Open AccessCombination of analytical hierarchy processes with fuzzy c- means in selecting quality broiler chicken
Author(s) -
Dudih Gustian,
Deni Hasman,
Dedi Supardi,
Siti Nurjanah,
Agus Darmawan,
Iswatun Mei Suciati,
Ria Dewi Hundayani
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/472/1/012048
Subject(s) - broiler , cluster (spacecraft) , quality (philosophy) , fuzzy logic , analytic hierarchy process , process (computing) , mathematics , hierarchy , poultry farming , agricultural science , microbiology and biotechnology , statistics , biology , computer science , operations research , zoology , economics , ecology , artificial intelligence , philosophy , epistemology , market economy , programming language , operating system
One of the key points in broiler chicken farming is in the initial process of selecting chicken seeds or known as DOC. The problem that emerged in the field is there are so many options of broiler breeds available from Breeding Farm, making it difficult for farmers and companies to choose the good quality broiler chickens. This research uses Analytical Hierarchy Combination Process for knowing which sequence is influenced on choosing process, in order to produce the good quality disease-free chicken seeds with standard variables; the highest weight is 0.207, followed by normal body 0.174 then weight according to the standard with a value of 0.172. Fuzzy C-Means is used for cluster processes in monitoring the quality levels of each chicken breeding process with results divided into 3 clusters. They are cluster 1 with as many as 410 breeders (96.70%), cluster 2 with 12 breeders (2.83%) and clusters 3 with 2 farmers (0.47%). With this method, companies can reduce losses caused by mistakes from these three parts and company profits increases.