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classifying-twitter-user-as-resident-or-tourist

Researches confirms that social media provides good insights on what people think, feel, concern, etc. It is expected that those insight mined from Twitter data has potential to support a better decision-making, especially in public sectors. Public sector wants to know local’s insight level; therefore they need to make sure they use the conversation from residents. However, the ground truth shows that tweets are mixed from the residents and tourist. This study investigates the best automatic fashion model to classify tweets posted by resident and tourist, in NTB. Indonesia. To do so, several consecutive phases were conducted. Those are pre-processing, data training, classification system, data testing, accuracy comparison, and result visualization. First of all, a Twitter dataset, which has 700,000 tweets posted by approximately 26,000 users in Nusa Tenggara Barat, Indonesia was prepared. The dataset divided into two sets, tweets from 4,000 users for data training and 22,000 users for data testing. Then, three popular classification algorithms were applied to the datasets. There are Multinomial Naïve Bayes, Support Vector Machines and Decision Tree. After that, 7 features are created. There are Bag of Words, Normalizer location, Total Tweet, Total Day, Tweet per Day, Total Location and Location per Day. Experiment shows that Multinomial Naïve Bayes with Bag of Words feature has 86% accuracy, while the rest of features give less than 65% accuracy. This is different with Support Vector Machines and Decision Tree results. These two algorithms produce better accuracy results excluding Bag of Words feature. It implies that Support Vector Machine and Decision Tree are more powerful when processing numerical value. However, among all classification system, Multinomial Naïve Bayes still being the most accurate algorithm for the model.

csv-cleaning-data-in-python

This program manipulate csv file to read and edit in python. The function is to add coordinate on given province. I use Geopy library to get the coordinate.

gender-analysis

Exploratory Data Analysis of Gender from World Bank Data. Analysis using Pearson Correlation for gender in Development Countries (E.g Indonesia and India). All analysis using pandas on Jupyter Notebook.

interactive_visualization_unicef

local-and-tourist-classification

Classify Local and Tourist based on their tweets behaviour using SVM and Naive Bayes

method-using-svm-and-naive-bayes-to-analyse-fuel-subsidy-decreases-effect-in-jakarta-based-on-twitte

This research is my undergraduate thesis focusing on the effect of fuel subsidy cut using Machine Learning in Big Data. The background came which fuel subsidy cut is an major effect in Indonesia and i wonder i can capture people behaviour (changing) of using transportation to work. The idea is i implementing SVM and Naive Bayes algorithm to classifiy people using public transportation and private vehicle. This work is collaborate with United Nations Global Pulse - Pulse Lab Jakarta to provides me tweets data. Due to the procedure that UN had, i cannot shared the data. Most of the scripts is written in python using IPython Notebook. And the dataset that i want to analyse is differentiate into two parts : before the event (fuel subsidy cut) and after the event. Will people changing their transportation to work considering about fuel price that rising? After that, i applicate regression analysis to have conclusion or brief understanding between the amount of public transportation user and private vehicle user. At the end, i visualize all of the result from the system by using D3JS Visualization. For further discussion and information, please contact me at : [email protected] , [email protected] This work is still in progress, i hope that you can give me feedback or suggestion through the process of the project.

muvera_dashboard

GitHub Muvera Dashboard

neo4j_case

neo4j sandbox playground

seap-services

Place for us to store our thoughts, resources and revel in our nerdiness.