Projects
A collection of my projects in AI, machine learning, and IoT. Check out more on GitHub.
2025
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Exoplantropy — AI-Based Exoplanet Detection
An AI-powered web application that detects exoplanets from space telescope data (Hubble). Our system increases the exoplanet identification rate from 1-in-10 to 7-in-10 by filtering false positives through machine learning classification. Developed during the 48-hour NASA Space Apps hackathon with mentorship from BRIN researchers and MIT AI experts.
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CrackMap — Deep Learning Crack Detection
A deep learning-powered image analysis tool for detecting cracks in materials using computer vision. Provides a complete pipeline from image preprocessing (tiling, CLAHE enhancement, skeleton extraction, blur) to crack inference using a pretrained ResNet18 model. The heatmap output highlights crack regions — the brighter the area, the higher the model's confidence. A Streamlit web app is included for interactive demos with configurable tile division and minimum crack length parameters.
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IHSG News Analyst — Predicting Stock Index Movement from News
An MLP-based predictive model that forecasts changes in the Indonesian Composite Index (IHSG) using multimodal features extracted from financial news and trading calendar patterns. Integrates semantic similarity from Sentence-BERT embeddings, financial sentiment signals from RoBERTa logits, and cyclical temporal feature encoding (dates, holidays, trading patterns). End-to-end pipeline from web scraping and preprocessing to modeling and Streamlit visualization.
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IndoRoBERTa — Indonesian Financial Sentiment Classifier
Fine-tuned
w11wo/indonesian-roberta-base-sentiment-classifierfor Indonesian financial news sentiment classification (Positive / Neutral / Negative). Built because no financial sentiment model existed for the Indonesian market. Trained onintanm/indonesian-financial-sentiment-analysisaugmented with Gemini-generated synthetic samples, GPT-2 prompt completions, and RoBERTa masked-token augmentation with class balancing (~23 k samples per class). Achieved 97.49% accuracy and F1-score (best at epoch 3 with early stopping). Trained on Google Colab T4 GPU. Published on Hugging Face. -
Qwen2.5-0.5B-reasoning-lora — Reasoning-Enhanced Small LLM
A LoRA-fine-tuned version of Qwen2.5-0.5B aimed at improving chain-of-thought reasoning capabilities in a compact language model. Demonstrates that lightweight parameter-efficient fine-tuning (LoRA) can enhance logical reasoning in sub-billion-parameter models, making advanced reasoning accessible on consumer hardware. Published on Hugging Face.
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QLoRA Qwen2 0.5B — Quantized Fine-Tuning Experiment
A QLoRA (Quantized Low-Rank Adaptation) fine-tuned Qwen2-0.5B model exploring efficient fine-tuning of small language models under extreme memory constraints. Uses 4-bit quantization combined with low-rank adapters to enable full fine-tuning on consumer GPUs while preserving model quality. Published on Hugging Face.
2024
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Automated Plane Photography System with Real-Time Tracking
An automated camera system that tracks and captures aircraft in real time using GPS-based flight data (ADS-B/API). Computes azimuth and elevation angles to dynamically control camera orientation via servo motors, enabling accurate visual tracking of passing aircraft. Built with embedded and IoT hardware (ESP32, ESP32-CAM, Raspberry Pi, SDR) and designed for future integration with ML-based aircraft recognition and cloud analytics.
IoT ESP32 Raspberry Pi -
InTeRRoR — Integrating LLM Output for Robotics Input
An automated pipeline that translates natural language instructions into executable C++ code using a Large Language Model, then compiles and deploys the generated program directly to an Arduino-based robot in a single workflow. Enables high-level human commands to be converted into low-level embedded control logic without manual coding — from user instruction to robot behavior in one step.