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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Pane, Syafrial Fachri Ramdan, Jenly Putrada, Aji Gautama Fauzan, Mohamad Nurkamal Awangga, Rolly Maulana Alamsyah, Nur Advanced and Creative Networks Research Center Telkom University Bandung Indonesia Universitas Logistik Dan Bisnis International Bachelor of Applied Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350399615

The policy of limiting community mobilization is implemented to reduce the daily rate of COVID-19. However, a high-accuracy sentiment analysis model can determine public sentiment toward such policies. Our research aims to improve the accuracy of the LSTM model on sentiment analysis of the Jakarta community towards PPKM using Indonesian language Tweets with emoji embedding. The first stage is modeling using the hybrid CNN-LSTM model. It is a combination between CNN and LSTM. The CNN model cites word embedding and emoji embedding features that reflect the dependence on temporary short-term sentiment. At the same time, LSTM builds long-term sentiment relationships between words and emojis. Next, the model evaluation uses Accuracy, Loss, the receiver operating curve (ROC), the precision and recall curve, and the area under curve (AUC) value to see the performance of the designed model. Based on the results of the tests, we conclude that the CNN-LSTM Hybrid Model performs better with the words+emoji dataset. The ROC AUC is 0.966, while the precision-recall curve AUC is 0.957. © 2022 IEEE.

关键词： CNN-LSTM Machine Learning Model Hybrid Sentiment Analysis Tweet

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Multi-Speaker Voice Cloning in Indonesian Using Sequence to Sequence Speech Synthesis

SSRN

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SSRN 2022年

作者： Awangga, Rolly Maulana Majesty, Dinda Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung40151 Indonesia

Multi-Speaker Voice Cloning is a system that can produce various voices based on voice samples and text input. Voice cloning can be an alternative to get audio data quickly without recording, which requires a lot of time, cost, and effort. Sequence to Sequence Speech Synthesis produces sounds that are naturally similar to speech equivalent to the human voice. Sequence to Sequence Speech Synthesis is a technology for generating speech directly from the text, consisting of attention-based predictive network features. Multi-Speaker Voice Clone consists of speaker encoder, encoder-decoder, and vocoder models. Tacotron-2 as model encoder-decoder and WaveNet as a vocoder. The speaker encoder will produce a speaker embedding to recognize the sound from the speaker. Tacotron-2 will embed text into the mel scale spectrogram. WaveNet will synthesize the time domain waveform from the mel scale spectrogram and generate sound predictions based on the samples. This model uses a multi-speaker voice dataset in Indonesian. Combining the three models produces a value of 3.28 for speech naturalness and 1.94 for speech similarity. © 2022, The Authors. All rights reserved.

关键词： Cloning

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Reevaluating Synthesizing Sentiment Analysis on COVID-19 Fake News Detection using Spark Dataframe 7

Reevaluating Synthesizing Sentiment Analysis on COVID-19 Fak...

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7th International Conference on Information Technology Systems and Innovation, ICITSI 2022

作者： Pane, Syafrial Fachri Prastya, Rayhan Putrada, Aji Gautama Alamsyah, Nur Fauzan, Mohamad Nurkamal Advanced and Creative Networks Networks Research Center Telkom University Bandung Indonesia Bachelor of Applied Informatics Engineering Universitas Logistik Dan Bisnis International Bandung Indonesia Advanced and Creative Research Center Telkom University Bandung Indonesia

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

Some research uses the random forest model and sentiment analysis to detect COVID-19 fake news. However, there is still a research opportunity to apply the method to Indonesian Tweets and reevaluate the feature's performance. Our research aims to reevaluate synthesizing the sentiment analysis feature on detecting COVID-19 fake news on Indonesian Tweets by using the Spark Dataframe. We divide the stages of machine learning development into several steps, including collecting data using Tweepy and then applying sentiment polarity scores using Apache Spark. We apply random forest to classify fake news using the Spark MLlib. Further, we use model evaluation calculation through the level of Accuracy, Recall, Precision, and F1. The results show that applying the sentiment polarity calculation to our Tweet dataset labels 148 Tweets with positive sentiments, 118 Tweets with negative sentiments, and 99 Tweets with neutral sentiments. The Pearson correlation coefficient (PCC) feature score of Sentiment equals 0.056 and ranks fifth in the top feature correlation scores list. According to the experimental findings, the random forest model produces Accuracy = 0.787 for both models with sentiment analysis and without sentiment analysis. Which indicates that sentiment analysis provides no significance in the prediction model. © 2022 IEEE.

