Mohamad Rizal Syafi'i
Natural Language Processing
Public Opinion Extraction
This project analyzes public opinion on smart city development using Latent Dirichlet Allocation (LDA). Data was collected, processed using natural language processing (NLP) techniques, and modelled to identify key topics. The analysis results reveal opinion trends and provide insights for stakeholders in planning a more inclusive and sustainable smart city.
Extracting Address Elements
Developing an address extraction model to capture Points of Interest (POI) names and street names from unstructured addresses in Indonesia. This project utilizes natural language processing (NLP) and text parsing techniques to improve address normalization accuracy, supporting various applications such as logistics, navigation, and spatial data analysis.
Simple ChatBot
Interactive chatbot to help users learn English practically. Chatbot is designed to respond to user queries using predefined responses from a dataset. The chatbot processes input questions and matches them with the most relevant answer. It is implemented in Python using Jupyter Notebook and utilizes an Excel dataset for question-response mapping.
Contacts Multiple Channels
Identifying and grouping all contacts from users who share the same contact information across multiple tickets. The system consolidates contacts based on shared phone numbers or email addresses, treating them as belonging to the same user. This helps in better tracking and unifying user interactions across different communication channels.
Tweets Analysis
A project that leverages PySpark to perform sentiment analysis on a dataset. The goal is to classify tweets using big data processing techniques. With PySpark, we efficiently handle large-scale datasets and apply NLP techniques to derive meaningful insights from social media conversations.