Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes
DOI:
https://doi.org/10.14421/jiska.5912Keywords:
Road Crack Detection, Gaussian Naive Bayes, Sobel Filter, Image Classification, Image ProcessingAbstract
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.
References
Anh, T., & Tam, M. (2022). Stripping resistance evaluation of bead coating via Hamburg wheel tracking test and image analysis 함부르크휠 시험과 이미지 분석을 통한 비드코트 탈리 저항성 평가. 24(6), 47–52.
Anjas, M. (2024). Optimization of Classification Algorithms Performance with k-Fold Cross Validation. 2.
Bhat, S., & Szczuko, P. (2025). Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson ’ s disease classification. 1–39.
Bhatt, U., Mani, S., Xi, E., & Kolter, J. Z. (2017). Intelligent Pothole Detection and Road Condition Assessment. September. http://arxiv.org/abs/1710.02595
Cha, Y. J., Choi, W., & Büyüköztürk, O. (2017). Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. https://doi.org/10.1111/mice.12263
Chakurkar, P. S., Vora, D., Patil, S., & Kotecha, K. (2024). Automated crack localization for road safety using contextual u-net with spatial-channel feature integration. MethodsX, 13(March), 102796. https://doi.org/10.1016/j.mex.2024.102796
Dong, L. (2025). A Local Thresholding Algorithm for Image Segmentation by Using Gradient Orientation Histogram. c.
Fahmi, M., Yudhana, A., & Sunardi, S. (2023). Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 22(3), 553–566. https://doi.org/10.30812/matrik.v22i3.2819
Fereidoon, H. Z., Nejad, M., & Fahimifar, A. (2016). Image Based Techniques for Crack Detection , Classification and Quantification in Asphalt Pavement : A Review. Archives of Computational Methods in Engineering, 424. https://doi.org/10.1007/s11831-016-9194-z
Fitriani, R. D., Yasin, H., & Tarno, T. (2021). PENANGANAN KLASIFIKASI KELAS DATA TIDAK SEIMBANG DENGAN RANDOM OVERSAMPLING PADA NAIVE BAYES (Studi Kasus: Status Peserta KB IUD di Kabupaten Kendal). Jurnal Gaussian, 10(1), 11–20. https://doi.org/10.14710/j.gauss.v10i1.30243
Grace, O. E., C, A. D., & N, O. J. (2025). Exploring the Effectiveness of Sobel , Canny , and Prewitt Edge Detection Algorithms on Digital Images.
Hasanah, Q., Oktavianto, H., & Rahayu, Y. D. (2022). Analisis Algoritma Gaussian Naive Bayes Terhadap Klasifikasi Data Pasien Penderita Gagal Jantung Gaussian Naive Bayes Algorithm Analysis Of Data Classification Of Heart Failure Patiens. Jurnal Smart Teknologi, 3(4), 2774–1702. http://jurnal.unmuhjember.ac.id/index.php/JST
Islam, R., Devnath, M. K., Samad, M. D., & Jaffrey Al Kadry, S. M. (2022). GGNB: Graph-based Gaussian naive Bayes intrusion detection system for CAN bus. Vehicular Communications, 33(November). https://doi.org/10.1016/j.vehcom.2021.100442
Khudhair, Zaid N., Ahmed Nidhal, Nidhal K. El Abbadi, Farhan Mohamed, Tanzila Saba, Faten S. Alamri, A. R. (2017). Color to Grayscale Image Conversion Based on Singular Value Decomposition. IEEE Access, xx. https://doi.org/n: DOI 10.1109/ACCESS.2023.3279734
Kurniati, F. T., & Pramana, D. (2023). Identifikasi Objek Menggunakan Random Forest dan Multi- Fitur. 130–136.
Kusumaningrum, J., Madenda, S., Karmilasari, & Nahdalina. (2022). Detection and Classification of Road Damage Based on Image Morphology and K-NN Method (K Nearest Neighbour). International Journal of Engineering and Advanced Technology, 11(5), 86–90. https://doi.org/10.35940/ijeat.e3543.0611522
Ma, S., Khalil, A., Hajjdiab, H., & Eleuch, H. (2020). Quantum Dilation and Erosion. 1–13. https://doi.org/10.3390/app10114040
Moghaddam, M. Z., Umili, G., Messina, V., Bonetto, S., Ferrero, A. M., Bollini, G., & Gandreau, D. (2020). applied sciences An SVM-Based Scheme for Automatic Identification of Architectural Line Features and Cracks.
Mohan, A., & Poobal, S. (2018). Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal, 57(2), 787–798. https://doi.org/10.1016/j.aej.2017.01.020
Mustolih, A., Arsi, P., Subarkah, P., Purwokerto, U. A., Info, A., Mining, D., Classifier, N. B., & Analysis, S. (2023). Sentiment Analysis Motorku X using Applications Naive Bayes Classifier Method. 6(2), 231–242.
Oliveira, H., Correia, P. L., & Member, S. (2013). and Characterization. 14(1), 155–168.
Damanik, S. T., Setiawan, A., Simanjuntak, T., & Tumanggor, A. (2023). Analisis Deteksi Tepi untuk Mengidentifikasi Jenis Mobil. 14(1), 103–109.
Supiyandi Supiyandi, Trisatin Panggabean, Nuzul Ramadhan, Sri Ratna Dewi, & Salsabila Yusra. (2024). Deteksi Tepi Sederhana Pada Citra Menggunakan Operator Sobel. Repeater : Publikasi Teknik Informatika Dan Jaringan, 2(3), 43–56. https://doi.org/10.62951/repeater.v2i3.90
Surya, A. A., & Yamasari, Y. (2024). Penerapan Algoritma Naïve Bayes (NB) untuk Klasifikasi Penyakit Jantung. Journal of Informatics and Computer Science (JINACS), 5(03), 447–455. https://doi.org/10.26740/jinacs.v5n03.p447-455
Susanto, E. R., & Cahyana, A. (2025). Penerapan Algoritma XGBoost untuk Prediksi Diabetes: Analisis Confusion Matrix dan ROC Curve. Fountain of Informatics Journal, 10(1), 40–50. https://doi.org/10.21111/fij.v10i1.14311
Tong Zhao, Junxiang He, Jingcheng Lv, Delei Min, Y. W. (2023). A Comprehensive Implementation of Road Surface Classification for Vehicle Driving Assistance: Dataset, Models, and Deployment. IEEE Transactions on Intelligent Transportation Systems, 24, 8361–8370. https://doi.org/10.1109/TITS.2023.3264588
Xu, Y., Xia, Y., Zhao, Q., Yang, K., & Li, Q. (2024). A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion. Electronics (Switzerland), 13(12), 1–21. https://doi.org/10.3390/electronics13122257
Xu, Z., Guan, H., Kang, J., Lei, X., Ma, L., Yu, Y., Chen, Y., & Li, J. (2022). Pavement crack detection from CCD images with a locally enhanced transformer network. International Journal of Applied Earth Observation and Geoinformation, 110(May), 102825. https://doi.org/10.1016/j.jag.2022.102825
Yi, B., Long, Q., Liu, H., Gong, Z., & Yu, J. (2024). A lightweight ground crack rapid detection method based on semantic enhancement. Heliyon, 10(14), e34782. https://doi.org/10.1016/j.heliyon.2024.e34782
Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., & Wang, S. (2019). DeepCrack: Learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28(3), 1498–1512. https://doi.org/10.1109/TIP.2018.2878966
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Fakhar Muhammad Hidayat, Cahyo Crysdian, Tri Mukti Lestari
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
