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All Journal Jurnal Informatika dan Teknik Elektro Terapan Electro Luceat INAJEEE (Indonesian Journal of Electrical and Electronics Engineering) SENTRI: Jurnal Riset Ilmiah
Putri Nur Rahayu
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Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice

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Journal : Electro Luceat

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Klasifikasi Kesegaran Daging Sapi Menggunakan Metode Ekstraksi Tekstur GLCM dan KNN Ade Prabowo; Danang Erwanto; Putri Nur Rahayu
Electro Luceat Vol 7 No 1 (2021): Electro Luceat (JEC) - July 2021
Publisher : LPPM Poltek ST Paul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jelekn.v7i1.344

Abstract

Meat is the soft part of the animal that is covered by skin and is attached to the bones which become food ingredients. This research was conducted to classify the types of fresh, inn and rotten beef using 120 samples of beef taken directly by the researcher. Before classifying the type of beef, the texture of the beef image was extracted using the GLCM method to produce texture parameters in the form of contrast, correlation, homogeneity and energy. Texture parameters are classified using the KNN method. The results in this study indicate that the extraction of beef image texture using the GLCM method can produce various values on the 4 parameters of the GLCM texture. In addition, the results of the classification of beef freshness using the KNN method to determine 3 types of meat quality, namely fresh, cooked and rotten beef, obtained an evaluation of the classification performance using the Confusion Matrix table with an Accuracy value of 0.82, Precision of 0.83, Recall of 0.82 and F-Measure of 0.82. So that the parameters of the beef image texture using the GLCM method can be classified properly using the KNN method.
KLASIFIKASI CACAT PADA KALENG KEMASAN MENGGUNAKAN METODE LACUNARITY DAN NAÏVE BAYES Danang Erwanto; Putri Nur Rahayu; Yudo Bismo Utomo
Electro Luceat Vol 7 No 2 (2021): Electro Luceat (JEC) - November 2021
Publisher : LPPM Poltek ST Paul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32531/jelekn.v7i2.398

Abstract

Cans are steel sheets coated with tin (Sn) and used to package food and beverage products. The use of cans as packaging for food products because cans are difficult for microorganisms to pass and cannot be penetrated by ultraviolet light so that the quality of packaged food or beverage products is maintained. The cans selected as packaging must be in a non-defective condition so that an inspection process is needed on the cans. This research implements the Lacunarity and Naïve Bayes Classification methods to classify the types of cans which are grouped into 2 classes, namely Good and Reject. From the implementation of the Lacunarity method, it is able to produce 28 values of texture feature extraction that vary in each image. The results of the evaluation of the classification of the Naïve Bayes Classification method to classify the condition of packaged cans obtained an accuracy value of 0.87, a precision of 0.88, a recall of 0.86 and an f-measure of 0.87, so that the Naïve Bayes Classification method can classify the types of cans packaging in Good and Reject condition based on the value of texture extraction using the Lacunarity method.
Co-Authors Abda Abda Ade Prabowo Adianto Aldi Febriansyah Ardhan, Mohammad Ardhan Fawwaz Augustino, Immanuel Freddy Bone, Iskia Ipan Dua’ Brian, Thomas Erwanto, Danang Hasin, Muhammad Khoirul Muhammad Khoirul Hasin Pujiputra, Anggarjuna Puncak Rinanto, Noorman Yudo Bismo Utomo