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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Siti Aminah
Institut Teknologi Pagaralam, Indonesia
Computer Science & IT Control & Systems Engineering

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Penerapan Algoritma K-Nearest Neighbor (KNN) Pada Klasifikasi Kualitas Biji Kopi Robusta Putri Ayu Lestari; Desi Puspita; Siti Aminah; Y Yadi
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.370

Abstract

The aim of this research is to produce a quality classification system for robusta coffee beans using the k-nearest neighbor algorithm (knn). This research is based on the process of classifying the quality of robusta coffee beans which is carried out conventionally and has not been computerized, namely the classification of coffee beans still uses the selection method of color and cleanliness of the beans. This of course takes a long time and errors often occur, so this research can help classify the quality of Robusta coffee beans using the k-nearest neighbor (knn) algorithm. The system was built using MATLAB software, which is used in this research. is the SDLC (Software Development Life Cycle) method, the stages of this research include analysis, design, coding and testing, for the testing method using a confusion matrix which is divided into 2, namely training data and test data. The results of this research are a seed quality classification system robusta coffee using the k-nearest neighbor (knn) algorithm with data used 80 images for training and 10 images for testing. It can be concluded that RGB extraction and the k-nearest neighbor (knn) method can be applied to classify the quality of robusta coffee beans from 10 test data Accuracy was obtained at 70 with image processing in coffee business management in Bandar Village, Pagaralam City, South Dempo District.
Co-Authors Desi Puspita Putri Ayu Lestari Yadi