Chili Type Detection System Using Principal Component Analysis Method
DOI:
https://doi.org/10.35870/ijsecs.v5i1.3735Keywords:
Chili Identification, Distance Metrics, City Block DistanceAbstract
Classification of types of chili vegetables is an important aspect in the agricultural industry to increase the efficiency of product management, packaging and distribution. This research aims to implement the Principal Component Analysis (PCA) method in the process of classifying vegetables and types of chilies. PCA is used to reduce the dimensionality of the data and extract the main features that are significant in distinguishing vegetable categories. The research dataset consists of digital images of chili vegetables which are extracted into color, texture and shape attributes. The research results show that PCA is able to significantly improve classification accuracy by minimizing computational complexity. Experiments were carried out with various numbers of principal components in PCA to determine the optimal configuration. In the best configuration, this method achieves classification accuracy of 90%, with PCA effectively reducing data dimensionality by up to 95% without losing important information. In conclusion, this approach has great potential to be implemented in vegetable classification automation systems to support efficiency in agricultural supply chains.
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Permana Putra, I. (2020). Scleroderma spp. in Indonesia: Poisoning case and potential utilization. JUSTEK: Jurnal Sains dan Teknologi, 3(2), 37. https://doi.org/10.31764/justek.v3i2.3517
Wardhani, I. P., & Widayati, S. (2019). Segmentasi warna citra HSV dan deteksi objek kupu-kupu dengan metode klasifikasi K-Means. Dalam Prosiding Seminar SeNTIK (Vol. 3, No. 1, hlm. 125–131).
Novianto, D., & Sugihartono, T. (2020). Sistem deteksi kualitas buah jambu air berdasarkan warna kulit menggunakan algoritma principal component analysis (PCA) dan K-Nearest Neighbor (K-NN). Jurnal Ilmiah Informatika Global, 11(2), 42–47. https://doi.org/10.36982/jiig.v11i2.1223
Widians, J. A., Pakpahan, H. S., Budiman, E., Haviluddin, H., & Soleha, M. (2019). Klasifikasi jenis bawang menggunakan metode K-Nearest Neighbor berdasarkan ekstraksi fitur bentuk dan tekstur. Jurnal Rekayasa Teknologi Informasi (JURTI), 3(2), 139. https://doi.org/10.30872/jurti.v3i2.3213
Hasan, M. A., & Liliana, D. Y. (2020). Pengenalan motif songket Palembang menggunakan deteksi tepi Canny, PCA dan KNN. Multinetics, 6(1), 1–7. https://doi.org/10.32722/multinetics.v6i1.27000
Putra, H. F., Tahiyat, R. M., Ihsan, R. M., Rahmaddeni, R., & Efrizoni, L. (2024). Penerapan algoritma K-Nearest Neighbor menggunakan wrapper sebagai preprocessing untuk penentuan keterangan berat badan manusia. MALCOM Indonesian Journal of Machine Learning and Computer Science, 4(1), 273–281. https://doi.org/10.57152/malcom.v4i1.1085
Nurnaningsih, D., Alamsyah, D., Herdiansah, A., Aristo, A., & Sinlae, J. (2021). Identifikasi citra tanaman obat jenis rimpang dengan Euclidean distance berdasarkan ciri bentuk dan tekstur. BITS, 3(3), 171–178. https://doi.org/10.47065/bits.v3i3.1019
Primandari, P. N., & Hardiansyah, B. (2018). Ekstraksi fitur menggunakan principal component analysis (PCA). Hasil Riset dan Pengabdian Masyarakat sebagai Inovasi Menuju Persaingan Global, 1(1), 66–74.
A’yuniyah, Q. A., & Reza, M. (2023). Penerapan algoritma K-Nearest Neighbor untuk klasifikasi jurusan siswa di SMA Negeri 15 Pekanbaru. Indonesian Journal of Informatics Research and Software Engineering, 3(1), 39–45. https://doi.org/10.57152/ijirse.v3i1.484
Pamungkas, A. (2020). Klasifikasi jenis sayuran menggunakan algoritma PCA dan KNN. Pemrograman Matlab. Diakses 11 Januari 2024, dari https://pemrogramanmatlab.com/2019/01/01/klasifikasi-jenis-sayuran-menggunakan-algoritma-pca-dan-knn/
Nugraha, R. A., Hidayat, E. W., & Shofa, R. N. (2023). Klasifikasi jenis buah jambu biji menggunakan algoritma principal component analysis dan K-nearest neighbor. Generation Journal, 7(1), 1–7. https://doi.org/10.29407/gj.v7i1.17900
Hasym, I. E., & Susilawati, I. (2021). Klasifikasi jenis ikan cupang menggunakan algoritma principal component analysis (PCA) dan K-nearest neighbors (KNN). KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 1(1), 168–179. https://doi.org/10.24002/konstelasi.v1i1.4242
Nurfitri, K., Pradana, A. D., & Widaningrum, I. (2021). Penerapan algoritma principal component analysis (PCA) dan K-nearest neighbors (KNN) pada klasifikasi. Jurnal Rekayasa Teknologi dan Komputasi, 1(1), 1–19.
Widyatama, R. A., & Hansun, S. (2019). Expert system for chili plants disease detection using certainty factor method. International Journal of Innovative Technology and Exploring Engineering, 9(1), 1145–1151. https://doi.org/10.35940/ijitee.a4440.119119
Syahputra, H., Nainggolan, J., Sirait, J., Ikromi, M., & Lubis, P. (2024). Red chili classification using HSV feature extraction and naive Bayes classifier. Teknosains Jurnal Sains Teknologi dan Informatika, 11(1), 33–40. https://doi.org/10.37373/tekno.v11i1.593
Prelle, A., Spadaro, D., Denca, A., Garibaldi, A., & Gullino, M. (2013). Comparison of clean-up methods for ochratoxin A on wine, beer, roasted coffee and chili commercialized in Italy. Toxins, 5(10), 1827–1844. https://doi.org/10.3390/toxins5101827
Cserháti, T., Forgács, E., Morais, H., & Mota, T. (2000). Classification of chili powders by thin-layer chromatography and principal component analysis. Journal of Biochemical and Biophysical Methods, 45(2), 221–229. https://doi.org/10.1016/s0165-022x(00)00119-6
Syukur, M., Zuhry, E., Armaini, A., Yunandra, Y., & Riska, F. (2023). Principal component and cluster analyses of 20 genotypes of chilli (Capsicum sp.) planted on peatlands. IOP Conference Series: Earth and Environmental Science, 1228(1), 012003. https://doi.org/10.1088/1755-1315/1228/1/012003
Rohaeti, E., Muzayanah, K., Septaningsih, D., & Rafi, M. (2019). Fast analytical method for authentication of chili powder from synthetic dyes using UV-Vis spectroscopy in combination with chemometrics. Indonesian Journal of Chemistry, 19(3), 668–676. https://doi.org/10.22146/ijc.36297
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