Naïve Bayes Classifier untuk Klasifikasi Cacat Biji Kopi Berdasarkan Warna dan Tekstur
DOI:
https://doi.org/10.22441/incomtech.v13i2.17307Kata Kunci:
Naïve Bayes, HSV, GLCM, KopiAbstrak
Pemilahan cacat biji kopi merupakan proses yang sangat penting untuk menjaga serta meningkatkan kualitas produksi, melihat kopi sebagai salah satu komoditas paling penting yang diperjual belikan. Penulis ingin meminimalisir kesalahan klasifikasi oleh manusia yang subjektif dengan mengimplementasi metode Naive Bayes untuk melakukan klasifikasi cacat biji kopi secara objektif. Biji kopi difoto sehingga menghasilkan citra biji kopi, ruang warna HSV digunakan untuk melakukan ekstraksi ciri warna biji, dan tekstur biji kopi diekstrak dengan metode GLCM. Pengujian terhadap model klasifikasi yang dibangun dengan 68 data latih menghasilkan akurasi 94.44% berdasarkan 36 data uji. Hasil akurasi menunjukkan ketika ada 36 data uji maka 2 data salah diklasifikasi atau ketika ada 100 data uji maka 5 hingga 6 biji akan salah diklasifikasi oleh model. Penelitian selanjutnya disarankan untuk melakukan pengamatan yang lebih dalam untuk mendapatkan fitur ciri yang dapat merepresentasikan perbedaan cacat pada biji dengan lebih representative, serta membandingkan metode klasifikasi Naive Bayes dengan metode klasifikasi lain untuk mendapatkan model klasifikasi yang lebih baik di masa depan.Unduhan
Referensi
M. Saputra, M. P. Kurniawan, dan M. T. Informatika, “Identifikasi Mutu Biji Kopi Arabika Berdasarkan Cacat,” J. Teknol. Inf. dan Komun., vol. 10, no. 1, hal. 27–35, 2020.
D. Giacalone, T. K. Degn, N. Yang, C. Liu, I. Fisk, dan M. Münchow, “Common roasting defects in coffee: Aroma composition, sensory characterization and consumer perception,” Food Qual. Prefer., vol. 71, no. March, hal. 463–474, 2019, doi: 10.1016/j.foodqual.2018.03.009.
N. Bhumiratana, K. Adhikari, dan E. Chambers, “Evolution of sensory aroma attributes from coffee beans to brewed coffee,” LWT - Food Sci. Technol., vol. 44, no. 10, hal. 2185–2192, 2011, doi: 10.1016/j.lwt.2011.07.001.
M. Garcia, J. E. Candelo-Becerra, dan F. E.Hoyos, “applied sciences Quality and Defect Inspection of Green Co ff ee Beans Using a Computer Vision System,” 2019.
P. Vithu dan J. A. Moses, “Machine vision system for food grain quality evaluation: A review,” Trends Food Sci. Technol., vol. 56, hal. 13–20, 2016, doi: 10.1016/j.tifs.2016.07.011.
J. Aramiko, “KLASIFIKASI KERUSAKAN BIJI KOPI MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER SKRIPSI,” 2020.
R. E. Angelia, K. C. R. Cavan, K. E. Recto, dan R. B. Bactat, “Dried Robusta Coffee Bean Quality Classification Using Convolutional Neural Network Algorithm,” ACM Int. Conf. Proceeding Ser., hal. 57–61, 2021, doi: 10.1145/3467707.3467715.
P. S. Maria dan M. Rivai, “Klasifikasi Kualitas Biji Kopi Menggunakan Pengolahan Citra dan Fuzzy Logic,” Semin. Nas. Menggagas Kebangkitan Komod. Unggulan Lokal Pertan. dan Kelaut., 2013.
I. Kurniastuti, E. N. I. Yuliati, F. Yudianto, dan T. D. Wulan, “Determination of Hue Saturation Value (HSV) color feature in kidney histology image,” J. Phys. Conf. Ser., vol. 2157, no. 1, 2022, doi: 10.1088/1742-6596/2157/1/012020.
F. Q. Chen dan Y. P. Zhou, “Color feature extraction of Hainan Li brocade image based on RGB and HSV,” 2015 12th Int. Comput. Conf. Wavelet Act. Media Technol. Inf. Process. ICCWAMTIP 2015, hal. 214–219, 2016, doi: 10.1109/ICCWAMTIP.2015.7493978.
P. Ganesan, V. Rajini, B. S. Sathish, dan K. B. Shaik, “HSV color space based segmentation of region of interest in satellite images,” 2014 Int. Conf. Control. Instrumentation, Commun. Comput. Technol. ICCICCT 2014, hal. 101–105, 2014, doi: 10.1109/ICCICCT.2014.6992938.
R. C. Gonzales dan R. E. Woods, Digital image processing (second edition), vol. 8, no. 1. 2002.
R. A. Surya, A. Fadlil, dan A. Yudhana, “Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix ( GLCM ) dan Filter Gabor untuk Klasifikasi Citra Batik Pekalongan,” J. Inform. Pengemb. IT (JPIT , Vol. 02, No. 02, Juli 2017, vol. 02, no. 02, hal. 23–26, 2017.
A. Kadir dan A. Susanto, Teori dan Aplikasi Pengolahan Citra, no. 2013. Penerbit Andi, 2013.
Sutarno, R. F. Abdullah, dan R. Passarella, “Identifikasi Tanaman Buah Berdasarkan Fitur Bentuk, Warna dan Tekstur Daun Berbasis Pengolahan Citra dan Learning Vector Quantization(LVQ),” Annu. Res. Semin., vol. 3, no. 1, hal. 65–70, 2017, [Daring]. Tersedia pada: https://seminar.ilkom.unsri.ac.id/index.php/ars/article/view/1742.
S. L. B. Ginting dan R. P. Trinanda, “TEKNIK DATA MINING MENGGUNAKAN METODE BAYES CLASSIFIER UNTUK OPTIMALISASI PENCARIAN PADA APLIKASI PERPUSTAKAAN,” 2013.
T. Wahyono, FUNDAMENTAL OF PYTHON FOR MACHINE LEARNING. Yogyakarta: GAVA MEDIA, 2021.
S. . Pattekari dan A. Parveen, “Prediction system for heart disease using Naïve Bayes,” Int. J. Adv. Comput. Math. Sci., vol. 3, no. 3, hal. 290–294, 2012.
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