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Journal : Clean Energy and Smart Technology

CLASSIFICATION OF NUTRITIONAL STATUS USING K-NEAREST NEIGHBOR (KNN) METHOD Lina Fauziah; Hidayatus Sibyan; Nur Hasanah; Iman Ahmad Ihsannudin; Nulngafan
Clean Energy and Smart Technology Vol. 1 No. 2 (2023): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v1i2.38

Abstract

According to a statement from the chairman of the Ngadimulyo village cadres, the prevalence of stunting in five-year-old babies (toddlers) in Ngadimulyo village in 2022 is 20%, which means there are 32 stunted toddlers out of 160 toddlers in Ngadimulyo village. The first 1000 days are the golden age for babies, but many toddlers aged 0-59 months still experience nutritional problems. In interviews with several mothers in Ngadimulyo village who have toddlers, many mothers still do not understand the calculation of nutritional status; they only rely on calculations from posyandu cadres recorded in the MCH book (Maternal and Child Health). Data recording is still conventional and produces physical data in the form of books, so data storage also has many risks, such as damage and loss. To reduce the risk of stunting, a system is needed that facilitates the calculation of under-five nutrition so that parents can independently calculate the nutritional status of their under-fives. Currently, many parents are still assisted in determining stunting criteria. Currently, parents only get stunting knowledge from counseling, so parents who have toddlers do not understand to analyze stunting symptoms independently. They are unaware if stunting occurs; therefore, with a system that can assist parents in calculating stunting rates, it is hoped that parents can provide maximum nutritional intake so that stunting cases decrease. Parents can also prevent stunting from occurring. One of the systems used to calculate the nutritional status classification of toddlers is the K-Nearest Neighbor Method (KNN). The reason for choosing the KNN method is because this method can meet other variables in determining the nutritional status of toddlers and is one of the most basic and simple grouping techniques.
EXPERT SYSTEM TO DIAGNOSE COW DISEASE USING WEBSITE-BASED DEMPSTER-SHAFER ALGORITHM Mufa’at Hadi Rosid; Muhamad Fuat Asnawi; Nur Hasanah
Clean Energy and Smart Technology Vol. 2 No. 1 (2023): October
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v2i1.46

Abstract

Cows are one of the livestock whose existence can meet various kinds of human needs because they are the main producer of animal protein such as meat or milk. However, there are problems that cause failure in running a cattle farm. One of the problems is the health factor of the cattle which is disturbed due to a disease. Limited knowledge and livestock health workers become an obstacle for cattle breeders in analyzing the possibility of disease affecting cattle based on the symptoms that appear. For this reason, an expert system was built that can diagnose cow disease based on a website which can later be an alternative when someone has limited access to experts to diagnose the disease he is suffering from. The dempster-shafer algorithm was chosen because it is able to provide certainty in performing diagnostic calculations. From the results of this study, an expert system for diagnosing cattle disease has been developed using the PHP programming language which can implement the algorithm and can run well, as evidenced by passing the testing phase using the black box test method and also successfully passing the accuracy test with experts. with a percentage of 86.67%.