The quality of coffee bean classification system based on color by using k-nearest neighbor method

Adiwijaya, Nelly Oktavia; Romadhon, Hammam Iqomatuddin; Putra, Januar Adi; Kuswanto, Dewangga Putra

doi:10.1088/1742-6596/2157/1/012034

Cited by 6 publications

(3 citation statements)

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“…This study discusses the K-Nearest Neighbor method in classifying the quality of coffee beans by class using 90 training data from 3 classes and 30 test data from each class. Accuracy results with k = 3, k = 5, and k = 7 are the same, namely 83% (Adiwijaya et al, 2022).…”

Section: Introductionmentioning

confidence: 72%

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: 72%

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

Amanina

Saraswati²

2022

JAIS

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“…In this study, the data was divided into three groups, with a total of 90 coffee beans, with each group totaling 30 coffee beans. The experimental results show an accuracy rate of 83%, there is no room to improve the quality of this research (Adiwijaya et al, 2022) Furthermore, research discusses the classification of coffee bean quality using the Convolutional Neural Network (CNN) method. In this research, a mobile application was developed using a model-based model to classify the quality of coffee beans automatically via a cellphone camera and using the ResNet-152 and VGG16 architecture.…”

Section: Literature Reviewmentioning

confidence: 94%

Classification of types Roasted Coffee Beans using Convolutional Neural Network Method

Metha,

Kusrini,

Ariatmanto

2024

SinkrOn

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In the current digital era, the role of technology in the agricultural industry is very necessary to increase yields which can have an impact on the productivity and welfare of farmers. Coffee is a drink that has been very popular for many years. Due to the high demand for coffee beans, this research aims to develop a system that can classify types of roasted coffee beans based on images using the Convolution Neural Network (CNN) method. Coffee bean processing is the most important stage in the coffee industry, classifying coffee beans often requires more in-depth knowledge and extensive experience regarding coffee beans. Therefore, this system can be a more effective solution. The author collects a dataset containing types of roasted coffee beans, then the Convolutional Neural Network (CNN) can analyze in the form of visual patterns each type of coffee bean. This implementation is expected to help the coffee industry identify coffee beans quickly and accurately.

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“…Other studies that addressed coffee classification issues, regardless of the approach, include but are not limited to [38][39][40][41][42][43][44][45].…”

Section: Electronic Nosementioning

confidence: 99%

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

et al. 2023

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This study investigates the classification of Arabic coffee into three major variations (light, medium, and dark) using simulated data gathered from the actual measurements of color information, antioxidant laboratory testing, and chemical composition tests. The goal is to overcome the restrictions of limited real-world data availability and the high costs involved with laboratory testing. The Monte Carlo approach is used to generate new samples for each type of Arabic coffee using the mean values and standard deviations of publicly available data. Using these simulated data, multiple machine-learning algorithms are used to classify Arabic coffee, while also investigating the importance of features in identifying the key chemical components. The findings emphasize the importance of color information in accurately recognizing Arabic coffee types. However, depending purely on antioxidant information results in poor classification accuracy due to increased data complexity and classifier variability. The chemical composition information, on the other hand, has exceptional discriminatory power, allowing faultless classification on its own. Notably, particular characteristics like crude protein and crude fiber show high relationships and play an important role in coffee type classification. Based on these findings, it is suggested that a mobile application be developed that uses image recognition to examine coffee color while also providing chemical composition information. End users, especially consumers, would be able to make informed judgments regarding their coffee preferences.

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The quality of coffee bean classification system based on color by using k-nearest neighbor method

Cited by 6 publications

References 0 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

Classification of types Roasted Coffee Beans using Convolutional Neural Network Method

Machine Learning Classification of Roasted Arabic Coffee: Integrating Color, Chemical Compositions, and Antioxidants

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