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Optimasi Model Deep Learning untuk Klasifikasi Stunting berdasarkan Data Antropometri dan Status Imunisasi

SemanTIK: Teknik Informasi · Vol. 11 No. 2 (2025)

DOI
10.55679/semantik.v11i2.233
ISSN
2502-8928, 2460-1446
Published
2025-12-16
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Stunting adalah masalah gizi jangka panjang yang memengaruhi pertumbuhan dan perkembangan anak. Klasifikasi status stunting sangat penting untuk mencegah dampak negatif jangka panjang terhadap kualitas hidup anak. Penelitian ini bertujuan untuk mengembangkan dan mengoptimalkan model Deep Learning dengan arsitektur Multilayer Perceptron (MLP), yaitu jenis jaringan syaraf tiruan berlapis yang mampu mempelajari pola non-linear secara efektif, dalam klasifikasi status stunting berdasarkan data Antropometri dan status imunisasi. Dataset yang digunakan terdiri dari 78 data balita, dengan distribusi seimbang antara stunting dan normal. Data yang digunakan masih tergolong kecil karena hanya mengambil sampel pada satu puskesmas. Model dilatih dengan algoritma Adam, menggunakan proses normalisasi data dan teknik early stopping untuk mencegah overfitting. Hasil evaluasi model menunjukkan kinerja yang sangat baik, dengan akurasi mencapai 87.5%, precision 1.00, recall 0.78, dan F1 Score 0.875. Temuan ini menunjukkan bahwa pendekatan berbasis MLP dapat menjadi alternatif yang efektif dalam mendukung proses klasifikasi status stunting secara otomatis dan akurat. Stunting is a long-term nutritional problem that affects children's growth and development. Early detection of stunting status is very important to prevent long-term negative impacts on children's quality of life. This research aims to develop and optimise Deep Learning models with Multilayer Perceptron (MLP) architecture in the classification of stunting status based on Anthropometric data and immunisation status. The dataset used consists of 78 toddler data, with a balanced distribution between stunting and normal. The model was trained with Adam's algorithm, using data normalisation process and early stopping technique to prevent overfitting. The model evaluation results showed excellent performance, with accuracy reaching 87.5%, precision 1.00, recall 0.78, and F1 Score 0.875. These findings suggest that MLP-based approaches can be an effective alternative in supporting the automatic and accurate classification of stunting status.

Keywords

Collaboration network of the authors

5 authors · 6 co-authorship links

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  • Universitas Sembilanbelas November Kolaka
  • Affiliation unknown
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Co-authorOrganisationJoint articles
MardiawatiAffiliation unknown1
Andi IlhamUniversitas Sembilanbelas November Kolaka1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://semantik.uho.ac.id/index.php/journal/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Optimasi Model Deep Learning untuk Klasifikasi Stunting berdasarkan Data Antropometri dan Status Imunisasi</dc:title>
	<dc:creator>mardiawati, mardiawati</dc:creator>
	<dc:creator>Paliling, Alders</dc:creator>
	<dc:creator>Mutmainnah, Nurul</dc:creator>
	<dc:subject xml:lang="en-US">Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Multilayer Perceptron</dc:subject>
	<dc:subject xml:lang="en-US">Stunting</dc:subject>
	<dc:subject xml:lang="en-US">Klasifikasi</dc:subject>
	<dc:subject xml:lang="en-US">Optimasi Model</dc:subject>
	<dc:description xml:lang="en-US">Stunting adalah masalah gizi jangka panjang yang memengaruhi pertumbuhan dan perkembangan anak. Klasifikasi status stunting sangat penting untuk mencegah dampak negatif jangka panjang terhadap kualitas hidup anak. Penelitian ini bertujuan untuk mengembangkan dan mengoptimalkan model Deep Learning dengan arsitektur Multilayer Perceptron (MLP), yaitu jenis jaringan syaraf tiruan berlapis yang mampu mempelajari pola non-linear secara efektif, dalam klasifikasi status stunting berdasarkan data Antropometri dan status imunisasi. Dataset yang digunakan terdiri dari 78 data balita, dengan distribusi seimbang antara stunting dan normal. Data yang digunakan masih tergolong kecil karena hanya mengambil sampel pada satu puskesmas. Model dilatih dengan algoritma Adam, menggunakan proses normalisasi data dan teknik early stopping untuk mencegah overfitting. Hasil evaluasi model menunjukkan kinerja yang sangat baik, dengan akurasi mencapai 87.5%, precision 1.00, recall 0.78, dan F1 Score 0.875. Temuan ini menunjukkan bahwa pendekatan berbasis MLP dapat menjadi alternatif yang efektif dalam mendukung proses klasifikasi status stunting secara otomatis dan akurat.
Stunting is a long-term nutritional problem that affects children's growth and development. Early detection of stunting status is very important to prevent long-term negative impacts on children's quality of life. This research aims to develop and optimise Deep Learning models with Multilayer Perceptron (MLP) architecture in the classification of stunting status based on Anthropometric data and immunisation status. The dataset used consists of 78 toddler data, with a balanced distribution between stunting and normal. The model was trained with Adam's algorithm, using data normalisation process and early stopping technique to prevent overfitting. The model evaluation results showed excellent performance, with accuracy reaching 87.5%, precision 1.00, recall 0.78, and F1 Score 0.875. These findings suggest that MLP-based approaches can be an effective alternative in supporting the automatic and accurate classification of stunting status.</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2025-12-16</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://semantik.uho.ac.id/index.php/journal/article/view/233</dc:identifier>
	<dc:identifier>10.55679/semantik.v11i2.233</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 11 No. 2 (2025): Vol. 11 No. 2 (2025): SemanTIK Teknik Informasi</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://semantik.uho.ac.id/index.php/journal/article/view/233/52</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 mardiawati mardiawati, Alders Paliling, Nurul Mutmainnah</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>