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11th International Conference on Information and Communication Technology, ICoICT 2023

作者： Abrari, Muhammad Giriarda Hindersah, Hilwadi Rusmin, Pranoto Hidaya Bandung Institute of Technology Master Program in Electrical Engineering Bandung Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350321982

Robots, in order to understand human commands, could be possible if there is an interaction between them. Possible interaction including the Natural Language, that humans have used to communicate with each other. The method used in this research for robots to understand Natural Language is Mental Image Directed Semantic Theory (MIDST) which uses unique Lmd formula as a basis for computing the Natural Language Understanding (NLU) processes. Equipped with support modules such as Speech to Text, Text to Speech, and Synonym Search Algorithm the robot successfully to receive, understand, and issue responses according to the Natural Language expressions given in Indonesian Language. The movement of the robot is also simulated in a two-dimensional plane to demonstrate its ability to serve humans. © 2023 IEEE.

关键词： Semantics

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Low-Power Current-Starved Digitally-Controlled Oscillator for Phase-Locked Loop in Battery- Powered Devices 8

Low-Power Current-Starved Digitally-Controlled Oscillator fo...

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8th International Conference on Electrical engineering and informatics, ICEEI 2021

作者： Madya Putra, Tengku Ahmad Adiono, Trio Master's Program in Electrical Engineering Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781665405560

The demand for IoT SoCs with low power and high precision internal oscillators is greatly increasing. This paper presents a design of digitally controlled oscillator which has low power consumption suitable for use in phase-locked loop inside battery-powered devices. The design incorporates current-starved logic buffers in ring configuration to produce oscillating logic signal as clock source. The circuit has been implemented and fabricated in 180-nm CMOS technology. The measured fabricated chip achieves frequency range from 1.1 MHz to 200 MHz. The power consumption is very low that only consumes 872 μW at maximum frequency and 59.4 μW at minimum frequency. © 2021 IEEE.

关键词： Phase locked loops

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Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory 10

Sentiment Analysis of Cooking Oil Price Policy in Indonesia ...

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10th International Conference on Advanced informatics: Concept, Theory and Application, ICAICTA 2023

作者： Sugiarto, Dedy Utami, Ema Yaqin, Ainul Raharjo, Suwanto Universitas Trisakti Faculty of Industrial Technology DKI Jakarta Indonesia Universitas Amikom Yogyakarta Master Program of Informatics Engineering Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350329919

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as 'mafia', 'mahal', 'langka', 'tangkap', 'bongkar', and 'mendag' could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions. © 2023 IEEE.

关键词： Sentiment analysis

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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model 8

Exploring the Impact of Word Embedding Dimensions on Depress...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Lestandy, Merinda Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach. © 2023 The Authors. Published by Elsevier B.V.

关键词： BiLSTM Depression Social Media Word Embedding

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Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis

arXiv

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arXiv 2023年

作者： Putra, Danendra Athallariq Harya Muharram, Arief Purnama Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Sentiment analysis using big data from YouTube videos metadata can be conducted to analyze public opinions on various political figures who represent political parties. This is possible because YouTube has become one of the platforms for people to express themselves, including their opinions on various political figures. The resulting sentiment analysis can be useful for political executives to gain an understanding of public sentiment and develop appropriate and effective political strategies. This study aimed to build a sentiment analysis system leveraging YouTube videos metadata. The sentiment analysis system was built using Apache Kafka, Apache PySpark, and Hadoop for big data handling;TensorFlow for deep learning handling;and FastAPI for deployment on the server. The YouTube videos metadata used in this study is the video description. The sentiment analysis model was built using LSTM algorithm and produces two types of sentiments: positive and negative sentiments. The sentiment analysis results are then visualized in the form a simple web-based dashboard. © 2023, CC BY.

关键词： Sentiment analysis

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

TechRxiv

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TechRxiv 2024年

作者： Muharram, Arief Purnama Sajid, Fahmi Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XGBoost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F-1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies. © 2024, CC BY.

关键词： Self-supervised learning

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Service Oriented Design for Indonesian E-Government System Using SOA 6

Service Oriented Design for Indonesian E-Government System U...

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Joint Conference of the 6th Annual Conference of Industrial and System engineering 2019, ACISE 2019 and 1st International Conference on Risk Management as an Interdisciplinary Approach 2019, ICRMIA 2019

作者： Fajar, A.N. Shofi, I.M. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering UIN Syarif Hidayatullah Indonesia

The objective of this study is to create a services design of E-Government system that can help Indonesian government to resolve some problems such as diversity platform and application. It causes the island applications that can make decision support processes is not well happen. In this study, we use literature review as an approach to discuss the phenomenon. It is performed by identifying, evaluating, and combining information from several other studies which relevant with this study's research questions. We use services oriented analysis and design approach to discussing the phenomenon. The ability of Service-oriented Architecture to connect multiple services between diversity systems and it can help the enterprise application travelling in Indonesian government. The case in this study is travelling system in Indonesia Government. The results in this study are the conceptual services for Travelling System using SOA approach and ESB Technology. It consists of several services and business services which is can be integrated among of them. © Published under licence by IOP Publishing Ltd.

