Klasifikasi Tingkat Kematangan Buah Pepaya Berdasarkan Fitur Warna Menggunakan JST

Firlansyah, Alfian; Kaswar, Andi Baso; Risal, Andi Akram Nur

doi:10.36805/technoxplore.v6i2.1438

Cited by 3 publications

(4 citation statements)

References 7 publications

(17 reference statements)

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“…Sistem untuk melakukan klasifikasi pada objek berbasis pengolahan citra digital dengan menggunakan metode yang berbeda sudah banyak digunakan dalam penelitian sebelumnya seperti, metode jaringan saraf tiruan [9][10] [13], metode K-Nearest Neighbors [11] [14], metode Fuzzy Mamdani [12].…”

Section: Pendahuluanunclassified

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Classification of the Level of Sugar Content in Papaya Fruit Based on Color Features Using Artificial Neural Network

Nurfitri,

Kaparang,

Hidayat

et al. 2023

J. Tek. Inform. (JUTIF)

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Papaya (Carica papaya L) is consumed by many people because it is beneficial for health. Along with increasing consumption or enthusiasts of papaya, the quality of papaya needs to be considered. One of the determining factors of the quality of papaya is its physical characteristics, which can be seen from its color, shape, and texture. Papaya of good quality has a delicious and sweet taste. The sweet taste of papaya is certainly influenced by the sugar content contained in it. However, to determine the sugar content in papaya is only done by human assessment based on its physical characteristics, this assessment is often less accurate. With a system that can determine the sugar content in papaya, it will make it easier for farmers to sort papaya fruit. Therefore, in this study, it is proposed to classify the level of sugar content in papaya based on color features using an Artificial Neural Network. The proposed method consists of 5 stages, namely, image acquisition, preprocessing, segmentation with the Otsu method, morphological operations, and classification with artificial neural networks. The number of papaya datasets used is 300 images which are divided into 3 classes, low class, medium class, and tal class. Based on the results of the tests that have been carried out, an accuracy of 92.85% is obtained for the training data, and for the test data, an accuracy of 100% is obtained. These results indicate that the proposed method can classify the level of sugar content in papaya fruit accurately.

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Section: Pendahuluanunclassified

“…Penelitian selanjutnya untuk mengetahui kematangan buah pepaya dengan metode Jaringan Saraf Tiruan (JST) yang diklasifikasikan dari karakteristik warnanya [13]. Hasil dari penelitian ini diperoleh akurasi dengan nilai 100%.…”

Section: Pendahuluanunclassified

Classification of the Level of Sugar Content in Papaya Fruit Based on Color Features Using Artificial Neural Network

Nurfitri,

Kaparang,

Hidayat

et al. 2023

J. Tek. Inform. (JUTIF)

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“…Misalnya dalam membedakan tingkat kematangan buah. Pada umumnya, dalam membedakan tingkat kematangan buah pisang dapat ditentukan hanya dengan melihat warna buahnya [2]. Namun.…”

Section: Pendahuluanunclassified

Klasifikasi Jenis Buah Pisang Berdasarkan Citra Warna dengan Metode SVM

et al. 2022

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Indonesia kaya dengan berbagai macam tanaman diantaranya buah pisang yang mempunyai beragam jenis. Untuk mengetahui tingkat kematangan pada suatu jenis pisang biasanya dilihat dari warna, akan tetapi karena faktor usia dan kelelahan dari para petani biasanya sering terjadi kesalahan pada saat pengukuran akurasi. Penelitian ini bertujuan untuk mengklasifikasi jenis pisang berdasarkan citra warna dengan menggunakan algoritma Support Vector Machine (SVM). Data yang digunakan adalah citra pisang dengan total 1256, yang diklasifikasi menjadi 2 jenis pisang, Pisang ambon dan Pisang lady finger. Hasil yang didapat dari penelitian ini ditunjukan oleh confusion matrix dengan nilai True Positive (TP) = 0,82 dan False Positive (FP)= 0,18. serta False Negative (FN) = 0,02 dan True Negative (TN) = 0,98. Dari nilai yang ditunjukan oleh confusion matrix dapat diartikan bahwa algoritma SVM cukup baik digunakan dalam mengklasifikasi jenis pisang sehingga dapat memberikan jaminan mutu atas produk yang dihasilkan oleh petani.

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“…Karena faktor usia atau kesehatan mata tersebut, biasanya dapat berpengaruh pada penglihatan menjadi buram atau mungkin tidak akurat, dan membutuhkan upaya ekstra untuk membedakan antar objek. Selain itu juga tingkat pemahaman setiap manusia dalam mengidentifikasi kualitas beras tentu berbeda-beda [5]. Berdasarkan masalah tersebut, maka diperlukan sistem pengklasifikasian tingkat kualitas beras.…”

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Classification of Rice Quality Levels Based on Color and Shape Features Using Artificial Neural Network Based on Digital Image Processing

Asnidar,

Perdana,

Ilham

et al. 2023

J. Tek. Inform. (JUTIF)

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Rice is the staple food of most Indonesians. In identifying the quality of rice, it can be seen from physical characteristics such as the color and shape of rice, because these characteristics can make an object can be identified properly and clearly. In general, what is done in determining the quality of rice by looking at its color and shape. But usually the human eye in identifying objects is sometimes less accurate which is influenced by several factors, such as age. So, several studies were conducted that tried to solve the problem by using digital image processing. However, the accuracy results obtained are still not accurate, because the datasets used in the previous study were relatively small, namely around 80 images, although the average level of accuracy obtained was quite high, but the number of datasets used was very small so that the level of accuracy was still inaccurate. Therefore, in this study, it is proposed that the title of classification of rice quality levels using JST based on digital image processing which divides rice into 3 classifications, namely, good, good enough, and not good where in this study using 330 digital images to produce a more accurate level of accuracy. In this study, there are several stages, namely, image retrieval, preprocessing, segmentation, morphological, feature extraction, and classification using artificial neural networks. Based on the research conducted, training accuracy was produced with an average accuracy of 98,75% while the test accuracy was produced with an average accuracy of 98,89%.

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Klasifikasi Tingkat Kematangan Buah Pepaya Berdasarkan Fitur Warna Menggunakan JST

Cited by 3 publications

References 7 publications

Classification of the Level of Sugar Content in Papaya Fruit Based on Color Features Using Artificial Neural Network

Classification of the Level of Sugar Content in Papaya Fruit Based on Color Features Using Artificial Neural Network

Klasifikasi Jenis Buah Pisang Berdasarkan Citra Warna dengan Metode SVM

Classification of Rice Quality Levels Based on Color and Shape Features Using Artificial Neural Network Based on Digital Image Processing

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