Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch

This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The level of palm oil FFB maturity will affect to oil extraction rate (OER) which is the main key performance indicator for palm oil industry. The most important pro...

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Main Author:	Norsani, Siti Hadijah
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General) TS Manufactures
Online Access:	http://eprints.utem.edu.my/id/eprint/20775/1/Application%20Of%20AHP%20For%20Determining%20The%20Best%20Of%20Palm%20Oil%20Fresh%20Fruit%20Bunch%20-%20Siti%20Hadijah%20Norsani%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/20775/2/Application%20Of%20AHP%20For%20Determining%20The%20Best%20Of%20Palm%20Oil%20Fresh%20Fruit%20Bunch%20-%20Siti%20Hadijah%20Norsani.pdf
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id	my-utem-ep.20775
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	T Technology (General) TS Manufactures
spellingShingle	T Technology (General) TS Manufactures Norsani, Siti Hadijah Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch
description	This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The level of palm oil FFB maturity will affect to oil extraction rate (OER) which is the main key performance indicator for palm oil industry. The most important process to classify the palm oil FFB ripeness is the grading process. Therefore, the quality grading process of FFB needs to be conducted properly to ensure that high-quality palm oil FFB is selected for production. Usually, the grading process performed by some graders in each mill manually. A sample from each lorry was taken in the grading process. However, this method takes time and may lead to errors in the classification process, especially if the graders have less experience. One of the useful tools that can be employed to make decisions in classification process is Analytical Hierarchy Process (AHP). The main concern was to ensure the reliability of AHP technique achievable. The methodology in this study consists of five phases ie; data collection from expert grader and industries visited, identifying the most important criteria, analysis by AHP method, validation by TOPSIS technique and finally the ranked of the best criteria of high quality FFB. The Expert Choice Software and Microsoft Office Excel are tools that used to analyze the data collected from expert graders in the AHP and TOPSIS technique. The main objective of this study is to determine the best quality of FFB using AHP. The result found that the number of detached fruitlets is the most important criteria to determine the FFB ripeness with 0.560 priority vector followed by color with 0.219 priority vector compared to other criteria. The sensitivity analysis performed to ensure the results are consistent and reliable. It will help the graders to conduct a proper grading process at mills to increase the quality of OER.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Norsani, Siti Hadijah
author_facet	Norsani, Siti Hadijah
author_sort	Norsani, Siti Hadijah
title	Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch
title_short	Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch
title_full	Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch
title_fullStr	Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch
title_full_unstemmed	Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch
title_sort	application of ahp for determining the best of palm oil fresh fruit bunch
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Manufacturing Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/20775/1/Application%20Of%20AHP%20For%20Determining%20The%20Best%20Of%20Palm%20Oil%20Fresh%20Fruit%20Bunch%20-%20Siti%20Hadijah%20Norsani%20-%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/20775/2/Application%20Of%20AHP%20For%20Determining%20The%20Best%20Of%20Palm%20Oil%20Fresh%20Fruit%20Bunch%20-%20Siti%20Hadijah%20Norsani.pdf
_version_	1747834003282460672
