laelasbuch

app.thethreadtimes.apk

The Thread Times Android Source Apk

football-python-flask-app

Python Flask app with a Machine Learning model that predict international football game result.

Heart-Disease-Predictor-QDA

Heart Disease Predictor QDA Framingham Dataset

it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

kumparanian

Data engineering and Data scientist hiring process at scale

laelasbuch

laelasbuch-landing-page-asbuch-clothing

Landing Page Asbuch Clothing Line Store

LDA-Prediction-Hearth-Disease

Hearth Disease Prediction on UCI Machine Learning Dataset with Linear Discriminant Analysis Algorithm

Sentiment-Analysis-about-Medan-City-Election

Indonesia has 19.5 million Twitter users from a total of 500 million global users and continues to grow over time. Twitter users utilize it as a forum for open campaigning by Medan mayoral candidates and their volunteers prompted Netizens to respond. Netizen's response to any tweet is Positive and Negative. Therefore, this research tries to analyze tweets about netizen sentiment for the Medan City Elections in 2020. Opinions or sentiments from Twitter users can certainly be used as criticism and suggestions that can be accommodated by candidates for mayor and deputy mayor of Medan. Netizens of Twitter often opinion about the Regional Head Candidate through his Uploads. The opinions of the Twitter Netizens are still random or unclassified. To facilitate the process of classifying opinion data netizens needed a Sentiment Analysis. Sentiment Analysis is carried out by classification of tweets containing Netizen sentiments towards the Implementation of Medan City Elections 2020. The classification method used in this research is the Multilayer Perceptron method with the relu activation function and adam optimization function combined with TF-IDF feature extraction. The validity test applied to this research used a confusion matrix. With tf-idf feature extraction and the multilayer, perceptron method will be able to automatically classify sentiment analysis with an accuracy of 92,733%

text-to-video-with-python-django

5-minute video with audio from text in Python using the Django framework, HTML, and CSS involves multiple steps, and it's a complex task

laelasbuch/Sentiment-Analysis-about-Medan-City-Election

Indonesia has 19.5 million Twitter users from a total of 500 million global users and continues to grow over time. Twitter users utilize it as a forum for open campaigning by Medan mayoral candidates and their volunteers prompted Netizens to respond. Netizen's response to any tweet is Positive and Negative. Therefore, this research tries to analyze tweets about netizen sentiment for the Medan City Elections in 2020. Opinions or sentiments from Twitter users can certainly be used as criticism and suggestions that can be accommodated by candidates for mayor and deputy mayor of Medan. Netizens of Twitter often opinion about the Regional Head Candidate through his Uploads. The opinions of the Twitter Netizens are still random or unclassified. To facilitate the process of classifying opinion data netizens needed a Sentiment Analysis. Sentiment Analysis is carried out by classification of tweets containing Netizen sentiments towards the Implementation of Medan City Elections 2020. The classification method used in this research is the Multilayer Perceptron method with the relu activation function and adam optimization function combined with TF-IDF feature extraction. The validity test applied to this research used a confusion matrix. With tf-idf feature extraction and the multilayer, perceptron method will be able to automatically classify sentiment analysis with an accuracy of 92,733%

laelasbuch/football-python-flask-app

Python Flask app with a Machine Learning model that predict international football game result.

laelasbuch/text-to-video-with-python-django

5-minute video with audio from text in Python using the Django framework, HTML, and CSS involves multiple steps, and it's a complex task

laelasbuch/app.thethreadtimes.apk

The Thread Times Android Source Apk

laelasbuch/Heart-Disease-Predictor-QDA

Heart Disease Predictor QDA Framingham Dataset

laelasbuch/it-cert-automation-practice

Google IT Automation with Python Professional Certificate - Practice files

laelasbuch/kumparanian

Data engineering and Data scientist hiring process at scale

laelasbuch/laelasbuch

laelasbuch/laelasbuch-landing-page-asbuch-clothing

Landing Page Asbuch Clothing Line Store

laelasbuch/LDA-Prediction-Hearth-Disease

Hearth Disease Prediction on UCI Machine Learning Dataset with Linear Discriminant Analysis Algorithm

laelasbuch/laelasbuch.github.io

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