IMAGE CLASSIFICATION OF LOCAL ROBUSTA AND ARABICA COFFEE SEEDS IN MALANG REGENCY USING GRAY LEVEL CO-OCCURRENCE MATRIX AND K-NEAREST METHODS

Authors

  • Devita Widiawati Dian Nuswatoro University Kediri
  • Muhammad Rijalun Shodaqu Dian Nuswatoro University Kediri
  • Gilang Priambodo Dian Nuswatoro University Kediri
  • Maulana Fajar Anas Dian Nuswatoro University Kediri
  • Titien Suhartini Sukamto Dian Nuswatoro University Kediri
  • Aris Nurhindarto Dian Nuswatoro University Kediri

DOI:

https://doi.org/10.33633/jais.v7i3.7214

Abstract

Coffee is one type results current plantation  this favored by some among. Indonesia is in the order to four Becomes Robusta coffee export and producer in the world. Appearance communities coffee lovers make coffee as provider field profession for part big resident. In Indonesia, especially in the Regency of Trunk, a lot very Public around who has coffee plantations including namely Robusta coffee and Arabica coffee (coffea arabica) local. For some new people Do you know and love coffee yet? can differentiate type of coffee visually. In the era of increasingly digitalization, advanced like this. There are several method for differentiate something object among them that is processing digital image. Frequent problems occur that is many less consumers in determine Robusta and Arabica coffee types. From trouble that, then researcher designing a system classification on robusta and Arabica coffee beans could obtained with implementation algorithm K-Nearest Lightweight Classification (K-NN). [1] combined with extraction feature Gray Level Co-Occurrence Matrix (GLCM). Digital image dataset used that is a total of 194 pictures where inside it there is type image coffee beans. Image dataset Robusta and Arabica coffee beans each local number of 97 images. Image dataset shared into 20 test data and 174 training data. Testing conducted using Matlab software produce score accuracy highest at distance pixels=1 and the value of K=1 with respect to angle of 45° by 95%.

References

J. Zeniarja, A. Ukhifahdhina, and A. Salam, “Diagnosis Of Heart Disease Using K-Nearest Neighbor Method Based On Forward Selection,” 2019.

V. Sari, F. Firdausi, and Y. Azhar, “Comparison Prediction Arabica Coffee Quality with Use SGD Algorithm, Random Forest and Naive Bayes,” Edumatic : Journal of Informatics Education, vol. 4, no. 2, pp. 1–9, Dec. 2020, doi : 10.29408/edumatic.v4i2.2202.

A. Rachmad Syulistyo and V. Meliana Agustin, “Predicting News Article Popularity with Multi Layer Perceptron Algorithm,” 2022. [Online]. Available: https://www.kaggle.com/waseemakramkhan/the-tribune-news-articles.

E. Hari Rachmawanto, C. Atika Sari, H. Pramono, and W. Shinta Sari, “Visitor Prediction Decision Support System at Dieng Tourism Objects Using the K-Nearest Neighbor Method,” 2022.

YN Yaspin, DW Widodo, and J. Sulaksono, “ Classification Clove Flower Quality for Increase Quality With Utilization Gray Level Co -Occurence Matrix (GLCM) characteristics.”

“4”.

D. Care Khrisne and D. Putra, "Automatic Image Annotation Using Block Truncation Method and K-Nearest Neighbor,” vol. 4, no. 1, 2013.

“Implementation K-Nearest Neighbor Algorithm As Classification Decision Support Receiver PPA and BBM Scholarships Sumarlin STIKOM Uyelindo Kupang .

C. Atika Sari and E. Hari Rachmawanto , “Sentiment Analyst on Twitter Using the K-Nearest Neighbors (KNN) Algorithm Against Covid-19 Vaccination,” 2022.

D. Ikhsan , E. Utami , and FW Wibowo, "METHOD OF CLASSIFICATION OF QUALITY GREENBEAN ARABIKA LANANG COFFEE AND USUALLY USING K-NEAREST NEIGHBOR BASED ON SHAPE," Journal SINUS SCIENCE, vol. 18, no. 2, p. 1, Jul. 2020, doi : 10.30646/sinus.v18i2.456.

D. Aditya Nugraha and A. Sartika Wiguna, “COLOR FEATURE SELECTION OF COFFEE BEAN DIGITAL IMAGE USING PRINCIPAL COMPONENT ANALYSIS METHOD Digital Image Selection of Coffee Seed Using Component Analysis Method,” 2020.

HP Hadi and H. Rachmawanto, “Extraction Feature Analysis Features and Colors In Classification Process Maturity K-Nearest Neighbor Based Rambutan Fruit,” SKANIKA: System Computer and Informatics Engineering, vol. 5, no. 2, pp. 177–189, 2022.

HP Hadi and EH Rachmawanto, “JIP (Journal Informatics Polynema) COLOR FEATURE EXTRACTION AND GLCM IN KNN ALGORITHM FOR CLASSIFICATION OF HAIR Maturity”.

“garuda1429115”.

Y. Prastyaningsih, W. Kusrini, P. Tanah Laut, JA Yani KM, D. Stage KecPelaihari Land District Laut, and South K., “ System Image Retrieval at the Coffee Bean Roasting Level Using Color Feature Extraction,” vol. 6, no. 2, p. 2021.

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Published

2022-12-28

Issue

Vol. 7 No. 3 (2022): Journal of Applied Intelligent System

Section

Articles

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