Application of Mahalanobis-Taguchi System in Palm Oil Plantation
DOI:
https://doi.org/10.15282/jmmst.v2i2.2864Keywords:
Palm oil plantation, Mahalanobis-Taguchi System, Mahalanobis distanceAbstract
This work deals with the palm oil plantation which is not utilizing the available data to be used as reference to measure the degree of abnormal observation. There are no assessment tools to quantify the degree of seriousness of abnormal observation. When a palm oil plantation is ignoring the optimization of the factors contributing to the problem, they might produce lots of waste. The objective of this work is to measure the degree of abnormality using Mahalanobis Taguchi System (MTS) and to diagnose the parameters that influence the system. MTS is a method used for identifying or to see the pattern in decision making. The main principle MTS is to choose a group of variables and achieve optimization of the factors that contributes to the problem by embodying with Mahalanobis Distance. From the results, the degree of abnormality was successfully measured using the MTS method. There are 5 sample blocks that belong in the abnormality group; PR16 B3, PR17 C5, PR15 A5, PR16 B5 and PR17 C4. The acquired result indicates that interrow weeding is the parameters that most influence the process flow in plantation. This variable contributes to 25% of the abnormal data. It can be concluded here is this parameter need to be diagnosed by increasing the dosage of interrow weeding so that the harvesting output performance can be improve.
References
Din, A. K. (2017). Dr. Ahmad Kushairi Din Malaysian Oil Palm Industry Performance 2016 and Prospects for 2017.
Mota-Gutiérrez, C. G., Reséndiz-Flores, E. O., & Reyes-Carlos, Y. I. (2018). Mahalanobis-Taguchi system: state of the art. International Journal of Quality and Reliability Management, 35(3), 596–613.
Chen, J., Cheng, L., Yu, H., & Hu, S. (2018). Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system. International Journal of Systems Science, 49(1), 147–159.
Reséndiz, E., Moncayo-Martínez, L. A., & Solís, G. (2013). Binary ant colony optimization applied to variable screening in the Mahalanobis-Taguchi System. Expert Systems with Applications, 40(2), 634–637.
Mota-Gutiérrez, C. G., Reséndiz-Flores, E. O., & Reyes-Carlos, Y. I. (2018). Mahalanobis-Taguchi system: state of the art. International Journal of Quality and Reliability Management, 35(3), 596–613.
Liparas, D., Angelis, L., & Feldt, R. (2012). Applying the Mahalanobis-Taguchi strategy for software defect diagnosis. Automated Software Engineering, 19(2), 141–165.
Jin, X., & Chow, T. W. S. (2013). Anomaly detection of cooling fan and fault classification of induction motor using Mahalanobis-Taguchi system. Expert Systems with Applications, 40(15), 5787–5795.
Hadighi, S. A., Sahebjamnia, N., Mahdavi, I., Asadollahpour, H., & Shafieian, H. (2013). Mahalanobis-Taguchi System-based criteria selection for strategy formulation: a case in a training institution. Journal of Industrial Engineering International, 9(1), 1–8.
John, B. (2014). Application of Mahalanobis-Taguchi system and design of experiments to reduce the field failures of splined shafts. International Journal of Quality and Reliability Management, 31(6), 681–697.
El-Banna, M. (2017). Modified Mahalanobis Taguchi System for Imbalance Data Classification. Computational Intelligence and Neuroscience, 2017.
Reséndiz, E., & Rull-Flores, C. A. (2013). Mahalanobis-Taguchi system applied to variable selection in automotive pedals components using Gompertz binary particle swarm optimization. Expert Systems with Applications, 40(7), 2361–2365.
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