关键词： COVID-19

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A Hybrid CNN-LSTM Model With Word-Emoji Embedding For Improving The Twitter Sentiment Analysis on Indonesia's PPKM Policy

A Hybrid CNN-LSTM Model With Word-Emoji Embedding For Improv...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Syafrial Fachri Pane Jenly Ramdan Aji Gautama Putrada Mohamad Nurkamal Fauzan Rolly Maulana Awangga Nur Alamsyah Advanced and Creative Networks Research Center Telkom University Bandung Indonesia Bachelor of Applied Informatics Engineering Universitas Logistik dan Bisnis International Bandung

关键词： COVID-19 Analytical models Sentiment analysis Limiting Social networking (online) Government Receivers

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TPOT on Increasing the Performance of Credit Card Application Approval Classification

TPOT on Increasing the Performance of Credit Card Applicatio...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Aji Gautama Putrada Etika Khusnul Laeli Syafrial Fachri Pane Nur Alamsyah Mohamad Nurkamal Fauzan Advanced and Creative Networks Research Center Telkom University Bandung Indonesia Bachelor of Applied Informatics Engineering Universitas Logistik dan Bisnis International Bandung Indonesia

ISBN: (纸本)9798350399141

Credit approval or credit card approval with the help of machine learning classification has become a hot topic in recent years. However, credit card approval classifications from previous papers have limited performance. Therefore, we propose evaluating tree-based pipeline optimization (TPOT) as the classification model creation automation for credit card approval in this study. The data used is 690, namely the credit card approval dataset sourced from Kaggle, which we obtained from the University of California learning machine and is publicly available. Then we compare the TPOT result model with the credit card approval classification from the related paper as a benchmark, namely naïve Bayes, support vector machine (SVM), and decision tree. The test results show that the TPOT model of credit card approval classification has higher accuracy than the benchmark model of the related research. The results of the TPOT have accuracy = 0.89. While the accuracy of naïve Bayes, SVM, and decision tree are 0.823, 0.852, and 0.796, respectively. Then we also see that the TPOT model results can produce a receiver operating curve (ROC) with the highest area under curve (AUC) value compared to the benchmark method, which is 0.940. AUC naive Bayes method, SVM, and decision tree are 0.897, 0.918, and 0.796, respectively.

关键词： Support vector machines Pipelines Receivers Benchmark testing Programming Credit cards Naive Bayes methods

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Entropy and information gain analysis on low cost BCI for motorbike users to prevent accident 4

Entropy and information gain analysis on low cost BCI for mo...

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4th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2019

作者： Awangga, Rolly Maulana Pane, Syafrial Fachri Majesty, Dinda Setiawan, Asep Asyhari, Moch Yusuf Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Universitas Islam Indonesia Magister Program of Informatics Engineering Yogyakarta Indonesia

ISBN: (纸本)9781728151182

The phenomenon of mothers giving the wrong vehicle signal lights to turn left and right reminds the effect of giving the right signal lights to other road users. Weak human concentration while driving can cause errors when giving vehicle signal lights or not giving vehicle signal lights. Mistakes when giving a signal light a vehicle has a risk of an accident. The driver's brain wave data will show the results of the rider's brain thinking when turning. This study aims to analyze the concentration of the driver when driving. This research will obtain driver brain wave data when driving and analyze human brain waves while riding a motorcycle, especially when turning left and right, to reduce the risk of accidents while driving. The driver will use brain waves tool while driving, and the computer will detect the driver's brain waves, and then send brainwave data to the computer. Driver brain waves are analyzed using the formula entropy and gain. The results of the driver's brain waves become a decision whether the driver is allowed to drive on the highway based on the detected waves (alpha, beta, gamma, theta, and delta) to reduce the chance of an accident. © 2019 IEEE.

