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yusaks83's Projects

fasttext icon fasttext

Library for fast text representation and classification.

hr-analytics-powerbi icon hr-analytics-powerbi

HR Data Analytics portfolio project on a dataset of 80K records that deals with HR KPIs like Performance tracking, attrition rate. Project involved cleaning, transforming data and visualizing it to create a dashboard. I have used the MS Excel and Power BI's Query Editor for data cleaning and preprocessing and dashboard.

inset icon inset

Indonesia Sentiment Lexicon

materi-training icon materi-training

Kumpulan Materi Training IoT/ Image Processing/ Robotics/ Computer Vision

method_3moahp icon method_3moahp

Inconsistency Reduction Technique for AHP and Fuzzy-AHP Methods

nlp-akash icon nlp-akash

Natural Language Processing notes and implementations.

pydecision icon pydecision

pyDecision is a comprehensive Python library that encompasses a wide array of Multi-Criteria Decision Analysis (MCDA) methods. These powerful and versatile tools assist in making effective decisions by comparing alternatives based on multiple criteria, making it a valuable resource for researchers, analysts, and decision-makers.

sastrawi icon sastrawi

High quality stemmer library for Indonesian Language (Bahasa)

sentiment-analysis-indonesia icon sentiment-analysis-indonesia

Sentiment Analysis Twitter Bahasa Indonesia dengan TextBlob. Diaplikasikan untuk menganalisis tentang topik debat capres-cawapres 2019

tweetfeels icon tweetfeels

Real-time sentiment analysis in Python using twitter's streaming api

twint icon twint

An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.

twitter-sentiment-analysis-in-python icon twitter-sentiment-analysis-in-python

The objective of this task is to detect hate speech in tweets. Tweet contains negative/hate sentiments as well as positive sentiments. So, the task is to classify negative tweets from other tweets. Given a training sample of tweets and labels, where label '1' denotes the tweet is negative and label '0' denotes the tweet is not negative. The objective is to predict the labels on the test dataset.

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