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All Journal Jurnal Penelitian Enjiniring Fakultas Teknik Unhas
Amirullah, Indrabayu
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Engineering

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Sistem Deteksi Lubang pada Pedesterian dengan Teknik Pengolahan Citra Areni, Intan Sari; Amirullah, Indrabayu; Nurtanio, Ingrid; Bustamin, Anugrayani; Rifaldi, Ahmad
Jurnal Penelitian Enjiniring Vol 23 No 2 (2019)
Publisher : Center of Techonolgy (COT), Fakultas Teknik, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (424.283 KB) | DOI: 10.25042/jpe.112019.04

Abstract

Pothole Detection System on Pedesterian using Image Processing Techniques. The pedestrian areas in Indonesia are still far from optimal in facilitating the users or the pedestrians. Potholed pedestrian areas are found in many parts of the street. This issue can harm pedestrians, especially blind people. For this reason, research has been carried out to create a system that can detect and estimate hole distances by processing images using mono cameras that can help blind people. The methods used to detect holes are the Threshold + Blob Analysis method and the HSV method. The obtained results indicate the level of accuracy of hole detection using the Threshold + Blob Analysis method is better than the HSV method. The average accuracy level of Threshold + Blob Analysis is 88.91%, while for the HSV method is 86.82%.
Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV Areni, Intan Sari; Amirullah, Indrabayu; Arifin, Nurhikma
Jurnal Penelitian Enjiniring Vol 23 No 2 (2019)
Publisher : Center of Techonolgy (COT), Fakultas Teknik, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (309.517 KB) | DOI: 10.25042/jpe.112019.03

Abstract

Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image. The method used for color segmentation is Hue, Saturation, Value (HSV) and for the classification of strawberry maturity using the Multi-Class Support Vector Machine (SVM) algorithm with a Radial Basic Function (RBF) kernel. Strawberry image data was retrieved using the Logitech C920 camera. The dataset consisted of 158 images of strawberries. The results showed that the classification of strawberry maturity using the multi-class SVM algorithm with kernel parameters RBF cost (C) = 10 and gamma (γ) = 10-3 produced the highest accuracy of 97%.
Co-Authors Areni, Intan Sari Arifin, Nurhikma Bustamin, Anugrayani Ingrid Nurtanio RIFALDI, AHMAD