Alexander Tiopan's repositories
Indonesian_Spelling_Corrector
Developing Indonesian spelling correction using RNN model on root word dataset. Preprocess with tokenization and root form conversion, train, evaluate, and potentially improve deployment.
Compressor_Control_Offline_RL
The project models a compressor machine to predict parameter changes, ensuring stable pressure. It preprocesses data, employs ensemble methods, and utilizes reinforcement learning.
Classify_Fish_Species
This project aims to classify fish species using morphological features extracted from fish images, including dissimilarity, correlation, and energy.
Brent_Oil_Price_Prediction
Using RNN with LSTM neural networks, this project forecasts Brent oil prices by preprocessing historical data, training the model with dropout, and evaluating for accurate predictions.
Fracture_Leg_Detection
Developing a fracture detection system for leg radiographs using VGG16 pre-trained CNN, aiming to enhance diagnosis efficiency in clinical settings.
Clustering_Mobilphone_Sales
Cluster mobile phones by attributes for market segmentation. Employ K-means, preprocessing, and visualization tools in Python for analysis and insights.
Clustering_Training_Customer
Analyze Banten Marine Polytechnic cadet/alumnus profiles with age, recency, frequency, and monetary methods, clustering via K-means algorithm from scratch.
Hospital_Revenue_Prediction
Clustering hospital revenue from insurance claim payment using timeseries clustering method.
American_Option_Price
Predicts the fair value of Company XYZ's during participation rights in the X project using American Binomial Options Method.
GE_Visualization
Utilize the GE McKinsey Matrix methodology to assess product strategic positioning within the market using Python tools.
Gold_Price_Prediction
A custom Radial Basis Function (RBF) neural network is implemented to predict gold prices using time series data.
District_Graph_Coloring
Apply Greedy and Welch Powell algorithms to assign colors to map districts ensuring adjacent districts have different colors.
Clustering_Digital_Wallet_Users
This project aims to cluster digital wallet users based on their survey responses using the KPrototypes algorithm, which handles both numerical and categorical data, resulting in three distinct clusters characterized by different demographic and behavioral attributes.
Stock_Trend_Prediction
Employing TOBA stock data, the random forest algorithm predicts price trends, reaching a 70.2% accuracy post hyperparameter tuning and feature selection.
Classify_Wine_Class
The project implements Adaboost from scratch to classify wine classes using features from the wine dataset.
Predicting_Overweight
The project utilizes Support Vector Regression to predict individual weights from obesity data, contributing to personalized healthcare strategies.
Kompas_News_Hoax_Detection
Automates fake news identification in Kompas articles, comparing Random Forest and Convolutional Neural Network models for text classification.
PDF_Malware_Detection
The project enhances GARUDA's cybersecurity by employing deep learning to classify malware in scholarly resources, utilizing Python libraries and evaluation metrics.
Pytorch_to_ONNX_Converter
Install packages, define CNN in PyTorch, load weights, export to ONNX, fix input name error, save model.
Kemenkeu_Bandwidth_Prediction
Forecast time series using ARIMA, employing exploratory data analysis, data preprocessing, stationarity checks, AR and MA modeling, differencing, and Python libraries.