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Tjatur Kandaga Gautama, Tjatur Kandaga
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Computer Science & IT

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Pengenalan Objek pada Computer Vision dengan Pencocokan Fitur Menggunakan Algoritma SIFT Studi Kasus: Deteksi Penyakit Kulit Sederhana Gautama, Tjatur Kandaga; Hendrik, Antonius; Hendaya, Riskadewi
Jurnal Teknik Informatika dan Sistem Informasi Vol 2 No 3 (2016): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v2i3.647

Abstract

Human vision can do amazing things such as recognizing people or objects, navigating through obstacles, recognizing the mood in a scene, and imagining stories. To do mimicry of the human vision, the computer requires a sensor that functions like the human eye and a computer program that serves as a data processor from the sensor. Computer vision is the science that uses image processing to make decisions based on images obtained from sensors. In other words, computer vision aims to build an intelligent machine that can "see". Computer vision can be used to detect skin diseases, for example, to detect disease Shingles (Herpes Zoster), Hives (Urticaria), Psoriasis, Eczema, Rosacea, Cold Sores (Fever Blisters), Rash, Razor Bumps, Skin Tags, Acne, Athlete's Foot, moles, Age or Liver Spots, Pityriasis Rosea, Melasma (Pregnancy Mask), Warts, and Seborrheic keratoses. Prewitt, Sobel, Roberts, and Canny operator are used to detect the edges of one or more objects. Then the results will be match with the results of edge detection image data base to determine the type of disease using Scale invariant Feature Transform (SIFT) algorithm. Skin Disease Detection Expert System will be implemented with C++ programming language, IDE MS Visual Studio 2010 and OpenCV 2.4 library. Keywords— computer vision, edge detection, SIFT algorithm, skin disease
Sistem Pakar Deteksi Kerusakan Sepeda Motor dengan Metode Forward Chaining Alfrido, Douglas; Gautama, Tjatur Kandaga
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 3 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i3.701

Abstract

Abstract — Automatic motorcycle has gain a lot of popularity in recent years. Motorcycle users usually didn’t understand technical issues about their vehicle. The purpose of this research is to build a website that could help motorcycle users to detect damage on their motorcycle. The website employs expert system with forward chaining method. Current knowledge base focused on automatic motorcycle machine failure, but can be expanded or changed to another more broad domain context. Users can get insight of what caused the damage and how to fix it.   
High Performance Computing Environment using General Purpose Computations on Graphics Processing Unit Widjaja, Andreas; Gautama, Tjatur Kandaga; Sujadi, Sendy Ferdian; Harnandy, Steven Rumanto
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i2.3715

Abstract

Here a report of a development phase of an environment of high performance computing (HPC) using general purpose computations on the graphics processing unit (GPGPU) is presented. The HPC environment accommodates computational tasks which demand massive parallelisms or multi-threaded computations. For this purpose, GPGPU is utilized because such tasks require many computing cores running in parallel. The development phase consists of several stages, followed by testing its capabilities and performance. For starters, the HPC environment will be served for computational projects of students and members of the Faculty of Information Technology, Universitas Kristen Maranatha. The goal of this paper is to show a design of a HPC which is capable of running complex and multi-threaded computations. The test results of the HPC show that the GPGPU numerical computations have superior performance than the CPU, with the same level of precision.
Co-Authors Alfrido, Douglas Andreas Widjaja Antonius Hendrik, Antonius Harnandy, Steven Rumanto Riskadewi Hendaya, Riskadewi Sendy Ferdian Sujadi