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All Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics
Khurun Ain Muzaqi
Institut Teknologi Telkom Purwokerto
Computer Science & IT

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Klasifikasi Status Gizi Pada Lansia Menggunakan Learning Vector Quantization 3 (LVQ 3) Khurun Ain Muzaqi; Apri Junaidi; Wahyu Andi Saputra
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 1 (2022): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i1.272

Abstract

The Elderly is someone who has reached the age of 60 years, the main health problem in the elderly is nutritional problems. Nutritional status is a measurement that can assess food intake and the use of nutrients in the body. One of the assessments of nutritional status in the elderly uses anthropometry with the type of measurement of Body Mass Index (BMI). Determination of nutrition is an effort to increase Life Expectancy (UHH). Therefore, a study will be conducted on the classification of nutritional status in the elderly using the Learning Vector Quantization 3 (LVQ 3) method with seven inputs used, namely: gender, age, Bb, Tb, BMI, social status and disease history, and five results of status classification nutritional status, namely inferior nutritional status, poor nutritional status, normal nutritional status, obese nutritional status, and very obese nutritional status. The best parameters used in this study are: learning rate (α) = 0.2, learning rate reduction = 0.4, window (ɛ) = 0.4 and minimum learning rate = 0.001, epoch = 1, 5, 10, 50, 100, 200, 500, 1000 with a comparison of the distribution of training and testing data of 80:20 on a total of 599 data. Based on the test results, the number of epoch values affects the accuracy results. The highest accuracy obtained is 86.67%. The calculations using the confusion matrix in this algorithm are 87% accuracy, 83% precision, and 81% recall. The Learning Vector Quantization 3 (LVQ 3) method can use to classify nutritional status in the elderly.
Co-Authors Apri Junaidi Wahyu Andi Saputra