Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor
DOI:
https://doi.org/10.14421/jiska.2024.9.3.159-169Keywords:
K-NN, Classification, RGB Feature Extraction, Banana, Digital ImagingAbstract
This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.
References
Angreni, I. A., Adisasmita, S. A., Ramli, M. I., & Hamid, S. (2019). Pengaruh Nilai K Pada Metode K-Nearest Neighbor (KNN) Terhadap Tingkat Akurasi Identifikasi Kerusakan Jalan. Rekayasa Sipil, 7(2), 63. https://doi.org/10.22441/jrs.2018.v07.i2.01
Arifki, H. H., & Barliana, M. I. (2018). Karakteristik dan Manfaat Tumbuhan Pisang di Indonesia : Review Artikel. Farmaka, 16(3), 196–203. https://doi.org/10.24198/JF.V16I3.17605
Bere, G. A., Tamtjita, E. N., & Kusumaningrum, A. (2016). Klasifikasi Untuk Menentukan Tingkat Kematangan Buah Pisang Sunpride. Conference SENATIK STT Adisutjipto Yogyakarta, 2, 109–113. https://doi.org/10.28989/senatik.v2i0.61
Fasnuari, H. A. D., Yuana, H., & Chulkamdi, M. T. (2022). Penerapan Algoritma K-Nearest Neighbor untuk Klasifikasi Penyakit Diabetes Melitus. Antivirus : Jurnal Ilmiah Teknik Informatika, 16(2), 133–142. https://doi.org/10.35457/antivirus.v16i2.2445
Guntara, R. G. (2023). Visualisasi Data Laporan Penjualan Toko Online Melalui Pendekatan Data Science Menggunakan Google Colab. ULIL ALBAB : Jurnal Ilmiah Multidisiplin, 2(6), 2091–2100. https://doi.org/10.56799/JIM.V2I6.1578
Kristiawan, K., & Widjaja, A. (2021). Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel. Jurnal Teknik Informatika Dan Sistem Informasi, 7(1), 35–46. https://doi.org/10.28932/jutisi.v7i1.3182
Lestari, Z. D., Nafi’iyah, N., & Susilo, P. H. (2019). Sistem Klasifikasi Jenis Pisang Berdasarkan Ciri Warna HSV Menggunakan Metode K-NN. Prosiding Seminar Nasional Teknologi Informasi Dan Komunikasi (SENATIK), 2(1), 11–15. https://prosiding.unipma.ac.id/index.php/SENATIK/article/view/880
Liantoni, F. (2016). Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor. Jurnal ULTIMATICS, 7(2), 98–104. https://doi.org/10.31937/ti.v7i2.356
Liantoni, F., & Annisa, F. N. (2018). Fuzzy K-Nearest Neighbor pada Klasifikasi Kematangan Cabai Berdasarkan Fitur HSV Citra. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 3(2), 101–108. https://doi.org/10.29100/jipi.v3i2.851
Limin, N. S., Sari, J. Y., & Purnama, I. P. N. (2019). Identifikasi Tingkat Kematangan Buah Pisang Menggunakan Metode Ektraksi Ciri Statistik Pada Warna Kulit Buah. ULTIMATICS, 10(2), 98–102. https://doi.org/10.31937/ti.v10i2.1004
Nishom, M. (2019). Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square. Jurnal Informatika: Jurnal Pengembangan IT, 4(1), 20–24. https://doi.org/10.30591/jpit.v4i1.1253
Rahayu, W. I., Prianto, C., & Novia, E. A. (2021). Perbandingan Algoritma K-Means dan Naïve Bayes untuk Memprediksi Prioritas Pembayaran Tagihan Rumah Sakit Berdasarkan Tingkat Kepentingan pada PT. Pertamina (Persero). Jurnal Teknik Informatika, 13(2), 1–8. https://ejurnal.ulbi.ac.id/index.php/informatika/article/view/1383
Raysyah, S. R., Arinal, V., & Mulyana, D. I. (2021). Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode KNN dan PCA. JSiI (Jurnal Sistem Informasi), 8(2), 88–95. https://doi.org/10.30656/jsii.v8i2.3638
Sidiq, U., Choiri, Moh. M., & Mujahidin, A. (2019). Metode Penelitian Kualitatif di Bidang Pendidikan (A. Mujahidin, Ed.). CV. Nata Karya. https://opac.perpusnas.go.id/DetailOpac.aspx?id=1257824
Siswanto, I., Utami, E., & Raharjo, S. (2020). Klasifikasi Tingkat Kematangan Buah Berdasarkan Warna dan Tekstur Menggunakan Metode K-Nearest Neighbor dan Nearest Mena Classifier. Inspiration: Jurnal Teknologi Informasi Dan Komunikasi, 10(1), 93–101. https://doi.org/10.35585/inspir.v10i1.2559
Stamou, G., Krinidis, M., Loutas, E., Nikolaidis, N., & Pitas, I. (2005). 2D and 3D Motion Tracking in Digital Video. In Handbook of Image and Video Processing (pp. 491–XVIII). Elsevier. https://doi.org/10.1016/B978-012119792-6/50093-0
Wahyono, W., Trisna, I. N. P., Sariwening, S. L., Fajar, M., & Wijayanto, D. (2020). Comparison of distance measurement on k-nearest neighbour in textual data classification. Jurnal Teknologi Dan Sistem Komputer, 8(1), 54–58. https://doi.org/10.14710/jtsiskom.8.1.2020.54-58
Wijaya, N., & Ridwan, A. (2019). Klasifikasi Jenis Buah Apel dengan Metode K-Nearest Neighbors dengan Ekstraksi Fitur HSV dan LBP. Jurnal Sisfokom (Sistem Informasi Dan Komputer), 8(1), 74–78. https://doi.org/10.32736/sisfokom.v8i1.610
Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064
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