spelling	my-utem-ep.207752021-10-08T17:00:07Z Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch 2016 Norsani, Siti Hadijah T Technology (General) TS Manufactures This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The level of palm oil FFB maturity will affect to oil extraction rate (OER) which is the main key performance indicator for palm oil industry. The most important process to classify the palm oil FFB ripeness is the grading process. Therefore, the quality grading process of FFB needs to be conducted properly to ensure that high-quality palm oil FFB is selected for production. Usually, the grading process performed by some graders in each mill manually. A sample from each lorry was taken in the grading process. However, this method takes time and may lead to errors in the classification process, especially if the graders have less experience. One of the useful tools that can be employed to make decisions in classification process is Analytical Hierarchy Process (AHP). The main concern was to ensure the reliability of AHP technique achievable. The methodology in this study consists of five phases ie; data collection from expert grader and industries visited, identifying the most important criteria, analysis by AHP method, validation by TOPSIS technique and finally the ranked of the best criteria of high quality FFB. The Expert Choice Software and Microsoft Office Excel are tools that used to analyze the data collected from expert graders in the AHP and TOPSIS technique. The main objective of this study is to determine the best quality of FFB using AHP. The result found that the number of detached fruitlets is the most important criteria to determine the FFB ripeness with 0.560 priority vector followed by color with 0.219 priority vector compared to other criteria. The sensitivity analysis performed to ensure the results are consistent and reliable. It will help the graders to conduct a proper grading process at mills to increase the quality of OER. 2016 Thesis http://eprints.utem.edu.my/id/eprint/20775/ http://eprints.utem.edu.my/id/eprint/20775/1/Application%20Of%20AHP%20For%20Determining%20The%20Best%20Of%20Palm%20Oil%20Fresh%20Fruit%20Bunch%20-%20Siti%20Hadijah%20Norsani%20-%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/20775/2/Application%20Of%20AHP%20For%20Determining%20The%20Best%20Of%20Palm%20Oil%20Fresh%20Fruit%20Bunch%20-%20Siti%20Hadijah%20Norsani.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=104905 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Manufacturing Engineering 1. Abdul Mukti, Luthfi Fatah, Henny Pramoedyo & Budi Setiawan, (2014). Analysis of Optimal Policy Option for Sustainable Palm Oil Plantation Development. Journal of Economics and Sustainable Development ISSN 2222-2855 5(5), pp.93-103. 2. Abdul Rashid Mohamed Shariff, (2015). FFB Classification at the Palm Oil Mill Using Advanced Techniques and Technology. 18th International Oil Palm Conference, Malaysia, 22 – 25 September 2015. 3. Abhishek, P.M., Tushar, R.M., Ketan, M.M., Sachin, B.K., (2004). A Review paper on “Multicriteria Decision Making Approaches and Criterion” Used in Supplier Selection and Evaluation. International Journal of Engineering Research & Technology (IJERT), 3(1), pp. 2425 – 2434. 4. Alessio Ishizaka and Ashraf Labib, (2009). Analytic Hierarchy Process and Expert Choice: Benefits and Limitations. ORInsight, 22(4), pp. 201–220. 5. Alireza A. and Abbas A., (2011). Sensitivity Analysis of TOPSIS Technique: The Results of Change in the Weight of One Attribute on the Final Ranking of Alternatives. Journal of Optimization in Industrial Engineering 7, pp. 23 – 28. 6. Andi Dirpan and Alim Setiawan Slamet, (2016). Selecting Postharvest Technology Method for Citrus Fruit using Analytic Hierarchy Process (AHP). ISSN 2413-0877 Volume 3, pp.64-69. 7. Asma M.A. Bahurmoz, (2006). The Analytic Hierarchy Process: A Methodology for Win-win Management. King Abdulaziz University, Jeddah. JKAU: Econ. & Adm., 20(1), pp. 3-16. 8. Bambang Aria Wisena and Arief Daryanto (2014). Sustainable Development Strategy and the Competitiveness of Indonesian Palm Oil Industry. International Journal of Managerial Studies and Research (IJMSR), 2(10), pp. 102-115. 9. Bhutia P.W., and Phipon R. (2012). Application of AHP and TOPSIS method for supplier selection problem, IOSR Journal of Engineering, 2(10), pp.43-50. 10. Carey Hepler and Glenn Mazur, (2007). The Analytic Hierarchy Process Methodologies and Application with Customers and Management at Blue Cross Blue Shielf of Florida. ISQFD’07-Williamsburg/ The 19th Symposium, QFD,7 – 8 September 2007. 11. Carlsson, C. and Walden, P., (1995). AHP in Political Group Decisions: A Study in the Art of Possibilities, Interfaces, 25(4), pp. 14-29. 