Comparison of Machine Learning Algorithms for Palm Oil Fresh Fruit Bunch (FFB) Ripeness
Abstract
Determining the maturity level of FFB is crucial because it directly affects the quality and quantity of palm oil produced. Ripe FFB has high oil content, ensuring better-quality palm oil. Traditional methods include visual inspection, manual sampling, and physical testing, which are labor-intensive and subjective and can result in inconsistencies and errors. Machine learning algorithms can analyze datasets quickly and accurately, while also identifying patterns and features that are not easily visible to humans. Therefore, the aim of this study is to examine and evaluate the effectiveness of machine learning algorithm classifiers in determining oil palm FFB ripeness. The algorithms used in this research for classification analysis are Logistic Regression, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Neural Networks (NN), and Naïve Bayes. In this research, the analysis was carried out using the Orange data mining tool, which carried out data analysis and data visualization. The results of performance evaluation was tested, and assessment (cross-validation accuracy estimation), ROC (Receiver Operating Characteristic) analysis, and confusion matrix. The best models were Neural Network, Logistic Regression and SVM. Naïve Bayes appears to have lower performance. The results using the prediction widget show that logistic regressing has the best accuracy.
##Keywords:##
FFB grading, Machine learning algorithm, cross validation, Confusion matrix.
Published
Mar 30, 2025
How to Cite
NASIRLY, Riri; ARSY, Fadli; ROHATUL, Qolby.
Comparison of Machine Learning Algorithms for Palm Oil Fresh Fruit Bunch (FFB) Ripeness.
Journal of Ocean, Mechanical and Aerospace -science and engineering-, [S.l.], v. 69, n. 1, p. 39-48, mar. 2025.
ISSN 2527-6085.
Available at: <https://isomase.org/Journals/index.php/jomase/article/view/523>. Date accessed: 03 june 2026.
doi: http://dx.doi.org/10.36842/jomase.v69i1.523.
References
[1] Mohamed, Y.M.A.M., Dambul, K.D. & Yeep, C.K. (2023). A review of non-destructive ripeness classification techniques for oil palm fresh fruit bunches. Journal of Oil Palm Research, 35(4), 543-554.
[2] Mamat, N., Othman, M.F., Abdulghafor, R., Alwan, A. A. & Gulzar, Y. (2023). Enhancing image annotation technique of fruit classification using a deep learning approach. Sustainability (Switzerland), 15(2), 901. https://doi.org/10.3390/su15020901.
[3] Sinambela, R., Mandang, T., Subrata, I.D.M. & Hermawan, W. (2020). Application of an inductive sensor system for identifying ripeness and forecasting harvest time of oil palm. Scientia Horticulturae, 265, 109231. https://doi.org/10.1016/j.scienta.2020.109231.
[4] Zolfagharnassab, S., Shariff, A.R.B.M., Ehsani, R., Jaafar, H.Z. & Aris, I.B. (2022). Classification of oil palm fresh fruit bunches based on their maturity using thermal imaging technique. Agriculture (Switzerland), 12(11), 1779. https://doi.org/10.3390/agriculture12111779’
[5] Utom, S.L., et al. (2018). Non-destructive oil palm fresh fruit bunch (FFB) grading technique using optical sensor. International Journal of Integrated Engineering, 10(1), 35–39. https://doi.org/10.30880/ijie.2018.10.01.006.
[6] Tuerxun, A., Shariff, A.R.M., Janius, R., Abbas, Z. & Mahdiraji, G.A. (2020). Oil palm fresh fruit bunches maturity prediction by using optical spectrometer. IOP Conference Series: Earth and Environmental Science, 540(1), 012085. https://doi.org/10.1088/1755-1315/540/1/012085.
[7] Syaifuddin, A., Mualifah, L.N.A., Hidayat, L. & Abadi, A.M. (2020). Detection of palm fruit maturity level in the grading process through image recognition and fuzzy inference system to improve quality and productivity of crude palm oil (CPO). Journal of Physics: Conference Series, 1581(1), 012003. https://doi.org/10.1088/1742-6596/1581/1/012003.
[8] Aji, W.S. & Hawari, K. (2019). A study of deep learning method opportunity on palm oil FFB (fresh fruit bunch) grading methods. Advances in Engineering Research, 189, 35–38.
[9] Septiarini, A., Hatta, H.R., Hamdani, H., Oktavia, A., Kasim, A.A. & Suyanto, S. (2020). Maturity grading of oil palm fresh fruit bunches based on a machine learning approach. In 2020 5th International Conference on Informatics and Computing, ICIC 2020 (pp. 1–5). IEEE. https://doi.org/10.1109/ICIC50835.2020.9288603.
