MARC details
| 000 -LEADER |
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| fixed length control field |
02588ntm a2200277 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000099229 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113326.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
170420t2016 my a f am 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0001252(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
201905271345 |
| Level of effort used to assign nonsubject heading access points |
atie |
| -- |
201704201041 |
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saini |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FSKKP .S25 2016 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Salidy Chindamanee Chamlong |
| 245 10 - TITLE STATEMENT |
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| Title |
Tomato ripeness classification using artificial neural network / |
| Statement of responsibility, etc. |
Salidy Chindamanee A/P Chamlong |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 85 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computer Systems and Software Engineering |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Project paper (Bachelor of Computer Science (Computer Systems & Networking) With Honours) -- Universiti Malaysia Pahang – 2016 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 55-56 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
The classification of tomato into its maturity level can be determined by several parameters such as size, shape and color. It is important to classify it before send to markets, because to import to distant market, the tomato should be pack at colour break stage and for local market, it should be pack when it is fully red. The purpose of this research is to proposed neural network algorithm for classification and identifying the maturity level, by which this algorithm can be run in the software called “Matlab” by using the Matlab code. In this research, the parameter that will be used to identify the maturity of the tomato is external color. At start, some of the tomato image will be prepare, which include image for ripe tomato, halp ripe tomato and unripe tomato. Then, each of the image will be extract its rgb value using Matlab. After obtain the value, this value will be manually rescale. Next ANN algorithm will be train and test by perform some calculation to classify the tomato into stage. Some sample data sets is prepared used to train the network and backpropagation algorithm will be use. The trained data then will produce MSE and RMSE value to show the difference of the actual result with the expected one. From this study, the neural network model for tomato classification has achieved the best MSE value of 0.009972142. The colour of tomato is believed to be the major method in determining its maturity. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computer Systems and Software Engineering |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and Colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |