Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun

Hafizan, Hananda; Putri, Anggita Nadia

doi:10.30645/kesatria.v1i2.23

Cited by 7 publications

(7 citation statements)

References 1 publication

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“…Perhitungan gizi balita yang banyak jumlahnya dan dihitung secara manual, artinya prosesnya sangat memakan waktu dan sering menimbulkan kesalahan dalam memasukan data membuat proses tidak efisien. Klasifikasi yang merupakan bagian dari data mining dapat membuat keputusan status gizi balita menjadi lebih cepat dan efisian dengan melihat pola pada data data sebelumnya [12].…”

Section: Pendahuluanunclassified

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Setiawan

Triayudi²

2022

mib

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One of the efforts in increasing the progress of a country is improving the quality of nutrition. Good balanced nutrition can increase the body's endurance, increase the level of intelligence and make a person develop normally. Infancy is a period of development and growth that requires more nutritional content than other age groups, making it the most vulnerable to nutritional disorders. Nutritional status under 5 years can be seen using the Anthroperti method. Calculations done manually make the process take a long time and errors often occur in entering data. Data mining can make decisions on the nutritional status of toddlers faster by looking at patterns in previous data. Naïve Bayes and K-Nearest Neigbor are one of the methods in classification. Naïve Bayes has the advantage of being able to achieve high accuracy values with minimal training data. Meanwhile, the K-Nearest Neighbor method has the advantage of being able to work optimally on data that has random errors (noise). Using 412 nutritional data for toddlers, the results obtained with Naïve Bayes getting an accuracy of 80.60%, while with K-Nearest Neighbor, an accuracy of 91.79% was obtained.

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Section: Pendahuluanunclassified

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Setiawan

Triayudi²

2022

mib

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“…Tahapan penelitian yang digunakan untuk menganalisis data dalam penerapan ini menggunakan proses tahapan knowledge discovery in databases (KDD) yang terdiri dari Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge [4]- [6] Gambar 1. Tahapan Penelitian [7]- [10] Berdasarkan gambar 1 diatas menjelaskan bahwa tahapan penelitian ini dibagi menjadi 5 (lima) tahapan sebagai berikut [8], [11], [12] Pada tahap ini dilakukan evolusi terhadap hasil-hasil prediksi yang diperoleh algoritma decision tree.…”

Section: Metodologi Penelitian 21 Tahapan Penelitianunclassified

Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree

Zami

Nurdiawan

Dwilestari

2022

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One of the health problems in Cirebon is about nutritional status. This happens because the increase and decrease in the number of children under five who experience nutritional status problems each year is uncertain. Toddlers are a group of people who are vulnerable to nutrition. The incidence of malnutrition if not addressed will cause a bad impact for toddlers. The impacts include death and chronic infection. Early detection of undernourished children (malnutrition and malnutrition) can be done with an examination of weight for age (W/U) to monitor the child's weight, the parameters used to calculate the nutritional status of toddlers include age, weight and height/length. To classify the nutritional status of children under five, a knowledge or scientific study is needed that can classify data based on the data from measurements and weighing. This study uses 8 criteria, namely Name, Address, Mother's Name, Gender, Age, Weight, Height, Status Classification. The accuracy results obtained are 98.86% with details, namely the Prediction Results of Malnutrition and it turns out that the True Malnutrition is 13 data. Poor Nutrition Prediction Results and turns out to be True Normal by 1 Data. Normal Prediction Results and it turns out to be True Malnutrition is 1 Data. Normal Prediction Results and turns out to be True Normal of 161 data. The results of the classification of infant levels based on age, infants aged 0 months to 10 months had normal nutrition, while infants aged 10 months to 19.5 months were prone to malnutrition for infants, and those aged more than 19.5 months had poor nutrition. normal.

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“…Penentuan status gizi pada anak dengan menggunakan pengukuran antropometri melihat kriteria seperti umur, TB, BB dan terakhir ukuran pertumbuhan anak tersebut. kriteria tersebut akan diklasifikasikan menggunakan model decision tree yang akan menghasilkan status gizi anak baik, kurang dan lebih [5] [6].…”

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Application of Machine Learning in Determining the Classification of Children's Nutrition With Decision Tree

Ula

Ulva

Mauliza

et al. 2022

J. Tek. Inform. (JUTIF)

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The problem of nutrition for children is a health problem that must be solved by the government. Malnutrition is a very important problem in the development of children, especially during the growth period. Lack of nutritional intake in children will have a negative impact on resistance to the virus. This will risk death caused by malnutrition. There is direct monitoring from the government, hospitals, and health offices in looking at the classification of nutrition in children in a system. This study aims to classify the nutritional status of children using a machine learning model, which then the final result can show the classification of nutritional vulnerability in each patient at the North Aceh Hospital. The stages of the research include the identification of theories about nutritional problems. Second, data collection is in the form of symptoms and diagnosis of disease classification in machine learning implementation. The third is to analyze the data using the research and development (R&D) method according to the classification of children's nutrition. Finally, the implementation of the patient classification model with decision tree into machine learning The variables included in the system include JK(X1), U (X2), BB(X3), TB (X4), and BB (X3), which are the variables that have the most influence on malnutrition in children. The results of this study for testing weight 16, height 9.7, age 33 months, nutritional value 54,23772 which the program output results are normal. Patient Syafira Nisman, weight 9, height 72, age 21 months, suffered from malnutrition. The results of the research on the application of machine learning for the classification of malnutrition using the decision tree method make it easier for patients and hospitals to classify children's nutrition.

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Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun

Cited by 7 publications

References 1 publication

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web

Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree

Application of Machine Learning in Determining the Classification of Children's Nutrition With Decision Tree

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