Portfolio

GolekFood
RECCOFFEE
Capital Bikeshare Data Analytics
Clickbait Headline Classification
Google Play Store Recommender System
Predictive Analysis of Gasoline Price
Natural Images Classification
Pyspark Water Quality Classification
Disaster Tweet Detection

GolekFood

Runner-up of Amikom ICT Award (AMICTA) 2023 in the AI and IOT category

GolekFood is an Indonesian food or drink recommendation website based on nutritional value. As an AI Engineer and data scientist in this project, I am in charge of cleaning data, analysing data, applying machine learning algorithms and deploying models so that they can be accessed through APIs.

Live Demo

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RECCOFFEE

Best Capstone Project at Dicoding Academy Certified Independent Study X Kampus Merdeka

RECCOFFEE is a coffee bean recommendation website based on user preferences which include aroma, acid, body, flavour, and aftertaste. The recommendation system on this website is made using the Gaussian Naive Bayes algorithm.

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Capital Bikeshare Data Analytics

This project is a dataset analysis project for Capital Bikeshare, a bicycle-sharing system that serves Washington, D.C., and certain counties of the larger metropolitan area. The goal of this project is to gain insights from the data in order to answer several business questions that determine the company's decisions.

Live Demo

Source Code
Clickbait Headline Classification

This project is a clickbait news headline classification project using three different deep learning algorithms namely LSTM, Bi-LSTM, and GRU. The three algorithms are compared based on accuracy, precision, recall, and F1-score, and training time. This project is also an experiment from a research with my lecturer at the university.

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Google Play Store Recommender System

This project aims to create an app recommendation system on the Google Play Store dataset based on its category. Recommendations utilise cosine simmilarity to measure the similarity between apps.

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Predictive Analysis of Gasoline Price

This project aims to predict future petrol prices based on existing time series data. The prediction process uses three algorithms namely Support Vector Regressor, Gradient Boosting Regressor, and KNeighbors Regressor. The three algorithms through the process of hyperparameter tuning.

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Natural Images Classification

This is a project to classify natural images. The images have four categories: cat, flower, fruit, and human. Classification utilises TensorFlow's Image Data Generator.

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Pyspark Water Quality Classification

This is a project that aims to classify whether water is safe for consumption or not based on its chemical content. This project is run using the PySpark library.

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Disaster Tweet Detection

This project is an experimental research project with my lecturer that aims to classify whether a tweet contains disaster event information or not. This project uses the SVM algorithm by modifying some parameters in it.

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