CLASSIFICATION OF DEFECTS ON PACKING CANS USING LACUNARITY AND NAÏVE BAYES METHODS
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Nov 27, 2021
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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.
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Erwanto, D., Rahayu, P., & Utomo, Y. (2021). CLASSIFICATION OF DEFECTS ON PACKING CANS USING LACUNARITY AND NAÏVE BAYES METHODS. Electro Luceat, 7(2), 142-150. https://doi.org/10.32531/jelekn.v7i2.398
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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[3] B. Sugandi dan S. Dewi, “Sistem Inspeksi Kecacatan pada Kaleng Menggunakan Filter Warna HSL dan Template Matching,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 2, hal. 124–130, 2018.
[4] R. Kusumawardani dan P. D. Karningsih, “Detection and Classification of Canned Packaging Defects Using Convolutional Neural Network,” PROZIMA (Productivity, Optim. Manuf. Syst. Eng., vol. 4, no. 1, hal. 1–11, 2020.
[5] R. E. Plotnick, R. H. Gardner, dan R. V O’Neill, “Lacunarity indices as measures of landscape texture,” Landsc. Ecol., vol. 8, no. 3, hal. 201–211, 1993.
[6] A. R. Backes, “A new approach to estimate lacunarity of texture images,” Pattern Recognit. Lett., vol. 34, no. 13, hal. 1455–1461, 2013.
[7] M. M. Firman Tempola dan A. Khairan, “Perbandingan Klasifikasi Antara KNN dan Naive Bayes pada Penentuan Status Gunung Berapi dengan K-Fold Cross Validation,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, 2018.
[8] M. Muchtar, “Penggabungan fitur dimensi fraktal dan lacunarity untuk klasifikasi daun,” Institut Teknologi Sepuluh Nopember, 2015.
[9] H. Irsyad dan A. Taqwiym, “Community Analysis Sentiment Against Palestinian People with Naive Bayes Classification,” JTECS J. Sist. Telekomun. Elektron. Sist. Kontrol Power Sist. dan Komput., vol. 1, no. 2, hal. 167, Jul 2021.
[10] D. Sartika dan D. I. Sensuse, “Perbandingan algoritma klasifikasi Naive Bayes, Nearest Neighbour, dan Decision Tree pada studi kasus pengambilan keputusan pemilihan pola pakaian,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 3, no. 2, hal. 151–161, 2017.
[11] A. Saleh, “Implementasi metode klasifikasi naive bayes dalam memprediksi besarnya penggunaan listrik rumah tangga,” Creat. Inf. Technol. J., vol. 2, no. 3, hal. 207–217, 2015.
[12] M. Erfan, D. Erwanto, dan P. N. Rahayu, “Ekstraksi Fitur Warna dan Tekstur Pada Kulit Katak Menggunakan Metode Momen Warna dan CCM,” Setrum Sist. Kendali-Tenaga-elektronika-telekomunikasi-komputer, vol. 9, no. 2, 2020.
[13] A. Prabowo, D. Erwanto, dan P. N. Rahayu, “Klasifikasi Kesegaran Daging Sapi Menggunakan Metode Ekstraksi Tekstur GLCM dan KNN,” Electro Luceat, vol. 7, no. 1, hal. 74–81, 2021.
[14] E. Prasetyo, “Data mining mengolah data menjadi informasi menggunakan matlab,” 2019.