Backend & Smart Contract Developer News
I'm Albany Siswanto, a college student in Indonesia with a deep passion for programming logic. Since the age of 16, during my high school education, I've been diving into the world of coding and building a solid experience. My focus is backend development, with a particular interest in web3 technologies, especially in designing smart contracts. With a spirit of collaboration, I am open to recruitment opportunities to create innovative solutions with a dynamic team.
| Year | Degree / Program | Institution |
|---|---|---|
| 2027 | Bachelor’s Degree in Computer Science | Bina Nusantara University |
| 2023 | Vocational High School in Software Engineering | SMK BPI Bandung |
| Year | Role / Position | Organization |
|---|---|---|
| Sep 2023 - Aug 2024 | Research and Development | Bina Nusantara Computer Club |
| Jun 2022 - Sep 2022 | Full-stack Developer Intern | PT Swamedia Informatika |
Bunny SQL Assistant is a command-line interface (CLI) tool that transforms natural language commands (in Indonesian or English) into valid SQL queries and executes them directly on your local database. This project is designed to simplify database interactions using everyday language.
The paper "Pneumonia Detection from X-Ray Images Using Deep Learning" presents a CNN-based AI system for detecting pneumonia from 5,856 Kaggle chest X-ray images. Built with TensorFlow, the model uses convolutional, pooling, flattening, and dense layers with the Adam optimizer, achieving 90% accuracy and 92% recall. Deployed on a web interface, it aids doctors by reducing diagnostic delays and errors, prioritizing high recall to minimize false negatives.
The paper "Optimization of Bone Fracture Diagnosis in X-Ray Systems" uses Python for model development, YOLOv11 Nano for efficient object detection, Kaggle for X-ray image datasets, and Roboflow for precise image annotation. Data preprocessing includes normalization, resizing, and augmentation to enhance dataset quality. Performance is evaluated using Precision, Recall, mAP50, mAP50-95, Confusion Matrix, and Precision-Recall/Recall-Confidence Curves to assess detection accuracy and sensitivity. These tools support effective data handling, model training, and evaluation for bone fracture detection.
Lapar is a Flutter and Go-based educational app designed to help parents manage their child's screen time and provide learning quizzes. The app has two main roles: Parent and Child.