关键词： Information services

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Supervised Machine Learning Approach for Predicting Cardiovascular Complications Risk in Patients with Diabetes Mellitus

Supervised Machine Learning Approach for Predicting Cardiova...

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International Conference on Electrical engineering and informatics, ICEEI

作者： Arief Purnama Muharram Fahmi Sajid Master Program of Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

Diabetes mellitus, particularly type-2 diabetes, remains a prevalent health issue, raising concerns due to its associated risk of complications. Among these, cardiovascular complications pose a significant threat, exhibiting high morbidity and mortality rates. Health screening plays a pivotal role in stratifying the risk levels of diabetes patients, facilitating proactive measures to prevent the progression of complications. As such, the primary objective of this study is to develop a predictive model system for assessing cardiovascular risk in diabetes patients. Our study used the Cardiovascular Disease dataset and conducts experiments with various supervised machine learning algorithms, such as Naive Bayes, decision tree, random forest, AdaBoost, and XG- Boost. The results reveal that ensemble learning algorithms based on boosting, particularly AdaBoost and XGBoost, outperform other supervised machine learning methods. However, even with the best performance achieved using the dataset, the accuracy stands at 0.71, and the F -1 score is 0.69, which is still acceptable for screening purposes. Although these results provide valuable insights, indicating individuals at higher risk for cardiovascular complications in diabetes, further improvements are needed to enhance early prevention strategies.

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Quantitative Assessment of Gait in Spinocerebellar Ataxia Using Deep Learning 4

Quantitative Assessment of Gait in Spinocerebellar Ataxia Us...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Lai, Ming-Hui Liu, Don-Gey Cheng, Ching-Hwa Lai, Chung-Yu Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Feng Chia University Department of Electronic Engineering Taichung Taiwan Hungkuang University Department of Medical Equipment Development and Application Taichung Taiwan

ISBN: (纸本)9798350360721

Spinocerebellar ataxia is a neurological disorder characterized by impaired neural coordination, leading to unstable gait, coordination difficulties, and other motor abnormalities. This disease significantly impacts patients' daily lives, making it challenging for them to maintain balance, walk smoothly, and even perform fine motor skills. The causes of spinocerebellar ataxia are diverse, including genetic factors, brain injuries, or certain diseases. Many patients with spinocerebellar ataxia require specialized medical care and ongoing monitoring to manage its effects. This study aims to utilize Human Posture Estimation to detect the gait patterns of patients with spinocerebellar ataxia and integrate it with a deep learning model to distinguish between normal and affected individuals. The outcomes offer physicians an objective diagnostic tool and facilitate early detection and intervention for patients with impaired gait, thereby supporting the enhancement or preservation of their quality of life. The current prediction accuracy for the risk of ataxia gait is 80%, with an F1 score of 78.26%. It is necessary to conduct a detailed quantitative assessment by analyzing the gait of patients with spinocerebellar ataxia. Quantitative evaluation of the data allows for an objective assessment of the patient's gait, which helps healthcare professionals to more accurately understand and assess the gait status of patients with spinocerebellar ataxia. © 2024 IEEE.

关键词： Gait analysis

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Analyzing Significance of Facial Features for Detection of Parkinson's Disease via Static Images 4

Analyzing Significance of Facial Features for Detection of P...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chiu, Chuang-Chien Yeh, Shoou-Jeng Sun, Yen-Chi Feng Chia University Department of Automatic Control Engineering Taichung Taiwan Cheng-Ching General Hospital Section of Neurology and Neurophysiology Taichung Taiwan Feng Chia University Master's Program of Biomedical Informatics and Biomedical Engineering Taichung Taiwan

ISBN: (纸本)9798350360721

Parkinson's disease (PD) is a neurodegenerative disorder often characterized by motor symptoms such as hypomimia, and a reduction in facial expression. We explored the significance of facial features in identifying PD through static images, focusing on detecting hypomimia. A dataset comprised of PD patients and healthy controls' static facial images were collected and analyzed. Facial feature detection was conducted using OpenCV and Dlib libraries for the precise localization of key landmarks. In the subsequent feature extraction, parameters such as symmetry, eye aspect ratio, mouth corner distance, muscle tension, and mouth aperture ratio were used. Through the standardization of these features, meaningful comparisons across diverse images were made. By analyzing hypomimia-related features alongside traditional facial parameters, the results provide a comprehensive understanding of the role of facial expressions in PD diagnosis. The findings suggested that examining facial features in static images enhances the accuracy of PD detection and understanding of the nuanced motor symptoms associated with the disease. © 2024 IEEE.

关键词： Feature extraction

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