[10] Shiddiq, M., Arief, D.S., Defrianto, V.V. Dasta, D.M. Panjaitan & Saputra, D. (2023). Counting of oil palm fresh fruit bunches using computer vision. Journal of Oil Palm Research, 35(1), 111–120. https://doi.org/10.21894/jopr.2022.0029.
[11] Goh, J.Q., Shariff, A.R.M. & Nawi, N.M. (2021). Application of optical spectrometer to determine maturity level of oil palm fresh fruit bunches based on analysis of the front equatorial, front basil, back equatorial, back basil, and apical parts of the oil palm bunches. Agriculture (Switzerland), 11(12), 1179. https://doi.org/10.3390/agriculture11121179
[12] Zulkifli, Z.M., Hashim, F.H., Raj, T. & Huddin, A.B. (2018). A rapid and non-destructive technique in determining the ripeness of oil palm fresh fruit bunch (FFB). Jurnal Kejuruteraan, 30(1), 93–101. https://doi.org/10.17576/jkukm-2018-30(1).
[13] Septiarini, A., Hamdani, H., Hatta, H.R. & Kasim, A.A. (2019). Image-based processing for ripeness classification of oil palm fruit. In 2019 5th International Conference on Science in Information Technology (ICSITech) (pp. 23–26). IEEE. https://doi.org/10.1109/ICSITech46713.2019.8987575.
[14] Alfatni, M.S.M., Shariff, A.R.M., Bejo, S.K., Ben Saaed, O.M.,& Mustapha, A. (2018). Real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM classifiers. IOP Conference Series: Earth and Environmental Science, 169(1), 012067. https://doi.org/10.1088/1755-1315/169/1/012067.
[15] Suharjito, et al. (2023). Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Scientific Data, 10(1), 1958. https://doi.org/10.1038/s41597-023-01958-x.
[16] Alfatni, M.S.M., Khairunniza-Bejo, S., Marhaban, M.H. B., Saaed, O.M.B., Mustapha, A. & Shariff, A.R.M. (2022). Towards a real-time oil palm fruit maturity system using supervised classifiers based on feature analysis. Agriculture (Switzerland), 12(9), 1461. https://doi.org/10.3390/agriculture12091461.
[17] Al-Sammarraie, M.A.J., et al. (2022). Predicting fruit’s sweetness using artificial intelligence—Case study: Orange. Applied Sciences (Switzerland), 12(16), 8233. https://doi.org/10.3390/app12168233.
[18] Thange, U., Shukla, V.K., Punhani, R. & Grobbelaar, W. (2021). Analyzing COVID-19 dataset through data mining tool ‘Orange’. In Proceedings of 2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021 (pp. 198–203). IEEE. https://doi.org/10.1109/ICCAKM50778.2021.9357754.
[19] Popchev, I.P. & Orozova, D.A. (2019). Towards big data analytics in the e-learning space. Sciendo. https://doi.org/10.2478/cait-2019-0023.
[20] Popchev, I., & Orozova, D. (2023). Algorithms for machine learning with Orange system. International Journal of Online and Biomedical Engineering, 19(4), 109–123. https://doi.org/10.3991/ijoe.v19i04.36897.
[21] Ratra, R. & Gulia, P. (2020). Experimental evaluation of open source data mining tools (WEKA and Orange). International Journal of Engineering Trends and Technology, 68(8), 30–35. https://doi.org/10.14445/22315381/IJETT-V68I8P206S.
[22] Arifin, B., Perwitasari, D.A., Zulkarnain, Z. & Rokhman, M.R. (2022). Models for predicting the quality of life domains on the general population through the Orange data mining approach. Pharmaciana, 12(1), 72. https://doi.org/10.12928/pharmaciana.v12i1.20827.
[23] Abdelmagid, A.S. & Qahmash, A.I.M. (2023). Utilizing the educational data mining techniques ‘Orange technology’ for detecting patterns and predicting academic performance of university students. Information Sciences Letters, 12(3), 1415–1431. https://doi.org/10.18576/isl/120330.
[24] Gera, M. & Goel, S. (2015). Data mining-techniques, methods and algorithms: A review on tools and their validity. International Journal of Computer Applications, 113(18), 975–8887.
[25] Hozairi, A., Anwari, S. & Alim, S. (2021). Orange data mining implementation for student graduation classification using K-nearest neighbor, decision tree and naive Bayes models. Jurnal Ilmiah NERO, 6(2), 2021.
