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Algoritma K-Means Clustering dalam Optimalisasi Komposisi Pakan Ternak Ayam Petelur

Felka Andini, Della Zilfitri, Yosep Filki, Muhammad Ridho

Abstract: In Indonesia, the laying hens business sector experiences many obstacles, farmers often face instability between the price of chicken eggs and the price of feed which tends to always increase. The income received by farmers is not proportional to the cost of feed incurred. The production cost of laying hens can be reduced if there is an increase in feed efficiency. Maintenance of laying hens lies in the provision of feed, water, physical conditions and the state of the cage. Feed is the main source of energy for laying hens. The problem of feed in laying hens must meet the quality and quantity of the feed itself so that the effect is very real and clear on egg production. Feed nutrition must also meet the needs of laying hens. Feeding laying hens without paying attention to the quality of the feed can result in the growth and productivity of chickens being not optimal. Combining feed is an effort that can be made to produce a quality feed composition. This research was conducted to compile the composition of laying hens' feed using the K-Means Clustering algorithm. The K-Means Clustering method is an algorithm used by researchers to group or cluster data on laying hens feed into several clusters by using the nutritional content of each feed as an attribute. In this study, the data analyzed was data on the nutritional content of laying hens feed consisting of attributes such as protein, fat, crude fiber, calcium and phosphorus. This study will produce 3 clusters of feed types consisting of highly optimal clusters, optimal clusters and less than optimal clusters. This research is expected to be used as a recommendation by laying hens in compiling the composition of laying hens to maintain the quality of the eggs produced.
Keywords: optimal / feed / laying hens / K Means Clustering / compile

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