12. Chandra Sekhar Nandi, Bipan Tudu and Chiranjib Koley, (2014). Computer Vision Based Mango Fruit Grading System. International conference on Innovative Engineering Technologies (ICIET’2014), Bangkok, Thailand, 28-29 December 2014. 13. Emrah Onder and Sundus Dag, (2013). Combining Analythical Hierarchy Process and TOPSIS Approaches for Supplier Selection in a Cable Company, Journal of Business, Economics & Finance, 2(2), pp. 56-74. 14. Feng-Yi Wu and Chung-Chu Chuang, (2013). The Optimal Relationship between Buyer and Seller Obtained Using TOPSIS Method. Journal of Advanced Management Science, 1(1), pp. 133-135. 15. Fuad Halimoen, Alvi Syahrin and Herman Mawengkang, (2015). Corporate Social Responsibility Program Toward Sustainability Crude Palm Oil Industry Using Analytic Hierarchy Process. International Research Journal of Engineering and Technology (IRJET), 2(6), pp. 1202 – 1209. 16. Hambali Ariff, Mohd. Sapuan, Napsiah Ismail and Y. Nukman (2008). Use of Analytical Hierarchy Process (AHP) for Selecting the Best Design Concept. Journal Teknology, 49(A) pp. 1-18. 17. Hambali A., Sapuan S.M., Ismail N. and Nukman Y., (2010). Material selection of polymeric composite automative bumpeer beam using analytical hierarchy process. J. Cent. South Univ. Tecnol, 17(2), pp. 244 – 256. 18. Hsu-Shih S., Huan-Jyh S. and Stanley L., (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45 (2007), pp.801 – 813. 19. Jahanshahloo G.R., Hosseinzadeh L. and Izadikhah M., (2006). Extension of the TOPSIS method for decision-making problem with fuzzy data. Applied Mathematics and Computation, 181 (2006), pp. 1544 – 1551. 20. Jayant A., Gupta P., Garg S.K. and Khan M., (2014). TOPSIS-AHP Based Approach for Selectin of Reverse Logistics Service Provider: A Case Study of Mobile Phone Industry. Procedia Engineering, 97 (2014), pp. 2147 – 2156. 21. Kalashetty, S.S., Purohit, G.K., and Hebbal, S.S., (2012). Selection of design Concept: A case study. IOSR Journal of Engineering, 2(10), pp. 25 – 30. 22. Kevin Kam F.Y., (2009). On Limitations of the Prioritization Methods in Analytic Hierarchy Process: A Study of Transportation Selection Problems. Proceedings of International MultiConference of Engineers and Computer Scientists 2009, Hong Kong, 18 – 20 March 2009. 23. Leemans V., Magein H. and Destain M.F., (2002) On-line Fruit Grading according to their External Quality using Machine Vision. Biosystem Engineering , 83(4), pp. 397 – 405. 24. Liberatone, M.J., Nydick, R.L. and Sanchez, P.M. (1992) The Evaluation of Research Paper (or Hor to Get an Academic Committee to Agree on Something), Interfaces, 22(2), pp. 92 – 100. 25. MPOB, (2003). Oil Palm Fruit Grading Manual Second Edition. Malaysian Palm Oil Board (MPOB). [e-book] Malaysian Palm Oil Board (MPOB). Available through: https://www.scribd.com/doc/213930600mpob-grading-manual-NEW2 [Accessed on 5 April 2016]. 26. MPOB, (2016). Overview of the Malaysian Oil Palm Industry 2015. [e-book] Economic & Industry Development Division Lamaysian Palm Oil Board. Available through: MPOB website http://mpob.gov.my [Accessed on 3 June 2016]. 27. Mahmoodzadeh S., Shahrabi J., Pariazar M. and Zaeri M.S., (2007). Project Selection by Using Fuzzy AHP and TOPSIS Technique. International Journal of Social, Behavioral, Educational, Economic,Business and Industrial Engineering, 1(6), pp. 270 – 275. 28. Manuela Baietto and Alphus D.Wilson, (2015). Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading. Sensors 2015, 15, pp. 899-931. 29. Mark V. and Patrick T.H., (2013). An Analysis of Multi-Criteria Decision Making Methods. Old Dominion University, International Journal of Operations Research, 10(2), pp. 56 – 66. 30. May Z., and Amaran M.H., (2011). Automated Oil Palm Fruit Grading System using Artificial Intelligence. International Journal of Video & Image Processing and Network Security, 11(3), pp. 30 – 35. 31. May Z., and Amaran M.H., (2011). Automated Ripeness Assessment of Oil Palm Fruit Using RGB and Fuzzy Logic Technique. MACMESE'11 Proceedings, Catania, Sicily,Italy, 3 – 5 November 2011. 32. Meftah Salem M.A., Abdul Rashid M.S., Helmi Zulhaidi M.S., Osama M.B and Omar M.E., (2008). Oil Palm Fruit Bunch Grading System Using Red, Green and Blue Digital Number. University Putra Malaysia, Journal of Applied Sciences, 8(8), pp. 1444-1452. 