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Open access Jurnal SINTA 5

KLASIFIKASI STATUS GIZI BAYI MENGGUNAKAN METODE K-NEAREST NEIGHBOR

AnoaTIK: Jurnal Teknologi Informasi dan Komputer · Vol. 3 No. 2 (2026) · pp. 77–83

DOI
10.33772/anoatik.v3i2.117
ISSN
2987-7652
Published
2026-01-18
Publisher
Program Studi Ilmu Komputer FMIPA-UHO
Language
ind
License
creativecommons.org/licenses/by-sa/4.0

Abstract

English

The development of a baby nutritional status classification system was carried out by applying the K-Nearest Neighbor (KNN) method with the aim of supporting more accurate monitoring of infant growth and development. The system determines the nutritional status of infants based on input data including age, weight, height, and mid-upper arm circumference, which are then compared with available training data. The system development process employed the waterfall approach, encompassing requirements analysis, system design, implementation, and testing stages. Testing was conducted using black box testing to ensure that all system functions operated according to requirements, as well as a confusion matrix to measure classification accuracy. The results showed that all system features functioned properly and the achieved accuracy rate reached 97.30%, indicating that the system has very good performance in effectively supporting the monitoring of infant nutritional status.

Bahasa Indonesia

Pengembangan sistem klasifikasi status gizi bayi dilakukan dengan menerapkan metode K-Nearest Neighbor (KNN) yang bertujuan untuk membantu pemantauan tumbuh kembang bayi secara lebih akurat. Sistem ini menentukan status gizi bayi berdasarkan data yang diinputkan, meliputi umur, berat badan, tinggi badan, dan lingkar lengan atas, kemudian membandingkannya dengan data pelatihan yang telah tersedia. Proses pengembangan sistem menggunakan pendekatan waterfall yang mencakup tahap analisis kebutuhan, perancangan sistem, implementasi, hingga pengujian. Pengujian dilakukan dengan metode black box testing untuk memastikan seluruh fungsi sistem berjalan sesuai dengan kebutuhan, serta confusion matrix untuk mengukur tingkat akurasi klasifikasi. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi dengan baik dan tingkat akurasi yang diperoleh mencapai 97,30%, sehingga sistem dinilai memiliki kinerja yang sangat baik dalam mendukung pemantauan status gizi bayi secara efektif.

Keywords

Collaboration network of the authors

19 authors · 47 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Universitas Gadjah Mada
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
La SurimiUniversitas Halu Oleo3
Gusti Arviana RahmanUniversitas Halu Oleo2
Andi TenriawaruUniversitas Halu Oleo2
SahrulAffiliation unknown2
Dian Safitri DurukaUniversitas Halu Oleo1
Umu KhabibahUniversitas Halu Oleo1
Amal AnugraUniversitas Halu Oleo1
Gunawan GunawanUniversitas Halu Oleo1
La Ode Muhammad Hafidz Abdillah Sam MongkitoUniversitas Halu Oleo1
Budi Wijaya RaufUniversitas Halu Oleo1
Ferdinand Murni HamunduUniversitas Halu Oleo1
Fazlul Rachmat MubbaraqUniversitas Halu Oleo1
Achirul Slamet SugiartoAffiliation unknown1
La Ode SaidiAffiliation unknown1
Ferdinand Murni HamunduAffiliation unknown1
Edi WinarkoUniversitas Gadjah Mada1

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://anoatik.uho.ac.id/index.php/atik/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id-ID">KLASIFIKASI STATUS GIZI BAYI MENGGUNAKAN METODE K-NEAREST NEIGHBOR</dc:title>
	<dc:creator>Lestari, Sri Ayu</dc:creator>
	<dc:creator>Ransi, Natalis</dc:creator>
	<dc:creator>Arfan, Muhammad</dc:creator>
	<dc:subject xml:lang="id-ID">Sistem Informasi</dc:subject>
	<dc:subject xml:lang="id-ID">black box testing</dc:subject>
	<dc:subject xml:lang="id-ID">Confusion Matrix</dc:subject>
	<dc:subject xml:lang="id-ID">Waterfall</dc:subject>
	<dc:subject xml:lang="id-ID">K-Nearest Neighbors (K-NN)</dc:subject>
	<dc:description xml:lang="en-US">&amp;nbsp;
The development of a baby nutritional status classification system was carried out by applying the K-Nearest Neighbor (KNN) method with the aim of supporting more accurate monitoring of infant growth and development. The system determines the nutritional status of infants based on input data including age, weight, height, and mid-upper arm circumference, which are then compared with available training data. The system development process employed the waterfall approach, encompassing requirements analysis, system design, implementation, and testing stages. Testing was conducted using black box testing to ensure that all system functions operated according to requirements, as well as a confusion matrix to measure classification accuracy. The results showed that all system features functioned properly and the achieved accuracy rate reached 97.30%, indicating that the system has very good performance in effectively supporting the monitoring of infant nutritional status.</dc:description>
	<dc:description xml:lang="id-ID">Pengembangan sistem klasifikasi status gizi bayi dilakukan dengan menerapkan metode K-Nearest Neighbor (KNN) yang bertujuan untuk membantu pemantauan tumbuh kembang bayi secara lebih akurat. Sistem ini menentukan status gizi bayi berdasarkan data yang diinputkan, meliputi umur, berat badan, tinggi badan, dan lingkar lengan atas, kemudian membandingkannya dengan data pelatihan yang telah tersedia. Proses pengembangan sistem menggunakan pendekatan waterfall yang mencakup tahap analisis kebutuhan, perancangan sistem, implementasi, hingga pengujian. Pengujian dilakukan dengan metode black box testing untuk memastikan seluruh fungsi sistem berjalan sesuai dengan kebutuhan, serta confusion matrix untuk mengukur tingkat akurasi klasifikasi. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi dengan baik dan tingkat akurasi yang diperoleh mencapai 97,30%, sehingga sistem dinilai memiliki kinerja yang sangat baik dalam mendukung pemantauan status gizi bayi secara efektif.</dc:description>
	<dc:publisher xml:lang="id-ID">Program Studi Ilmu Komputer FMIPA-UHO</dc:publisher>
	<dc:date>2026-01-18</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://anoatik.uho.ac.id/index.php/atik/article/view/117</dc:identifier>
	<dc:identifier>10.33772/anoatik.v3i2.117</dc:identifier>
	<dc:source xml:lang="en-US">AnoaTIK: Jurnal Teknologi Informasi &amp; Komputer; Vol. 3 No. 2 (2025): Desember 2025; 77-83</dc:source>
	<dc:source xml:lang="id-ID">AnoaTIK: Jurnal Teknologi Informasi dan Komputer; Vol 3 No 2 (2025): Desember 2025; 77-83</dc:source>
	<dc:source>2987-7652</dc:source>
	<dc:source>10.33772/anoatik.v3i2</dc:source>
	<dc:language>ind</dc:language>
	<dc:relation>https://anoatik.uho.ac.id/index.php/atik/article/view/117/42</dc:relation>
	<dc:rights xml:lang="id-ID">Hak Cipta (c) 2026 Sri Ayu Lestari, Natalis Ransi, Muhammad Arfan</dc:rights>
	<dc:rights xml:lang="id-ID">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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