan, Alexander Agung Widhyatmoko, Danu Sembodo, Jarot Soeroso Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Business School Management Department Jakarta Indonesia Bina Nusantara University School of Design Dept. of Visual Communication Design Jakarta Indonesia Bina Nusantara University Master of Information System Program BINUS Graduate Program Jakarta Indonesia

ISBN: (纸本)9781728133331

Due to Covid-19, body temperature measurement is mandatory and become an important consideration in determining whether an individual is healthy or not. This paper presents the development of portable temperature and air quality detector to prevent suspect of Covid-19 with the main symptom is the body of temperature above 38° Celsius. We propose an algorithm and architecture used for temperature detector with maximum distance 80cm and CO2 and Volatile Organic Compounds (VOC) measurement are as indicator of good air quality. Based on experiment, we can detect temperature accurately until 0.3° Celsius using digital temperature sensor MLX90614 comparing with commercial one, furthermore the system able to give information about the quality of air, allowing/not allowing someone to enter a room with accuracy 92.5%. © 2021 IEEE.

关键词： Air quality

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective

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Procedia computer science 2024年 234卷 1175-1182页

作者： Marcel Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey.

关键词： transformational leadership digital transformation digital adoption conceptual framework

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Revalidating the Encoder-Decoder Depths and Activation Function to Find Optimum Vanilla Transformer Model

Revalidating the Encoder-Decoder Depths and Activation Funct...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Yaya Heryadi Bambang Dwi Wijanarko Dina Fitria Murad Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Graduate Program– Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Thailand

The transformer model has become a state-of-the-art model in Natural Language Processing. The initial transformer model, known as the vanilla transformer model, is designed to improve some prominent models in sequence modeling and transduction problems such as language modeling and machine translation. The initial transformer model has 6 stacks of identical encoder-decoder layers with an attention mechanism whose aim is to push limitations of common recurrent language models and encoder-decoder architectures. Its outstanding performance has inspired many researchers to extend the architecture to improve its performance and computation efficiency. Despite many extensions to the vanilla transformer, there is no clear explanation of the encoder-decoder set out depth in the vanilla transformer model. This paper presents exploration results on the effect of combination encoder-decoder layer depth and activation function in the feed-forward layer of the vanilla transformer model on its performance. The model is tested to address a downstream task: text translation from Bahasa Indonesia to the Sundanese language. Although the value difference is not significantly large, the empirical results show that the combination of depth = 2 with Sigmoid, Tanh, and ReLU activation function; and d = 6 with ReLU activation shows the highest average training accuracy. Interestingly, d = 6 and ReLU show the lowest average training and validation loss. However, statistically, there is no significant difference between depth and activation functions.

关键词： Training computer science Computational modeling computer architecture Transformers Computational efficiency Machine translation

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