KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT
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Abstract
Pepper fruit (Piper nigrum L.) is an agricultural commodity whose market value strongly depends on its ripeness level at harvest. Ripeness determination, which is still commonly performed through visual observation, tends to be inaccurate and subjective. This study aims to classify the ripeness level of pepper fruit based on skin color using an ensemble learning approach. The dataset consists of 1,996 pepper fruit images categorized into four ripeness levels unripe, semi ripe, ripe, and overripe. Color features were extracted from the HSV color model using color moment statistics including mean, standard deviation, and skewness. Random Forest and XGBoost models were combined using a soft voting method. The results show that the ensemble model achieved 98.25% accuracy, 98.30% precision, 98.27% recall, and 98.26% F1-score. The ensemble approach proved superior to single models by providing more accurate and stable classification of pepper fruit ripeness.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Mujidah, J. I., Bakti, R. Y., Lukman, Muhammad Faisal, Muhammad Syafaat, AM Hayat, M., & Syamsuri, A. M. (2025). KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT. Jurnal Informatika Progres, 17(2), 1-11. https://doi.org/10.56708/progres.v17i2.467
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[19] E. Hernando, A. Chandra Saputra, and J. Parhusip, “KLASIFIKASI TINGKAT KEMATANGAN BUAH NAGA BERDASARKAN WARNA KULIT MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS,” Dec. 2024.
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[22] E. Helmud, E. Helmud, F. Fitriyani, and P. Romadiana, “Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 13, no. 1, pp. 92–97, 2024, doi: 10.32736/sisfokom.v13i1.1985.
[2] X. Wu, X. Wu, H. Huang, F. Zhang, and Y. Wen, “Characterization of Pepper Ripeness in the Field Using Hyperspectral Imaging (HSI) with Back Propagation (BP) Neural Network and Kernel Based Extreme Learning Machine (KELM) Models,” Anal. Lett., vol. 57, no. 3, pp. 409–424, 2024.
[3] Pusat Data dan Informasi Pertanian, “OUTLOOK KOMODITAS LADA 2022 Pusat Data dan Sistem Informasi Pertanian i OUTLOOK LADA Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal-Kementerian Pertanian 2022,” 2022.
[4] M. Ali Buchari, K. Haritami, and R. Passarella, “Implementasi Metode Hue Saturation Value Pada Identifikasi Citra Warna Kulit Studi Kasus: Pembuatan Surat Keterangan Catatan Kepolisian,” 2022.
[5] B. Maulana Alfaruq, D. Erwanto, and I. Yanuartanti, “Klasifikasi Kematangan Buah Tomat Dengan Metode Support Vector Machine,” Gener. J., vol. 7, no. 3, pp. 64–72, 2023, doi: 10.29407/gj.v7i3.21092.
[6] R. Natras, B. Soja, and M. Schmidt, “Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting,” Remote Sens., vol. 14, no. 15, pp. 1–34, 2022, doi: 10.3390/rs14153547.
[7] I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, no. August, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.
[8] T. Kavzoglu and A. Teke, “Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost),” Arab. J. Sci. Eng., vol. 47, no. 6, pp. 7367–7385, 2022, doi: 10.1007/s13369-022-06560-8.
[9] S. Joses, D. Yulvida, and S. Rochimah, “Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier,” J. Appl. Comput. Sci. Technol., vol. 5, no. 1, pp. 72–80, 2024, doi: 10.52158/jacost.v5i1.741.
[10] S. Hidayat et al., “Optimalisasi Model Ensemble Learning dengan Augmentasi dan SMOTE pada Sistem Pendeteksi Kualitas Buah,” JTIM J. Teknol. Inf. dan Multimed., vol. 6, no. 1, pp. 27–36, Apr. 2024, doi: 10.35746/jtim.v6i1.406.
[11] A. P. Argadianata et al., “Klasifikasi Kualitas Buah Apel Menggunakan Metode Random Forest,” vol. 9, no. 2, pp. 2016–2022, 2025.
[12] I. Elzandy et al., “KLASIFIKASI VARIETAS ANGGUR BERDASARKAN CITRA DAUN MENGGUNAKAN VGG16-XGBOOST,” vol. 9, no. 3, pp. 5483–5491, 2025.
[13] R. Mardianto, Stefanie Quinevera, and S. Rochimah, “Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga,” J. Appl. Comput. Sci. Technol., vol. 5, no. 1, pp. 63–71, 2024, doi: 10.52158/jacost.v5i1.742.
[14] H. B. Kibria, M. Nahiduzzaman, M. O. F. Goni, M. Ahsan, and J. Haider, “An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI,” Sensors, vol. 22, no. 19, 2022, doi: 10.3390/s22197268.
[15] M. M. Maulana, “IMPLEMENTASI CITRA WARNA HSV UNTUK IDENTIFIKASI RHESUS GOLONGAN DARAH,” Pharmacogn. Mag., vol. 75, no. 17, pp. 399–405, 2021.
[16] A. I. Nurulrachman, R. C. Wihandika, and ..., “Ekstraksi Ciri pada Klasifikasi Citra Batik menggunakan Metode Gray Level Co-Occurrence Matrix, Local Binary Pattern, dan HSV Color Moment,” … Teknol. Inf. dan …, vol. 7, no. 1, pp. 374–383, 2023, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12197%0Ahttp://j-ptiik.ub.ac.id/index.php/j-ptiik/article/download/12197/5557
[17] M. A. Amrozi, D. Figo SW, and R. Wahyusari, “Perbandingan Segmentasi Ruang Warna HSV dan YCbCr untuk Deteksi Objek,” Infomatek, vol. 26, no. 2, pp. 217–222, Dec. 2024, doi: 10.23969/infomatek.v26i2.19025.
[18] C. C. Bey Lirna, T. Trimono, and A. T. Damaliana, “Employee Voluntary Attrition Prediction At Pt.Xyz: Ensemble Machine Learning Approach With Soft Voting Classifier,” J. Tek. Inform., vol. 5, no. 5, pp. 1231–1239, 2024, doi: 10.52436/1.jutif.2024.5.5.2007.
[19] E. Hernando, A. Chandra Saputra, and J. Parhusip, “KLASIFIKASI TINGKAT KEMATANGAN BUAH NAGA BERDASARKAN WARNA KULIT MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS,” Dec. 2024.
[20] M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.
[21] S. E. Prasetyo, P. H. Prastyo, and S. Arti, “A Cardiotocographic Classification using Feature Selection: A comparative Study,” JITCE (Journal Inf. Technol. Comput. Eng., vol. 5, no. 01, pp. 25–32, 2021, doi: 10.25077/jitce.5.01.25-32.2021.
[22] E. Helmud, E. Helmud, F. Fitriyani, and P. Romadiana, “Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 13, no. 1, pp. 92–97, 2024, doi: 10.32736/sisfokom.v13i1.1985.