Klasifikasi Buah Zaitun Menggunakan Convolution Neural Network

Antoko, Toton Dwi; Ridani, Muhammad Azhar; Minarno, Agus Eko

doi:10.34010/komputika.v10i2.4475

Cited by 7 publications

(10 citation statements)

References 10 publications

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“…Fase penilaian eksposur model merupakan hasil dari fase pelatihan yang menggunakan data baru sebagai data uji. Hasil dari fase ini adalah tingkat akurasi/kinerja model saat memprediksi data kelas yang tidak diketahui, khususnya data uji [3], [9].…”

Section: Klasifikasiunclassified

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Optimasi Klasifikasi Buah Anggur Menggunakan Data Augmentasi dan Convolutional Neural Network

Nana¹,

Mulyana²,

Akbar³

et al. 2022

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Anggur adalah buah yang populer dan dapat dengan mudah ditemukan hampir di mana saja di dunia. Banyak yang akan terkagum-kagum dengan rasa manis dan nikmat dari buah anggur ini. Anggur tidak hanya membawa kelezatan yang luar biasa untuk kita semua, tetapi juga membawa manfaat khusus bagi kesehatan manusia. Oleh karena itu, peneliti mencoba membuat program pengenalan citra buah anggur yang menggunakan algoritma Data Augmentation dan Convolutional Neural Network. Ini adalah aktivitas konvolusi yang menggabungkan beberapa pemrosesan persiapan dengan beberapa komponen yang bergerak bersama melalui sistem sensor biologis. Anggur yang digunakan adalah Champagne, Concord, Cotton Candy, Chris Monceedless, Gewürztraminer, Grenora, Kyoho, Moondrops, Pinot Noir, Riesling, Sultana, Sweet Jubilee dan Valiant. Optimalisasi klasifikasi dilakukan pada citra buah anggur menggunakan dua model pengujian yaitu model Sequential dan model on-top VGG16 yang beroperasi pada website aplikasi Google Collaboratory dan Keras. Data pengujian untuk observasi ini pada data latih sebanyak 2400 citra dan data uji sebanyak 480 citra yang menghasilkan nilai untuk model sequential dengan akurasi sebesar 98,54% dan loss sebesar 0,027%, untuk model on-top VGG16 nilai akurasinya adalah 99,37% dan nilai loss hanya 0,029%.

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Section: Klasifikasiunclassified

“…Formulir ini mengecilkan peta fitur ke ukuran aslinya 75%. Metode pooling yang digunakan adalah Max Pooling [9]. Gambar 3.…”

Section: 2 Lapisan Poolingunclassified

Optimasi Klasifikasi Buah Anggur Menggunakan Data Augmentasi dan Convolutional Neural Network

Nana¹,

Mulyana²,

Akbar³

et al. 2022

Smart Comp

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“…The convolution and max pooling processes are carried out several times to get a feature map of the desired size. This feature map will be the input to the Fully Connected Layer process which has been converted into a onedimensional array for classification [19].…”

Section: Max Poolingmentioning

confidence: 99%

“…Flatten is important in classifying. If the flattening process is not carried out, the results from the convolution process cannot be processed by the fully connected layer [19].…”

Section: Flattenmentioning

confidence: 99%

Classification of Geometric Batik Motif Typical of Indonesian Using Convolutional Neural Network

et al. 2022

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Batik is a world heritage from Indonesia which is a characteristic of Indonesian culture. On October 2, 2009 batik has been awarded as a cultural heritage from UNESCO. Indonesia has 5.849 batik patterns from Aceh to Papua. The ability to recognize batik cloth patterns is certainly quite difficult and only owned by certain people who have expertise. One way to identify batik patterns is by using a pattern recognition classification method based on quantitative measurements of the main features or characteristics of an object. Deep Learning is one solution to detect batik patterns automatically. One of deep learning methods that can classify patterns of batik patterns is Convolutional Neural Network (CNN). CNN is able to group and detect objects in the image automatically by accepting input data with a size of m×n. CNN uses image input through a convolution layer and be processed according to the specified filter. Each layer produces a pattern from several parts of the image that facilitates the classification process. This study uses the CNN method and obtains the average value of 96% accuracy, 96,78% precision, 96,74% recall, and 96,74%.

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“…The model exposure assessment phase is the result of the training phase using the new data as test data. The result of this phase is the level of accuracy/performance of the model when predicting unknown class data, especially test data (Antoko, et al, 2021;Hanafi et.al, 2019;Kartika Wisnudhanti, 2020).…”

Section: Introductionmentioning

confidence: 99%

Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods

Malau¹,

Mulyana²

2022

JAETS

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Edelweiss is a plant that grows at a height, and is known as a perennial flower because it has beautiful petals and does not wilt easily. Although edelweiss in Indonesia is still in the same family as Leontopodium Alpinum, it turns out that the type of edelweiss found in the mountains of Indonesia is different from edelweiss found abroad. Therefore, in this study, an image processing system was developed that can classify the types of edelweiss flowers based on their image using Linear Discriminant Analysis to classify data into several classes based on the boundary line (straight line) obtained from linear equations. In this study, the types of edelweiss flowers used in this study were Anaphalis Javanica and Leontopodium Alpinum, the two types of edelweiss flowers were distinguished based on their color characteristics using hue and saturation values. The images used are 1500 images for training data and 450 test data images with a training and test data ratio of 70:30, so that the accuracy produced in the testing process is 99.77% in the Linear Discriminant Analysis method.

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Klasifikasi Buah Zaitun Menggunakan Convolution Neural Network

Cited by 7 publications

References 10 publications

Optimasi Klasifikasi Buah Anggur Menggunakan Data Augmentasi dan Convolutional Neural Network

Optimasi Klasifikasi Buah Anggur Menggunakan Data Augmentasi dan Convolutional Neural Network

Classification of Geometric Batik Motif Typical of Indonesian Using Convolutional Neural Network

Classification of Edelweiss Flowers Using Data Augmentation and Linear Discriminant Analysis Methods

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