mohon untuk mengunggah soft file dalam bentuk PDF Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes

Hidayat, Fakhar Muhammad, Crysdian, Cahyo ORCID: https://orcid.org/0000-0002-7488-6217 and Lestari, Tri Mukti ORCID: https://orcid.org/0009-0005-9416-7905 (2026) mohon untuk mengunggah soft file dalam bentuk PDF Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes. JISKA (Jurnal Informatika Sunan Kalijaga), 11 (2). pp. 155-168. ISSN 25280074

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Abstract

Manual identification of road damage using simple measuring tools is considered inefficient, subjective, and time-consuming, hindering the infrastructure repair process. This study aims to optimize automatic road crack detection by combining edge detection for feature extraction and Gaussian Naive Bayes (GNB) classification. This research utilizes the Road Surface Classification Dataset (RSCD), consisting of 1000 concrete road images with balanced class proportions. The research process includes image acquisition, segmentation, and preprocessing using the Sobel filter to extract edge features and erosion to refine crack representation. Statistical features in the form of black pixel count and edge length are extracted as model inputs. Experiments were conducted using three data split scenarios (70:30, 80:20, 90:10) validated with the K-Fold Cross Validation method. The test results show that the 90:10 data split scenario yields the most optimal and stable performance, achieving 88% accuracy, 91.67% precision, 84.62% recall, and an F1-Score of 88%. This study optimises the balance between computational efficiency and detection accuracy through a lightweight hybrid approach that integrates edge-based feature extraction with probabilistic classification.

Item Type:	Journal Article
Keywords:	Road Crack Detection, Gaussian Naive Bayes, Sobel Filter, Image Classification, Image Processing
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Dr. Cahyo Crysdian
Date Deposited:	13 Jul 2026 09:20

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