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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Ulfa Laela R
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Computer Science & IT

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Aplikasi Citra Digital untuk Klasifikasi Kematangan Buah Pepaya Ulfa Laela R; Asriayani Ismail; Raden Wirawan; Najirah Umar; Rahmat Hidayat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

There are still many people who use the manual system by looking directly at or guessing the maturity level of papaya fruits, with classification results that are less than optimal. The purpose of this study is to design a digital image application that can be useful in distinguishing the ripeness of naturally ripe papaya fruits and carbites. This application uses 2 extraction features, namely texture and colour, for texture uses Grey Level Cooccurence Matrks (GLCM), while colour uses Hue, Saturation, Value (HSV) and K-Nearest Neighbour (K-NN) algorithm to classify. The result of this study is the creation of a digital image application, which can help distinguish the ripeness of papaya fruit from the results of the classification of natural ripe and ripe carbine. Based on the test results, 60 training data and 15 testing data were used. By producing an accuracy value of 80% on each testing data that has been carried out.
Kombinasi Algoritma KNN, HSV dan LBP Pada Pengolahan Citra Digital untuk Membedakan Kematangan Pisang Mirfan; Sudriawan; Ulfa Laela R; Mila Jumarlis
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The use of carbide in ripening bananas can result in chemical contamination of bananas. This can have a negative impact on the health of consumers who consume these bananas, so this research aims to design a system to differentiate between naturally ripe bananas and carbonated ripe bananas with Digital Images using the Hue Saturation Value (HSV), Local Binary Patterns (LBP) and K-Nearest Neighbor (K-NN). In this research, the system development used is UML (Unified Modeling Language). Meanwhile, making software in this system uses PHP, HTML, CSS, Java script software and for the database uses MySql. This research collects data obtained through observation, interviews and literature study. The method used to create this system is Hue Saturation Value (HSV) to extract color features, Local Binary Patterns (LBP) to extract texture features and the K-Nearest Neighbor (K) algorithm. -NN) for classification of plantain types. The digital image classification system distinguishes naturally ripe or carbitant plantains and can display classification results well so that it can help the public in distinguishing naturally ripened bananas or carbitants. The system created has been able to implement the Hue Saturation Value (HSV), Local Binary Patterns (LBP), and K-Nearest Neighbor (KNN) methods well and the system can differentiate naturally ripe bananas and carbonates well with a level of accuracy for the k= value 3, namely 100%, k=5 96.67%, k=7 93.33% and k=9 with an accuracy of 96.67% from 30 testing data using 200 training data.
Co-Authors Asriayani Ismail Mirfan Najirah Umar Raden Wirawan Rahmat Hidayat Sudriawan