[26] Sajwan, V. & Ranjan, R. (2019). Classifying flowers images by using different classifiers in orange. International Journal of Engineering and Advanced Technology, 8(6 Special Issue 3), 1057–1061. https://doi.org/10.35940/ijeat.F1334.0986S319.
[27] Sabri, N., Ibrahim, Z., Syahlan, S., Jamil, N. & Mangshor, N.N.A. (2017). Palm oil fresh fruit bunch ripeness grading identification using color features. Journal of Fundamental and Applied Sciences, 9(4S), 565–579. https://doi.org/10.4314/jfas.v9i4s.3.
[28] Mason, C., Twomey, J., Wright, D. & Whitman, L. (2018). Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Research in Higher Education, 59(3), 382–400. https://doi.org/10.1007/s11162-017-9473-z.
[29] Guerrero, M.C., Parada, J.S. & Espitia, H.E. (2021). EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, 7(6), e07258. https://doi.org/10.1016/j.heliyon.2021.e07258.
[30] Florkowski, C.M. (2008). Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. The Clinical Biochemist Reviews, 29 (Suppl 1), S83.
[2] Mamat, N., Othman, M.F., Abdulghafor, R., Alwan, A. A. & Gulzar, Y. (2023). Enhancing image annotation technique of fruit classification using a deep learning approach. Sustainability (Switzerland), 15(2), 901. https://doi.org/10.3390/su15020901.
[3] Sinambela, R., Mandang, T., Subrata, I.D.M. & Hermawan, W. (2020). Application of an inductive sensor system for identifying ripeness and forecasting harvest time of oil palm. Scientia Horticulturae, 265, 109231. https://doi.org/10.1016/j.scienta.2020.109231.
[4] Zolfagharnassab, S., Shariff, A.R.B.M., Ehsani, R., Jaafar, H.Z. & Aris, I.B. (2022). Classification of oil palm fresh fruit bunches based on their maturity using thermal imaging technique. Agriculture (Switzerland), 12(11), 1779. https://doi.org/10.3390/agriculture12111779’
[5] Utom, S.L., et al. (2018). Non-destructive oil palm fresh fruit bunch (FFB) grading technique using optical sensor. International Journal of Integrated Engineering, 10(1), 35–39. https://doi.org/10.30880/ijie.2018.10.01.006.
[6] Tuerxun, A., Shariff, A.R.M., Janius, R., Abbas, Z. & Mahdiraji, G.A. (2020). Oil palm fresh fruit bunches maturity prediction by using optical spectrometer. IOP Conference Series: Earth and Environmental Science, 540(1), 012085. https://doi.org/10.1088/1755-1315/540/1/012085.
[7] Syaifuddin, A., Mualifah, L.N.A., Hidayat, L. & Abadi, A.M. (2020). Detection of palm fruit maturity level in the grading process through image recognition and fuzzy inference system to improve quality and productivity of crude palm oil (CPO). Journal of Physics: Conference Series, 1581(1), 012003. https://doi.org/10.1088/1742-6596/1581/1/012003.
[8] Aji, W.S. & Hawari, K. (2019). A study of deep learning method opportunity on palm oil FFB (fresh fruit bunch) grading methods. Advances in Engineering Research, 189, 35–38.
[9] Septiarini, A., Hatta, H.R., Hamdani, H., Oktavia, A., Kasim, A.A. & Suyanto, S. (2020). Maturity grading of oil palm fresh fruit bunches based on a machine learning approach. In 2020 5th International Conference on Informatics and Computing, ICIC 2020 (pp. 1–5). IEEE. https://doi.org/10.1109/ICIC50835.2020.9288603.
[10] Shiddiq, M., Arief, D.S., Defrianto, V.V. Dasta, D.M. Panjaitan & Saputra, D. (2023). Counting of oil palm fresh fruit bunches using computer vision. Journal of Oil Palm Research, 35(1), 111–120. https://doi.org/10.21894/jopr.2022.0029.
[11] Goh, J.Q., Shariff, A.R.M. & Nawi, N.M. (2021). Application of optical spectrometer to determine maturity level of oil palm fresh fruit bunches based on analysis of the front equatorial, front basil, back equatorial, back basil, and apical parts of the oil palm bunches. Agriculture (Switzerland), 11(12), 1179. https://doi.org/10.3390/agriculture11121179
[12] Zulkifli, Z.M., Hashim, F.H., Raj, T. & Huddin, A.B. (2018). A rapid and non-destructive technique in determining the ripeness of oil palm fresh fruit bunch (FFB). Jurnal Kejuruteraan, 30(1), 93–101. https://doi.org/10.17576/jkukm-2018-30(1).
