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All Journal Jurnal Informatika Progres Innovative: Journal Of Social Science Research
Jong, Fenny
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Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Public Health Other

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PERBANDINGAN KINERJA K-NEAREST NEIGHBORS DAN CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI CITRA KONDISI PERMUKAAN JALAN Jong, Fenny; Handhayani, Teny
PROGRESS Vol 17 No 1 (2025): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i1.426

Abstract

Improving road infrastructure quality is an important aspect of transportation development and road user safety. Automatically assessing road surface conditions can accelerate maintenance and repair efforts. This study compares two classification methods, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), to evaluate road surface conditions based on digital images. Texture features are extracted using the Gray Level Co-occurrence Matrix (GLCM), including Contrast, Homogeneity, Energy, and others, to enhance the classification accuracy in KNN, while feature extraction and classification in CNN are performed automatically. The dataset used in this research consists of 1500 images of road surfaces with three different conditions: smooth, cracked, and potholes. Each condition contains 500 images with a resolution of 300x300 pixels. The results show that the KNN algorithm achieves an accuracy of 57.2%, while CNN demonstrates the best performance with an accuracy of 93.8%. for 80% training data and 20% testing data
Classification of Vegetable Types Using Singular Value Decomposition (SVD) and K-Nearest Neighbor (KNN) Algorithms Jong, Fenny; Herwindiati, Dyah Erny
Innovative: Journal Of Social Science Research Vol. 4 No. 5 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i5.14523

Abstract

Vegetables are widely grown in Indonesia, but sometimes they can be prepared poorly and pose risks to consumers. To solve this problem, we need a high-quality system that can identify good and safe vegetables. This study aims to create a vegetable classification system using pictures and computer algorithms. The system analyzes different types of vegetable images, including color and shape. It uses special techniques called Singular Value Decomposition (SVD) and K- Nearest Neighbor (KNN) to classify the vegetables based on their features. The researchers used a dataset of 121 vegetable images, which were divided into 73 training images and 48 test images. The results showed that the system was able to classify the vegetables with a high accuracy rate of 85.42%. This study has the potential to help improve the quality of vegetables and contribute to the development of automated systems in the agricultural industry.
Co-Authors Dyah Erny Herwindiati Teny Handhayani