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All Journal Jurnal Ilmu-Ilmu Pertanian Indonesia Journal of Degraded and Mining Lands Management InComTech: Jurnal Telekomunikasi dan Komputer BASIS (BAHASA DAN SASTRA INGGRIS) AGRITROPICA : Journal of Agricultural Sciences Kamaya: Jurnal Ilmu Agama MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JUTIS : Jurnal Teknik Informatika Jurnal Informatika dan Rekayasa Perangkat Lunak Dharmas Education Journal (DE_Journal) JURNAL ILMIAH GLOBAL EDUCATION JIMTEK Multicultural Islamic Education Review Jurnal Pedagogy Buletin Dharmas Andalas
Sukisno Sukisno, Sukisno
University Of Bengkulu
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Determining Toddler's Nutritional Status with Machine Learning Classification Analysis Approach Hidayat, Taufik; Ridwan, Mohammad; Iqbal, Muhamad Fajrul; Sukisno, Sukisno; Rizky, Robby; Manongga, William Eric
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4092

Abstract

The nutritional status of toddlers is a common issue many countries face worldwide. Various facts indicate that malnutrition is a primary focus for many researchers. Several efforts have been made to address this problem, including developing analytical models for identification, classification, and prediction. This study aims to evaluate the nutritional status of children by utilizing a classification analysis approach using Machine Learning. This research aims to improve the accuracy of the classification system and facilitate better decision-making in stunted toddlers, which is a priority, especially in the health sector. The Machine Learning classification analysis process will later utilize the performance of the Naive Bayes algorithm, the Support Vector Machine algorithm, and the Multilayer Perceptron algorithm. ML performance can be optimized using gridsearchCV to produce optimal classification analysis patterns. The data set of this study uses 6812 toddler data sourced from the Health Center at the Tangerang Regency Health Office. Based on the research presented, Machine Learning performance in analyzing nutritional status classification provides maximum results. The results are reported based on a precision level with an accuracy of 88%. The results of this analysis can also present a classification of nutritional status based on knowledge. This study can contribute to and update the analysis model in determining nutritional status. The results of this study can also provide benefits in handling nutritional status problems that occur in children.
Co-Authors Agustina, Cindi al afgani, jamal udin Anwar, Sholihul Apri Andani, Apri Armin Nurhartanto, Armin Bambang Pramudya Noorachmat, Bambang Pramudya Bidarita Widiati Chaeruman, Uwes Anis Dwi Wahyuni Ganefianti, Dwi Wahyuni Dwijonagoro, Suwarna Fathiaty Murtadho Febri Rismaningsih, Febri Haryanto Haryanto Herison, Catur Iqbal, Muhamad Fajrul Istikomah , Erna Januardi, Dedi Khursatul Munibah Kurniawan, Hanif Fajar Manongga, William Eric Maulana, Rheza Mimi Sutrawati Mobarrok, Abi Mohammad Ridwan Muhamad Irsan Mujiyanto Mujiyanto Nia Komalasari Ninuk Lustyantie Nugrogo, Asep Hardiyanto Nugroho, Asep Hardiyanto Nurjanah, Uswatun Nyanasuryanadi, Partono Paris, Atika Salman Purwanto, Yanuar Jarwadi Putra, Ismail Restutomo Herjiwandono, Bayu Ringki Putra Azalika Rizky, Robby Rustikawati Rustikawati Saputra, Pringky Okta Setyawati, Metta SITI ROSYIDAH, SITI Sukrim, Sukrim Sumardi Sumardi Sura Menda Ginting Taufik Hidayat Widiatmaka Widiatmaka Yulandari, Evi Safitri