Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Telematika : Jurnal Informatika dan Teknologi Informasi
Waode Siti Nurul Hassanah
Universitas Halu Oleo
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Digital Image Processing to Detect Cracks in Buildings Using Naïve Bayes Algorithm (Case Study: Faculty of Engineering, Halu Oleo University) Waode Siti Nurul Hassanah; Yunda Puji Lestari; Rizal Adi Saputra
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.8925

Abstract

Purpose: To detect cracks in the walls of buildings using digital image processing and the Naïve Bayes Algorithm.Design/methodology/approach: Using the YCbCr color model for the segmentation process and the HSV color model for the feature extraction process. This study also uses the Naïve Bayes Algorithm to calculate the probability of feature similarity between testing data and training data.Findings/result: Detecting cracks is an important task to check the condition of the structure. Manual testing is a recognized method of crack detection. In manual testing, crack sketches are prepared by hand and deviation states are recorded. Because the manual approach relies heavily on the knowledge and experience of experts, it lacks objectivity in quantitative analysis. In addition, the manual method takes quite a lot of time. Instead of the manual method, this research proposes digital-based crack detection by utilizing image processing. This study uses an intelligent model based on image processing techniques that have been processed in the HSV color space. In addition, this study also uses the YcbCr color space for feature extraction and classification using the Naïve Bayes Algorithm for crack detection analysis on building walls. The accuracy of the research test data reached 88.888888888888890%, while the training data achieved an accuracy of 93.333333333333330%.Originality/value/state of the art: This study has the same focus as previous research, namely detecting cracks in building walls, but has different methods and is implemented in case studies.
Co-Authors Rizal Adi Saputra Yunda Puji Lestari