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Endina Putri Purwandari
Department of Informatics, Universitas Bengkulu
Computer Science & IT Electrical & Electronics Engineering

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Identifikasi Jenis Bambu Berdasarkan Tekstur Daun dengan Metode Gray Level Co-Occurrence Matrix dan Gray Level Run Length Matrix Endina Putri Purwandari; Rachmi Ulizah Hasibuan; Desi Andreswari
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.891 KB) | DOI: 10.14710/jtsiskom.6.4.2018.146-151

Abstract

Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.
Pengenalan sketsa wajah menggunakan principle component analysis sebagai aplikasi forensik Endina Putri Purwandari; Aan Erlansari; Andang Wijanarko; Erich Adinal Adrian
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13422

Abstract

Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.
Co-Authors Aan Erlansari Andang Wijanarko Desi Andreswari Erich Adinal Adrian Rachmi Ulizah Hasibuan