Klasifikasi Kematangan Buah Apel Anna Berdasarkan Ekstraksi ::Fitur Warna dan Tekstur dengan Model Support Vector Machine ::dan K-Nearest Neighbour

Taribi, Suta Ilmidani and Dr. Dimas Firmanda Al Riza, ST. M.Sc. and Darmanto, ST. MT (2024) Klasifikasi Kematangan Buah Apel Anna Berdasarkan Ekstraksi ::Fitur Warna dan Tekstur dengan Model Support Vector Machine ::dan K-Nearest Neighbour. Sarjana thesis, Universitas Brawijaya.

Abstract

Apel anna merupakan salah satu varietas apel yang di budidayakan di daerah Malang. Tingkat kematangan apel anna dapat ditentukan dengan mutu eksternal seperti warna kulit dan tekstur kulit, maupun internal seperti kadar sari buah dan kadar buah. Ada beberapa cara yang dapat dilakukan dalam penentuan tingkat kematangan buah, salah satunya menggunakan ekstraksi fitur warna yaitu RGB, HSV, dan L*a*b serta ekstraksi fitur tekstur metode Gray Level Co-occurrence Matrix (GLCM). Tujuan dari penelitian ini yaitu untuk membangun pemodelan machine learning yang memiliki keakuratan tinggi dalam mengklasifikasi tingkat kematangan buah apel anna. Dimana apel anna akan diklasifikasikan melalui ekstraksi fitur warna dan tekstur. Penelitian dilakukan dengan dua tahap yaitu uji non-destruktif dan destruktif. Uji non-destruktif dilakukan dengan akuisisi citra objek menggunakan kamera handphone. Uji destruktif dilakukan dengan pengukuran total padatan terlarut. Proses pengolahan data dilakukan dengan menggunakan model SVM dan K-NN pada algoritma machine learning. Setelah dilakukan analisis, didapatkan hasil akurasi dari dua pemodelan bahwa model SVM memiliki hasil akurasi yang tertingg

English Abstract

Anna apples are one of the apple varieties cultivated in the Malang area. The maturity level of Anna apples can be determined by external qualities such as skin color and skin texture, as well as internal qualities such as juice content and sugar content. There are several ways that can be used to determine the level of ripeness of fruit, one of which is using color feature extraction, namely RGB, HSV, and L*a*b as well as texture feature extraction using the Gray Level Co-occurrence Matrix (GLCM) method.. The aim of this research is to build a machine learning model that has high accuracy in classifying the maturity level of Anna apples. Where Anna apples will be classified through the extraction of color and texture features. The research was carried out in two stages, namely non-destructive and destructive testing. Non-destructive testing is carried out by acquiring object images using a cellphone camera. Destructive tests are carried out by measuring total dissolved solids. Data processing is carried out using SVM and K-NN models in machine learning algorithms. After carrying out the analysis, the accuracy results from the two models were obtained that the SVM model had the highest accuracy results.

Item Type:	Thesis (Sarjana)
Identification Number:	052410
Uncontrolled Keywords:	Kematangan apel anna, citra digital, SVM, K-NN
Divisions:	Fakultas Teknologi Pertanian > Keteknikan Pertanian
Depositing User:	Unnamed user with username nova
Date Deposited:	12 Sep 2024 05:33
Last Modified:	12 Sep 2024 05:33
URI:	http://repository.ub.ac.id/id/eprint/226002

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