Yudha Andriano Rismawan
Hey there 👋
I'm Yudha Andriano Rismawan
Artificial Intelligence Enthusiast
A self-motivated and passionate individual pursuing a Master’s degree in Statistics at Institut Teknologi Sepuluh Nopember, working with Prof. Nur Iriawan. I am highly interested in research, particularly in image processing, computer vision, statistical modeling, metaheuristic algorithms, and artificial intelligence . Experienced in medical image research, conference presentations, and writing academic research papers.
My CV Google Scholar Let's connect!
News
No news available at the moment.
Selected publications
Here are some of my selected publications. For more, please check out my Google Scholar.
Implementation of the Content Based Image Retrieval Method for Classifying Citrus Fruit Diseases

Implementation of the Content Based Image Retrieval Method for Classifying Citrus Fruit Diseases
IEEE

Budi Sunarko, Muhammad Rozy, Apriansyah Wibowo, Yudha Andriano Rismawan
Implementation of Deep Neural Network Algorithm in Developing Chatbot for Automation of Admission and Integrated Service Helpdesk of Universitas Negeri Semarang

Implementation of Deep Neural Network Algorithm in Developing Chatbot for Automation of Admission and Integrated Service Helpdesk of Universitas Negeri Semarang
IEEE

Budi Sunarko, Famela Tiara Intan, Yudha Andriano Rismawan, Dhidik Prastiyanto, Arief Arfriandi, Uswatun Hasanah
Development of coronary heart disease diagnosis system based on facial imagery

Development of coronary heart disease diagnosis system based on facial imagery
AIP Publishing

Yudha Andriano Rismawan, Agung Adi Firdaus, Indah Novi Yarman, Anisa Dwi Lutfi Yanti, Anan Nugroho, Rachelia Intan Merdina, Andreas Arie Setiawan
Latest from blog
Particle Swarm Optimization

Particle Swarm Optimization
New

Particle Swarm Optimization (PSO) is a population-based metaheuristic optimization algorithm inspired by the collective social behavior of biological swarms, such as bird flocking and fish schooling. By simulating cooperative interactions among particles in a search space, PSO efficiently explores complex optimization problems and has become widely used due to its simplicity, flexibility, and strong global search capability.
Solving PDF Integrals of Normal Distribution

Solving PDF Integrals of Normal Distribution

A brief proof demonstrating that the total area under a probability density function must equal 1 to satisfy the fundamental requirement of probability theory.
Proving the Gaussian Integral

Proving the Gaussian Integral

The probability density function (PDF) of normal distribution is derived from Gaussian Integral.