PENERAPAN ARTIFICIAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI KUALITAS MUTIARA KHAS LOMBOK BERDASARKAN CIRI VISUAL

Authors

  • Muh Nasirudin Karim Universitas Teknologi Mataram
  • Muhammad Masjun Efendi Universitas Teknologi Mataram
  • Zumratul Muahidin Universitas Teknologi Mataram

DOI:

https://doi.org/10.69916/comtechno.v3i1.336

Keywords:

pengolahan citra, segmentasi, regionprops, jaringan syaraf tiruan, klasifikasi Mutiara

Abstract

Penelitian ini bertujuan untuk mengklasifikasikan citra mutiara Lombok berdasarkan bentuk, ukuran, dan kecacatan menggunakan metode pengolahan citra dan kecerdasan buatan. Proses segmentasi citra dilakukan menggunakan metode thresholding untuk memisahkan objek mutiara dari latar belakang, kemudian dilanjutkan dengan deteksi tepi menggunakan metode Canny guna mempermudah ekstraksi fitur. Fitur morfologis seperti area, perimeter, roundness, diameter, serta cacat bentuk dan warna diekstraksi menggunakan metode regionprops. Hasil ekstraksi ini kemudian digunakan sebagai variabel dalam proses klasifikasi menggunakan Jaringan Syaraf Tiruan (JST) dan dibandingkan dengan metode Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 360 citra mutiara yang terbagi dalam tiga kelas: A, AA, dan AAA. Hasil klasifikasi menunjukkan bahwa metode JST menghasilkan akurasi tertinggi sebesar 98%, mengungguli SVM yang memperoleh akurasi 96%. Temuan ini menunjukkan bahwa kombinasi metode regionprops dan JST efektif dalam klasifikasi multiview citra mutiara Lombok.

References

M. N. Karim, R. A. Pramunendar, M. A. Soeleman, P. Purwanto, and B. Imran, “Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN),” ILKOM Jurnal Ilmiah, vol. 14, no. 3, pp. 209–217, 2022, doi: 10.33096/ilkom.v14i3.1317.209-217.

B. Imran, A. Yani, R. Muslim, and Z. Zaeniah, “Lombok Pearl Quality Classification Using a Combination of Feature Extraction and Artificial Neural Networks Based on Shape,” Jurnal Pilar Nusa Mandiri, vol. 18, no. 2, pp. 167–172, 2022, doi: 10.33480/pilar.v18i2.3507.

Q. Xuan, Z. Chen, Y. Liu, H. Huang, G. Bao, and D. Zhang, “Multiview Generative Adversarial Network and Its Application in Pearl Classification,” IEEE Transactions on Industrial Electronics, vol. 66, no. 10, pp. 8244–8252, 2019, doi: 10.1109/TIE.2018.2885684.

Q. Xuan et al., “Automatic Pearl Classification Machine Based on a Multistream Convolutional Neural Network,” IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6538–6547, 2018, doi: 10.1109/TIE.2017.2784394.

A. Mathur and G. M. Foody, “Multiclass and binary SVM classification: Implications for training and classification users,” IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 2, pp. 241–245, 2008, doi: 10.1109/LGRS.2008.915597.

X. Liu, S. Jin, Z. Yang, G. Królczyk, and Z. Li, “Measuring Shape Parameters of Pearls in Batches Using Machine Vision: A Case Study,” Micromachines (Basel), vol. 13, no. 4, pp. 1–14, 2022, doi: 10.3390/mi13040546.

C. K. Jha and M. H. Kolekar, “Cardiac arrhythmia classification using tunable Q-wavelet transform based features and support vector machine classifier,” Biomed Signal Process Control, vol. 59, p. 101875, 2020, doi: 10.1016/j.bspc.2020.101875.

W. F. Schreiber and O. J. Tretiak, “Image Processing,” vol. 59, no. 11, 1971.

L. Luo et al., “Grape berry detection and size measurement based on edge image processing and geometric morphology,” Machines, vol. 9, no. 10, 2021, doi: 10.3390/machines9100233.

M. Ohsaki, P. Wang, K. Matsuda, S. Katagiri, H. Watanabe, and A. Ralescu, “Confusion-matrix-based kernel logistic regression for imbalanced data classification,” IEEE Trans Knowl Data Eng, vol. 29, no. 9, pp. 1806–1819, 2017, doi: 10.1109/TKDE.2017.2682249.

M. Hasnain, M. F. Pasha, I. Ghani, M. Imran, M. Y. Alzahrani, and R. Budiarto, “Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking,” IEEE Access, vol. 8, pp. 90847–90861, 2020, doi: 10.1109/ACCESS.2020.2994222.

Suharjito, B. Imran, and A. S. Girsang, “Family relationship identification by using extract feature of gray level co-zoccurrence matrix (GLCM) based on parents and children fingerprint,” International Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2738–2745, 2017, doi: 10.11591/ijece.v7i5.pp2738-2745.

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Published

2025-07-23

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How to Cite

Karim, M. N., Efendi, M. M., & Muahidin, Z. (2025). PENERAPAN ARTIFICIAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI KUALITAS MUTIARA KHAS LOMBOK BERDASARKAN CIRI VISUAL . Journal Computer and Technology, 3(1), 39–47. https://doi.org/10.69916/comtechno.v3i1.336

Issue

Vol. 3 No. 1 (2025): Juli 2025

Section

Articles

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Copyright (c) 2025 Muh Nasirudin Karim, Muhammad Masjun Efendi, Zumratul Muahidin

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This work is licensed under a Creative Commons Attribution 4.0 International License.