Deteksi Citra Uang Kertas dengan Fitur RGB Menggunakan K-Nearest Neighbor

Pratama, Andhika; Mustajib, Muhammad; Nugroho, Aryo

doi:10.30864/eksplora.v9i2.336

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…5000 old & new banknotes. The results obtained that from the 16 test data obtained 15 data were detected correctly, and the accuracy obtained was 93.7% with k = 5 (Pratama, Mustajib and Nugroho, 2020).…”

Section: Introductionmentioning

confidence: 87%

Classification of Arabica Coffee Green Beans Using Digital Image Processing Using the K-Nearest Neighbor Method

Amanina

Saraswati²

2022

JAIS

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Arabica coffee is the largest commodity produced by farmers in Pagergunung Village, Bulu District, Temanggung Regency. Coffee production in recent years has increased rapidly by 80% with the increasing lifestyle of the Indonesian people marked by the number of coffee shop buildings in various regions, and of course the demand for Arabica coffee has also increased, therefore it must improve the quality or quality of the coffee itself. However, in determining and classifying the quality of coffee beans, errors often occur due to the lack of understanding of the farmers in processing coffee. Based on this, the purpose of this research is to classify using the K- Nearest Neighbor method and feature extraction using the average value of Red-Green-Blue (RGB) color in determining the quality and quality of coffee beans according to grade so that they can get a high selling price. In this study using as many as 150 training image data and 150 testing image data, the results of this classification accuracy are 80% using k=1.

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Section: Introductionmentioning

confidence: 87%

Classification of Arabica Coffee Green Beans Using Digital Image Processing Using the K-Nearest Neighbor Method

Amanina

Saraswati²

2022

JAIS

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“…The Confusion Matrix will be used for model evaluation. The number of successful and failed data predicted by the model will be stored in one Cofusion Matrix table (Pratama et al, 2020). The form of the Confusion Matrix can be seen in Table 2.…”

Section: Performance Testmentioning

confidence: 99%

Implementation of Deep Learning to Detect Indonesian Hoax News with Convolutional Neural Network Method

Yoviananda

Fahrudin²

2022

ijeeit

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This study aims to establish and test a model that is used to determine valid news and hoax news. The method used is the Convolutional Neural Network (CNN) method and Word2Vec as embeddings. The research stages consist of data collection, pre-processing, word embeddings, model formation and testing the results obtained. The data used is 958 news. After testing with the distribution of data by 80% as training data and 20% as test data and 5 times epoch, the model that has been formed can determine valid news and hoax news well. In this study, a model with a vector dimension of 400 as input data and a multiple filter size of 3,4,5 became the best model. The resulting accuracy, precision and recall are 0.91. These results are influenced by the selection of the size of the vector dimensions on the output of Word2Vec, the selection of the filter size on the convolution layer and the addition of the Indonesian Wikipedia corpus into the corpus used.

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“…Terdapat beberapa metode yang bisa dilakukan untuk mendeteksi nominal uang. Yaitu Neural Network [8], JST Backpropagation [11], dan K-Nearest Neighbor [1]. Namun beberapa penelitian tersebut memiliki akurasi di bawah 60% dan juga menggunakan hardware berupa Arduino dan sensor TCS3200 untuk pengambilan data.…”

Section: Pendahuluan (Heading 1)unclassified

Deteksi Nominal Uang Kertas Menggunakan OCR (Optical Character Recognition)

Hamidah

Irawan

Hasbullah

et al. 2022

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Instrumen pembayaran yang saat ini umum digunakan diseluruh dunia ialah uang kertas. Saat ini, perkembangan sistem teknologi yang semakin canggih membuat seluruh aktivitas dan pekerjaan yang dilakukan oleh seseorang dapat diselesaikan lebih mudah dan lebih cepat. Salah satu keringanan yang dapat dinikmati oleh masyarakat adalah adanya teknologi atau sistem yang mampu mendeteksi/mengenal nominal dari uang kertas. Pada penelitian ini, penulis mengusulkan metode untuk mendeteksi nominal uang kertas dengan menggunakan OCR (Optical Character Recognition). Tahapan-tahapan untuk mendeteksi nominal dari citra uang adalah Resize, Cropping, Rotasi Citra, Grayscale, Filter, Biner, Morfologi, dan OCR. Total dataset uang kertas yang digunakan sebanyak 70 lembar uang dengan pecahan Rp 1,000,00, Rp 2,000,00, Rp 5,000,00, Rp 10,000,00, Rp 20,000,00, Rp 50,000,00, dan Rp 100,000, Dari Dari hasil penelitian yang dilakukan menggunakan 70 sampel uang kertas, didapatkan hasil tingkat akurasi deteksi citra uang kertas yang benar sebesar 94%, dengan tingkat akurasi error sebesar 6%.

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Deteksi Citra Uang Kertas dengan Fitur RGB Menggunakan K-Nearest Neighbor

Cited by 9 publications

References 6 publications

Classification of Arabica Coffee Green Beans Using Digital Image Processing Using the K-Nearest Neighbor Method

Classification of Arabica Coffee Green Beans Using Digital Image Processing Using the K-Nearest Neighbor Method

Implementation of Deep Learning to Detect Indonesian Hoax News with Convolutional Neural Network Method

Deteksi Nominal Uang Kertas Menggunakan OCR (Optical Character Recognition)

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