Klasifikasi Tingkat Kematangan Pisang Berdasarkan Ekstraksi Fitur Tekstur dan Algoritme KNN

Kosasih, Rifki

doi:10.22146/jnteti.v10i4.462

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…Sebuah citra memiliki karakteristik unik yang dapat dibedakan dengan objek lainnya yang disebut fitur. Informasi dari sebuah citra diekstrak kemudian digunakan sebagai parameter masukan untuk membedakan antara objek atau citra satu dengan lainnya dalam proses identifikasi atau klasifikasi [5]. Dengan ekstraksi fitur, dapat diekstrak informasi atau fitur dari sebuah gambar yang dapat digunakan sebagai referensi untuk membedakan dengan gambar lain [6].…”

Section: Pendahuluanunclassified

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Asep Zainal Alfarizi,

Enny Itje Sela

2024

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Rhizome is a modification of plant stems that grow under the soil surface and function as a storage place for food reserves. This plants have internodes that function produce new shoots and roots. Rhizomes are commonly used by people as spices in cooking and herbal medicine. Rhizomes have many types, such as ginger, sand ginger, fingerroot, turmeric, galangal, and curcuma. These types have similarities to each other, such as texture, shape, and color. These similarities can cause problems such as difficulty in identifying the type of rhizome. The solution to this problem is a computer system that can classify the type of rhizomes. The system in this research was built using the K-Nearest Neighbor method and Gray Level Co-occurrence Matrix texture feature extraction. Research data amounted to 500 images with ginger, sand ginger, fingerroot, turmeric, and galangal classes. The stages of this research are data collection, image resizing, conversion to grayscale, GLCM feature extraction, storing the extraction results into dataframe, dividing data into train data and test data, classification with K-NN, and implement GUI to make operation easier. Accuracy results on this system get a value of 74% on test data and 64% on train data with value of K=11.

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Section: Pendahuluanunclassified

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Asep Zainal Alfarizi,

Enny Itje Sela

2024

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“…Salah satu cara untuk mengetahui kualitas buah pisang dapat dilihat dari tingkat kematangan pada buah pisang tersebut. Untuk mengetahui tingkat kematangan buah pisang dapat dilihat dari perubahan yang ada pada warna buah [4]. Kematangan buah pisang jika diklasifikasikan dari perubahan warnanya dapat dilihat secara langsung oleh manusia menggunakan mata, tetapi memerlukan lebih banyak waktu jika jumlah buah yang diamati sangat banyak.…”

Section: Pendahuluanunclassified

Implementasi Metode Convolutional Neural Network Untuk Deteksi Kematangan Buah Pisang Menggunakan Inception V3

Wulansari,

Muslih

2023

Journal of Information System

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Pisang merupakan komoditi ekspor yang perlu diperhatikan kualitasnya. Salah satu cara mengetahui kualitasnya dapat dilihat dari tingkat kematangannya. Tingkat kematangan buah pisang tersebut dapat diklasifikasikan dan diidentifikasi oleh mata manusia secara langsung, tetapi dengan jumlah panen yang berlimpah akan memunculkan permasalahahan berhubungan dengan keputusan yang diambil secara cepat bahwa pisang tersebut disimpulkan mentah, matang atau busuk, maka diperlukan cara untuk mengklasifikasikan tingkat kematangan buah pisang dengan bantuan teknologi. Penelitian ini berfokus pada pengklasifikasian tingkat kematangan buah pisang dengan menggunakan arsitektur inception V3 yang merupakan pengembangan dari metode convolutional neural network. Model yang telah dikembangkan dapat menghasilkan akurasi 95% pada proses training dan pada proses evaluasi dengan menggunakan confusion matrix menghasilkan nilai precision sebesar 93%, nilai recall 94% dan nilai akurasi sebesar 94%.

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“…Banana is a fruit that contains vitamins, carbohydrates, and minerals. Banana plants grow in about 150 countries, i.e., India, Brazil, Philippines, Indonesia, China, Ecuador, Cameroon, Mexico, Colombia, and Costa Rica [1], [2]. Bananas are also an alternative fruit for tired people, so they are in great demand by many people.…”

Section: Introductionmentioning

confidence: 99%

Classification of six banana ripeness levels based on statistical features on machine learning approach

Kosasih,

Sudaryanto,

Fahrurozi

2023

IJAAS

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<span>Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.</span>

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Klasifikasi Tingkat Kematangan Pisang Berdasarkan Ekstraksi Fitur Tekstur dan Algoritme KNN

Cited by 4 publications

References 7 publications

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Klasifikasi Rimpang Menggunakan Metode K-Nearest Neighbor dan Ekstraksi Ciri Gray Level Co-occurrence Matrix

Implementasi Metode Convolutional Neural Network Untuk Deteksi Kematangan Buah Pisang Menggunakan Inception V3

Classification of six banana ripeness levels based on statistical features on machine learning approach

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