33. Mohamad F.S, Abdul Manaf A. and Chuprat S., (2012). Exploiting Suitable Color Model for Ripeness Identification. Proceedings of the 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2012, 1, pp. 483 – 488. 34. Mohamad F.S, Abdul Manaf A. and Chuprat S., (2011). The Use of Elimination Method and Nearest Neighbor for Oil Palm Fruit Ripeness Indicator. ICSECS 2011, 1(179), pp.674 – 686. 35. Mohamad F.S, Abdul Manaf A. and Chuprat S., (2012). Exploiting Distance Measurement for Ripeness Identification. IJCSI International Journal of Computer Science, 9(3), pp. 105 – 108. 36. Muhammad Makky and Peeyush Soni, (2013). Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision. Computer and Electronic in Agriculture, 93,pp. 129 – 139. 37. Noor Hasmiza H., Norhisam M., Roslina M.S., Ishak A., Desa A., Hiroyuki W. and Kinihisa T., (2013). Investigations on a Novel Inductive Concept Frequency Technique for the Grading of Oil Palm Fresh Fruit Bunches. Sensors 2013, 13(2), pp. 2254 – 2266. 38. Norasyikin F. and Junita M.S., (2014). Color Feature Extraction of Oil Palm Fresh Fruit Bunch Image for Ripeness Classification. Proceedings of the 13th International Conference on Applied Computer and Applied Computational Science (ACACOS '14), Kuala Lumpur, Malaysia, 23 – 25 April 2014. 39. Nureize A. and Watada J., (2010). A fuzzy regression approach to a hierarchical evaluation model for oil palm fruit grading. Fuzzy Optim Decis Making (2010, 9(1), pp.105 – 122. 40. Nursuriyati J., Azlina M. and Syazwani A., (2009). Automated Grading of Palm Oil Fresh Fruit Bunches (FFB) using Neuro-Fuzzy Technique. International Conference of Soft Computing and Pattern Recognition, Washington, USA, 4 – 7 December 2009. 41. Ozden Bayazit, (2005). Use of AHP in Decisio-making for Flexible Manufacturing System. Journal of Manufacturing Technology Management, 16(7), pp. 808 – 819. 42. Ramanathan R., (2001). A note on the use of the Analytic Hierarchy Process for Environmental Impact Assessment. Journal of Environmental Management (2001), 63, pp. 27 – 35. 43. Rosdiyana S. and Fauziah Z., (2013). Oil Palm Fruit Maturity Grading System Using Computer Vision Technique. Malaysian Technical Universities Conference on Engineering & Technology (MUCET) 2013, Kuantan, Pahang, 3 – December 2013. 44. Roseleena, Nursuriati, Ahmad and Low., (2011). Assessment of palm oil fresh fruit bunches using photogrammetric grading system. Universiti Teknologi MARA, Malaysia. International Food Research Journal, 18(3), pp. 999-1005. 45. Saravanan S. and Mahendran P., (2014). An AHP Based Approach – Selection of Measuring Instrument for Engineering Institution Selection. Asia Pacific Journal of Research. I(XIV), pp. 143 – 150. 46. Shahroodi. Kambiz., (2012). Application of Analytical Hierarchy Process (AHP) Technique To Evaluate and Selecting Suppliers in an Effective Supply Chain. Kuwait Chapter of Arabian Journal of Business and Management Review, 1(8), pp. 119 – 132. 47. Subin, B.M., and Subin, G. M., (2015). Improving Production Efficiency by Modifying Employee Scheduling System at a Palm Oil Industry. International Journal of Science Technology & Engineering, 2(5), pp. 135 – 138. 48. Tajuddin Bantacut, Suprihatin & Panji Maulana., (2014). Application of Cleaner Production in Palm Oil Mill: A Case Study at PT Perkebunan Nusantara IV Adolina Business Unit Perbaungan North Sumatera Indonesia. Chemistry and Materail Research, 6(12), pp. 178 – 187. 49. Thomas L. Saaty., (1990). How to make a decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48(1990), pp. 9 – 26. 50. Thomas L. Saaty., (2008). Decision making with the analytic hierarchy process. Int. J. Services Sciences, 1(1), pp. 83 – 98. 51. Wu, J. and Wu, N., (1984). Analyzing Multi-dimensional Attributes for the Single Plant Location Problem via an Adaption of The Analytic Hierarchy Process, International Journal of Operations & Production Management, 4(3), pp. 13 – 21. 52. Yudi R.P, Norfialdi and Kardiman., (2014). Aplikasi Metode AHP (Analytical Hierarchy Process) Dalam Menganalisis Indikator Kinerja Kunci Rantai Pasok Tandan Buah Segar Kelapa Sawit di PT.XYZ. Jurnal Agribisnis Kerakyatan, 4(1), pp. 12 – 24. 53. Zavbi R. and Duhovnik J., (1996). The analytic hierarchy process and functional appropriateness of components of technical system. Journal of Expert System with Applications, 33(1), pp. 86 – 95.

Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch

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