Klasifikasi Jenis Madu Akasia dan Madu Hutan Berdasarkan Warna RGB Menggunakan Metode Multilayer Perceptron
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Ridwan Halim
*
Universitas Teknologi Yogyakarta, Sleman,
Indonesia
- Adityo Permana Wibowo Universitas Teknologi Yogyakarta, Sleman, Indonesia (*) Corresponding Author
Abstract
The advancement of technology has driven the agricultural industry to become more advanced and modern. Distinguishing honey with nearly identical colors is a challenging task. However, The ability to differentiate the color of acacia and forest honey is the simplest approach to ensuring the authenticity and quality of honey products. This study aims to develop a honey color classification model using Multilayer Perceptron (MLP). Image data were collected from various angles under natural lighting, followed by ninety experiments using parameter combinations, including data imbalance handling methods, dense layer structures, and training settings. The results showed that the MLP model with an optimal configuration, utilizing the Adaptive Synthetic Sampling (ADASYN) method for data imbalance, achieved a validation accuracy of 90.63%. This accuracy highlights the potential of the model to support industrial automation processes in reliably distinguishing honey colors.
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References
M. Habib, J. Alhamdani, D. Syauqy, and B. H. Prasetio, “Sistem Klasifikasi Kualitas Jenis-Jenis Madu berdasarkan Warna, Kecerahan, dan pH menggunakan Metode JST Backpropagation,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 6, pp. 2548–964, 2022, [Online]. Available: http://j-ptiik.ub.ac.id
A. Ningsih, I. Darwis, and R. Graharti, “Terapi Madu Pada Penderita Ulkus Diabetikum,” Medula, vol. 9, no. 1. pp. 192–197, 2019.
R. Ma and R. Shakhboz, “Honey is a Source of Health,” Int. J. Biol. Eng. Agric., vol. 03, no. 02, pp. 53–57, 2024.
A. Febriyanti, C. K. Jiu, and S. Ariyanti, “Efektivitas Jenis-Jenis Madu (Madu Hutan, Madu Kelulut Dan Madu Ternak) Terhadap Kadar Gula Darah”,” J. Keperawatan dan Kesehat., vol. 11, no. 1, pp. 12–20, 2020, doi: 10.54630/jk2.v11i1.114.
C. T. Dewi, D. R. Fajari, K. I. Bilqis, L. F. Ahmad, and N. ilmi Hayati, “Honey’s health benefits according to the qur’an,” J. STIKES Muhamamadiyah Ciamis J. Kesehat., vol. 9, no. 2, pp. 22–25, 2022, [Online]. Available: https://ojs.stikesmucis.ac.id/index.php/jurkes/article/download/114/72/406
H. E. Putri, L. T. Arlym, and R. Widowati, “Pengaruh Konsumsi Madu Akasia Terhadap Dismenore Pada Remaja,” Menara Med., vol. 6, no. 2, pp. 231–239, 2024, doi: 10.31869/mm.v6i2.5190.
O. R. Nasharuddin, N. A., Sunaryo, dan Puspitarini, “Analisa Kualitas Madu Akasia, Karet dan Randu Produksi PT Kembang Joyo Sriwijaya,” J. Din. Rekasatwa, vol. 5, no. 2, pp. 169–173, 2022.
D. I. Wahyuni, S. Suratno, and P. Pujiastuti, “Analysis Analisis Kualitas Kimia Madu Akasia Carpa (Acacia crassicarpa) dari Lebah Madu Apis mellifera di Kabupaten Jember,” BioEksakta J. Ilm. Biol. Unsoed, vol. 6, no. 2, p. 74, 2024, doi: 10.20884/1.bioe.2024.6.2.8762.
C. R. Ulfa, “Uji aktivitas antioksidan madu hutan trumon dan madu budidaya bener meriah dengan metode dpph,” 2022.
R. Ratri Rahayu, R. Widowati, and F. Mutiariami Dahlan, “CITRA DELIMA : Jurnal Ilmiah Institut Citra Internasional Perbedaan Efek Madu Akasia dengan Madu Multiflora terhadap Peningkatan Kadar Hemoglobin Remaja Putri,” Ji, vol. 7, no. 1, pp. 1–7, 2023, [Online]. Available: http://jurnalilmiah.ici.ac.id/index.php/
A. Prasetya Wibawa, W. Lestar, A. Bella Putra Utama, I. Tri Saputra, and Z. Nabila Izdihar, “Multilayer Perceptron untuk Prediksi Sessions pada Sebuah Website Journal Elektronik,” Indones. J. Data Sci., vol. 1, no. 3, pp. 57–67, 2020, doi: 10.33096/ijodas.v1i3.15.
I. G. R. M. Putra, M. W. A. Kesiman, G. A. Pradnyana, and I. M. D. Maysanjaya, “Identifikasi Citra Ukiran Ornamen Tradisional Bali Dengan Metode Multilayer Perceptron,” SINTECH (Science Inf. Technol. J., vol. 4, no. 1, pp. 29–39, 2021, doi: 10.31598/sintechjournal.v4i1.552.
