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All Journal JISKa (Jurnal Informatika Sunan Kalijaga)
Tri Mukti Lestari
Universitas Islam Negeri Maulana Malik Ibrahim
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Optimasi Deteksi Retakan Jalan Menggunakan Filter Sobel dan Klasifikasi Gaussian Naïve Bayes Fakhar Muhammad Hidayat; Cahyo Crysdian; Tri Mukti Lestari
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5912

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.
Klasifikasi Penyakit Mata Berdasarkan Citra Fundus Menggunakan Metode Multi-Layer Perceptron Laudza Atsila Prasetyo; Okta Qomaruddin Aziz; Tri Mukti Lestari
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6022

Abstract

This research aims to evaluate the performance of the Multi-Layer Perceptron (MLP) for classifying eye diseases from fundus images in the ODIR dataset, which comprises four classes: Normal, Diabetic, Glaucoma, and Cataract. The methodology includes feature extraction using GLCM and Gabor, data pre-processing through cleaning, augmentation, and undersampling, and testing 16 model scenarios with variations in the number of hidden layers (2 and 3) and neuron configurations. The results show that data balance and dataset size are the most influential factors affecting model performance, with the best results achieved through the combination of undersampling and augmentation. The optimal architecture was obtained with the 64–32-neuron configuration, yielding a mean accuracy of 73.06%. Overall, this study concludes that combining a balanced dataset with a proportional MLP architecture significantly improves the model’s ability to classify eye diseases from fundus images.
Co-Authors Cahyo Crysdian Fakhar Muhammad Hidayat Laudza Atsila Prasetyo Okta Qomaruddin Aziz