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All Journal JPM (Jurnal Pemberdayaan Masyarakat) Mulia International Journal in Science and Technical Jurnal Ilmiah Manusia dan Kesehatan 'Arabiyya: Jurnal Studi Bahasa Arab KATALIS: Jurnal Penelitian Kimia dan Pendidikan Kimia Multica Science and Technology Journal of Medical Science; Jurnal Ilmu Medis Rumah Sakit Umum Daerah dr. Zainoel Abidin, Banda Aceh International Conference on Health Science, Green Economics, Educational Review and Technology (IHERT) MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Penelitian dan Pendidikan Agama Islam Inovasi Kesehatan Global
Mauliza
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Religion Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : Multica Science and Technology

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APPLICATION OF MACHINE LEARNING IN PREDICTING CHILDREN'S NUTRITIONAL STATUS WITH MULTIPLE LINEAR REGRESSION MODELS Mutammimul Ula; Ananda Faridhatul Ulva; Mauliza; Muhammad Abdullah Ali; Yumna Rilasmi Said
Multica Science and Technology Vol 2 No 2 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i2.363

Abstract

Forecasting is an important part of making plans and making decisions that can predict future events. Forecasting techniques in this study used multiple linear regression. This study aims to predict the number of cases of child nutritional status in children in each region. The purpose of this study was to see the results of predicting the number of children's nutritional status in each region and to make it easier to predict children's nutrition. The research method includes the analysis of the system built and the design of machine learning applications using the Multiple Linear Regression method. Then the system built can help predict the nutritional status of children in Aceh quickly, precisely, and accurately. The data used is data on the nutritional status of children in 2018, 2019, and 2020. Based on the results of forecasting for 2021 based on data obtained in previous years, the predicted results of total nutritional status in 2021 are 449,0912126. The results of this study indicate that the linear regression method obtains the best model results by being able to predict the implementation of machine learning.
Co-Authors Ananda Faridhatul Ulva Aprillya, Asri Cut Khairunnisa Desti, Meltri Rahma Ermatita - Eva Darnila Halimah Harvina Sawitri Hendra Raza Intan Badola Iwan Pahendra Anto Saputra Laode Irdana Muhammad Abdullah Ali Murtala Mutammimul Ula Nariza Yanti Nasution, Herry Syahbannuddin Novita Andayani, Novita Nurhafidhah Oktaviani, Coryna Parinduri, Sari Atika Richki Hardi Safril Maulida Sahirman Salumaiyah Siregar, Peni Rahmadani Syukran Teuku Nih Farisni Venanda, Natasya Yumna Rilasmi Said