Y. Amrozi, D. Yuliati, A. Susilo, N. Novianto, and R. Ramadhan, “Klasifikasi Jenis Buah Pisang Berdasarkan Citra Warna dengan Metode SVM,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 11, no. 3, pp. 394–399, 2022, doi: 10.32736/sisfokom.v11i3.1502.
R. Maneno, B. Baso, P. G. Manek, and K. Fallo, “Deteksi Tingkat Kematangan Buah Pinang Menggunakan Metode Support Vector Machine Berdasarkan Warna Dan Tekstur,” J. Inf. Technol., vol. 3, no. 2, pp. 60–66, 2023, doi: 10.32938/jitu.v3i2.5323.
A. Jinan, B. H. Hayadi, and U. P. Utama, “Klasifikasi Penyakit Tanaman Padi Mengunakan Metode Convolutional Neural Network Melalui Citra Daun (Multilayer Perceptron),” J. Comput. Eng. Sci., vol. 1, no. 2, pp. 37–44, 2022.
S. N. Ria, M. Walid, and B. A. Umam, “Pengolahan Citra Digital Untuk Identifikasi Jenis Penyakit Kulit Menggunakan Metode Convolutional Neural Network (CNN),” Energy - J. Ilm. Ilmu-Ilmu Tek., vol. 12, no. 2, pp. 9–16, 2022, doi: 10.51747/energy.v12i2.1118.
A. Ahmad, I. S. K. Idris, and A. Bode, “Klasifikasi Jenis Buah Tomat Menggunakan Covolutional Neural network,” J. Ilm. Ilmu Komput., vol. 2, no. 2, pp. 83–89, 2023.
M. Afriansyah, J. Saputra, Y. Sa’adati, and Valian Yoga Pudya Ardhana, “Optimasi Algoritma Nai?ve Bayes Untuk Klasifikasi Buah Apel Berdasarkan Fitur Warna RGB,” Bull. Comput. Sci. Res., vol. 3, no. 3, pp. 242–249, 2023, doi: 10.47065/bulletincsr.v3i3.251.
A. Goswami et al., “Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods,” Electron., vol. 11, no. 3, pp. 1–26, 2022, doi: 10.3390/electronics11030431.
G. D. Setyawan, A. Yuswanto, A. M. Ridwan, B. Wibowo, and M. Firmansyah, “Implementasi Metode Adasyn Dalam Deteksi Url Berbahaya Menggunakan Machine Learning: Demi Meningkatkan Keamanan Siber Di Era Digital,” Teknokom, vol. 6, no. 2, pp. 123–126, 2023, doi: 10.31943/teknokom.v6i2.153.
A. R. Prananda, E. L. Frannita, E. Pramitaningrum, A. Hidayat, W. B. Setiawan, and N. Purwaningsih, “Klasifikasikan Jenis Cacat Kulit Menggunakan SMOTE-GoogLeNet,” JITU J. Inform. Technol. Commun., vol. 8, no. 1, pp. 21–30, 2024, doi: 10.36596/jitu.v8i1.1341.
F. P. Nursyamsyi and F. N. Hasan, “KLIK: Kajian Ilmiah Informatika dan Komputer Klasifikasi Sentimen Terhadap Aplikasi Identitas Kependudukan Digital Menggunakan Algoritma Naïve Bayes dan SVM,” Media Online, vol. 4, no. 3, pp. 1788–1798, 2023, doi: 10.30865/klik.v4i3.1517.
P. A. Nugroho, “Implementasi Jaringan Syaraf Tiruan Multi-Layer Perceptron Untuk Prediksi Penyinaran Matahari Kota Bandung,” Komputa J. Ilm. Komput. dan Inform., vol. 12, no. 1, pp. 83–90, 2023, doi: 10.34010/komputa.v12i1.9419.
I. Tolstikhin et al., “MLP-Mixer: An all-MLP Architecture for Vision,” Adv. Neural Inf. Process. Syst., vol. 29, pp. 24261–24272, 2021.
Y. Nie et al., “MLP Architectures for Vision-and-Language Modeling: An Empirical Study,” 2021, [Online]. Available: http://arxiv.org/abs/2112.04453
A. V. Deviney, J. J. Classen, and J. A. Bruce, “A methodology for using a multilevel perspective framework to analyze complex systems,” Methodol. Innov., vol. 16, no. 2, pp. 123–137, 2023, doi: 10.1177/20597991231160280.
W. Li, H. Chen, J. Guo, Z. Zhang, and Y. Wang, “Brain-inspired Multilayer Perceptron with Spiking Neurons,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2022-June, pp. 773–783, 2022, doi: 10.1109/CVPR52688.2022.00086.
S. T. Hidayatullah, D. Syauqy, and H. Fitriyah, “Klasifikasi Sumber Nektar Madu berdasarkan Kecerahan dan Warna dengan Metode Naive Bayes berbasis Embedded System,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 8, pp. 3455–3461, 2021, [Online]. Available: http://j-ptiik.ub.ac.id
J. Bergstra and Y. Bengio, “Random Search for Hyper-Parameter Optimization,” J. Mach. Learn. Res., vol. 13, pp. 281–305, 2012.
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