关键词： Entropy

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Entropy and Information Gain Analysis on Low Cost BCI for Motorbike Users to Prevent Accident

Entropy and Information Gain Analysis on Low Cost BCI for Mo...

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International Conference on Information Technology, Information Systems and Electrical engineering (ICITISEE)

作者： Rolly Maulana Awangga Syafrial Fachri Pane Dinda Majesty Asep Setiawan Moch Yusuf Asyhari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Magister Program of Informatics Engineering Universitas Islam Indonesia Yogyakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

关键词： Electroencephalography Entropy Turning Tools Calibration Motorcycles

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Smart Fish Feeder Using Arduino Uno With Fuzzy Logic Controller

Smart Fish Feeder Using Arduino Uno With Fuzzy Logic Control...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Nisa Hanum Harani Anggi Sholihatus Sadiah Anny Nurbasari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Economics Maranatha Khristian University Bandung Indonesia

ISBN: (数字)9781728120942

ISBN: (纸本)9781728120959

Smart Fish Feeder is an automatic fish feeder that can be controlled using android smartphone. With this tool, fish owners will easily adjust the feeding schedule according to the recommended feed dose, and provide aquarium cleaning scheduling. In designing a fish feeder, there are some criteria such as temperature and time interval of feeding. The data collection has been done by interviewing ornamental fish traders. The data also has been obtained from literature studies that support problem solving theory. The Laravel framework is used to interpret the system while Firebase as the Database Management System. The Android is as a front end that interacts directly with the user. Automatic fish feeding systems are implemented using arduino micro-controller and prototype feeding devices. This research uses Fuzzy Logic Controller method. With the creation of the prototype smart fish feeder the device functions well in terms of both controller and push data. The results of the calculation of the duration of fish feed using the Fuzzy Sugeno Algorithm have been successfully applied to the smart fish feeder.

关键词： Fish Feeds Fuzzy logic Fuzzy sets Temperature sensors Microcontrollers

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Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot

Comparison Of Multinomial Naive Bayes Algorithm And Logistic...

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International Conference on applied engineering (ICAE)

作者： Muhammad Yusril Helmi Setyawan Rolly Maulana Awangga Safif Rafi Efendi Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia

ISBN: (纸本)9781538680674

Chatbot is software that communicates using natural language. chatbots such as machine conversation systems, Chatterbot, virtual agents, and dialogue systems. This software enables to simulate human conversations. In this research, the chatbot system that will be created must be able to understand the natural language of what is entered by the user, and the chatbot will answer according to what the user is expecting. The researcher proposes a classification method to identify intent rather than user input or called intent classification on the chatbot system; the researcher also wants to know the level of accuracy, precision, and recall on the evaluation results of both methods. The classification method applied in this research is the Naive Bayes method and compared with the Logistic Regression method to determine the class intention. The evaluation results show the level of accuracy precision and recall in the Logistic Regression model is higher than the Naive Bayes model.

关键词： Logistics Training data Bayes methods Mathematical model Data models Classification algorithms Natural languages

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An Analysis of Customer Agrotourism Resort Behaviour based on RFM and Mean Shift Clustering

An Analysis of Customer Agrotourism Resort Behaviour based o...

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International Conference on applied engineering (ICAE)

作者： Muhammad Ruslan Maulani Syafrial Fachri Pane Rolly Maulana Awangga Diana Asri Wijayanti Wahyu Caesarendra Diploma Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781538680674

This paper presents an analysis study of customer reservation behavior in Agrotourism N8 based on recency, frequency and monetary (RFM) method. The historical data from January to December 2016 were collected from Rancabali tour and used for RFM analysis. Prior to the RFM analysis, the data were grouped into frequency and monetary using mean shift clustering. This analysis will provide the guideline to Agrotourism N8 to predict the customer needs in the future based on historical data. The RFM method shows that Rancabali tour always crowded with visitors until the end of each month. The highest number of visitors occurred in January, July, and December. Moreover, The highest revenue earned in December. The mean shift shows the income-based cluster by the frequency of customer.

关键词： Companies Market research Bandwidth informatics Kernel Industries

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