03394ntm a2200361 i 4500 vtls000104342 KUKTEM 20251117113438.0 180719s2018 my a f a m 000 0 eng d THE0008051(Local) 201906181320 amy2 201808131529 fateeha 201807191610 fateeha UMP eng UMP rda FKEE .N87 2018 r Thesis Nurdiyana Zahed, author. Intelligent odour profile classification of honey using Case Based Reasoning and K-Nearest Neighbors / Nurdiyana Zahed Kuantan, Pahang : UMP, 2018 xii, 102 pages : illustrations (some color) ; 30 cm. + 1 CD ROM text rdacontent text rdacontent unmediated rdamedia computer rdamedia volume rdacarrier computer disc rdacarrier text file PDF rda Faculty of Electrical and Electronics Engineering Thesis (Master of Science) -- Universiti Malaysia Pahang – 2018 Includes bibliographical references Honey produced by sting bee contains several important nutrients that can be used as food additive, medicinal food and food preservative. In Malaysia, three most common types of pure honey are Gelam, Acacia, and Tualang which can be verified from Department of Agriculture Malaysia. Honey can be classified using several methods and one of them is by referring to its botanical origin (botanical place where the honey is collected) which influence the quality of honey, its market price and benefits. Various techniques can be found in classifying honey where odour profile is amongst them. In order to establish honey odour profile, electronic nose (E-nose) is one of the significant instrument that can be used for honey odour profile measurement and detection. E-nose comprises of three major components; chamber, microcontroller and sensor arrays that capable of establishing odour profile pattern. In order to get E-nose response measurement, it has been validated using Gas Chromatography Mass Spectrometry (GC-MS) which was pre-processed using Solid Phase Micro-extraction (SPME). Three datasets of three different groups of honey from E-nose were measured in electrical signal for odour detection. The measured resistance data was pre-processed using normalization technique to standardize the scale of sample features. Mean values were extracted as selected feature and statistical methods using proximity matrix and boxplot data pattern graphical representation according to types of honey. Mean features for Case-based Reasoning (CBR) cases that have been extracted were employed into two types of classifiers; intelligent classification using CBR and K-Nearest Neighbors (KNN). Both CBR and KNN performances have been evaluated. The results have shown 100% rate of accuracy, sensitivity and specificity of classification from CBR classifier and KNN using weigh (k=1), ratio 90:10 and Euclidean distance. Faculty of Electrical and Electronics Engineering Dissertations Universities and colleges Disertations Theses 0 0 0 -2 20000 20000 2019-09-04 0 FKEE .N87 2018 r Thesis 0000123598 2019-09-04 00:00:00 1 2019-09-04 RESTRICT 0 0 0 -2 20000 20000 2019-09-04 0 CD 11391 | FKEE .N87 2018 r Thesis 0000123599 2019-09-04 00:00:00 1 2019-09-04 RESTRICT VIRTUA40 8800 8806 VTLSSORT0010*0030*0050*0080*0200*0390*0400*0900*1000*2450*2640*3000*3360*3361*3370*3371*3380*3381*3470*5000*5020*5040*5200*6100*6500*6501*9990*9992