[13] Septiarini, A., Hamdani, H., Hatta, H.R. & Kasim, A.A. (2019). Image-based processing for ripeness classification of oil palm fruit. In 2019 5th International Conference on Science in Information Technology (ICSITech) (pp. 23–26). IEEE. https://doi.org/10.1109/ICSITech46713.2019.8987575.
[14] Alfatni, M.S.M., Shariff, A.R.M., Bejo, S.K., Ben Saaed, O.M.,& Mustapha, A. (2018). Real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM classifiers. IOP Conference Series: Earth and Environmental Science, 169(1), 012067. https://doi.org/10.1088/1755-1315/169/1/012067.
[15] Suharjito, et al. (2023). Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Scientific Data, 10(1), 1958. https://doi.org/10.1038/s41597-023-01958-x.
[16] Alfatni, M.S.M., Khairunniza-Bejo, S., Marhaban, M.H. B., Saaed, O.M.B., Mustapha, A. & Shariff, A.R.M. (2022). Towards a real-time oil palm fruit maturity system using supervised classifiers based on feature analysis. Agriculture (Switzerland), 12(9), 1461. https://doi.org/10.3390/agriculture12091461.
[17] Al-Sammarraie, M.A.J., et al. (2022). Predicting fruit’s sweetness using artificial intelligence—Case study: Orange. Applied Sciences (Switzerland), 12(16), 8233. https://doi.org/10.3390/app12168233.
[18] Thange, U., Shukla, V.K., Punhani, R. & Grobbelaar, W. (2021). Analyzing COVID-19 dataset through data mining tool ‘Orange’. In Proceedings of 2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021 (pp. 198–203). IEEE. https://doi.org/10.1109/ICCAKM50778.2021.9357754.
[19] Popchev, I.P. & Orozova, D.A. (2019). Towards big data analytics in the e-learning space. Sciendo. https://doi.org/10.2478/cait-2019-0023.
[20] Popchev, I., & Orozova, D. (2023). Algorithms for machine learning with Orange system. International Journal of Online and Biomedical Engineering, 19(4), 109–123. https://doi.org/10.3991/ijoe.v19i04.36897.
[21] Ratra, R. & Gulia, P. (2020). Experimental evaluation of open source data mining tools (WEKA and Orange). International Journal of Engineering Trends and Technology, 68(8), 30–35. https://doi.org/10.14445/22315381/IJETT-V68I8P206S.
[22] Arifin, B., Perwitasari, D.A., Zulkarnain, Z. & Rokhman, M.R. (2022). Models for predicting the quality of life domains on the general population through the Orange data mining approach. Pharmaciana, 12(1), 72. https://doi.org/10.12928/pharmaciana.v12i1.20827.
[23] Abdelmagid, A.S. & Qahmash, A.I.M. (2023). Utilizing the educational data mining techniques ‘Orange technology’ for detecting patterns and predicting academic performance of university students. Information Sciences Letters, 12(3), 1415–1431. https://doi.org/10.18576/isl/120330.
[24] Gera, M. & Goel, S. (2015). Data mining-techniques, methods and algorithms: A review on tools and their validity. International Journal of Computer Applications, 113(18), 975–8887.
[25] Hozairi, A., Anwari, S. & Alim, S. (2021). Orange data mining implementation for student graduation classification using K-nearest neighbor, decision tree and naive Bayes models. Jurnal Ilmiah NERO, 6(2), 2021.
[26] Sajwan, V. & Ranjan, R. (2019). Classifying flowers images by using different classifiers in orange. International Journal of Engineering and Advanced Technology, 8(6 Special Issue 3), 1057–1061. https://doi.org/10.35940/ijeat.F1334.0986S319.
[27] Sabri, N., Ibrahim, Z., Syahlan, S., Jamil, N. & Mangshor, N.N.A. (2017). Palm oil fresh fruit bunch ripeness grading identification using color features. Journal of Fundamental and Applied Sciences, 9(4S), 565–579. https://doi.org/10.4314/jfas.v9i4s.3.
[28] Mason, C., Twomey, J., Wright, D. & Whitman, L. (2018). Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Research in Higher Education, 59(3), 382–400. https://doi.org/10.1007/s11162-017-9473-z.
[29] Guerrero, M.C., Parada, J.S. & Espitia, H.E. (2021). EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks. Heliyon, 7(6), e07258. https://doi.org/10.1016/j.heliyon.2021.e07258.
[30] Florkowski, C.M. (2008). Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests. The Clinical Biochemist Reviews, 29 (Suppl 1), S83.
