SISTEM KLASIFIKASI KUALITAS BUAH JERUK BERBASIS CITRA THERMAL MENGGUNAKAN LEARNING VECTOR QUANTIZATION

ELVIRA, NINA and Sutarno, Sutarno (2019) SISTEM KLASIFIKASI KUALITAS BUAH JERUK BERBASIS CITRA THERMAL MENGGUNAKAN LEARNING VECTOR QUANTIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Orange fruit is one of the most cultivated fruits in Indonesia, Medan orange is one of the most widely sold citrus varieties on the market because of its sweet taste. The selection process of quality on citrus fruit that is done manually is less effective because not all citrus fruits that look good on the outside have a sweet taste. Based on this problem, a study was conducted to classify the quality of citrus fruits using thermal images. Thermal capture using the FLIR ONE Pro thermal camera and smartphone. Next through the image processing, namely preprocessing as an increase in image quality and noise removal, segmentation as a process of separation between background and foreground, feature extraction is used to obtain object characteristics and classification using the Learning Vector Quantization (LVQ) algorithm for grouping data. Test results obtained in this study are 90% accuracy, 100% sensitivity, and 80% specificity in classifying oranges based on fruit quality.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Buah jeruk, Thermal Camera, Warna, learning vector quantization
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 4290 not found.
Date Deposited:	14 Jan 2020 06:21
Last Modified:	14 Jan 2020 06:21
URI:	http://repository.unsri.ac.id/id/eprint/24011

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