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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Journal of Economic, Bussines and Accounting (COSTING) Jurnal Riset Informatika JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Community Empowerment Reputasi: Jurnal Rekayasa Perangkat Lunak Conten : Computer and Network Technology Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Penamas: Journal of Community Service Jurnal Indonesia Sosial Teknologi Jurnal Pengabdian Masyarakat Bangsa
Gunawan Budi Sulistyo
Universitas Bina Sarana Informatika
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Journal : Jurnal Riset Informatika

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Stunting Early Warning Application Using KNN Machine Learning Method Nani Purwati; Gunawan Budi Sulistyo
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.550

Abstract

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still quite high, coupled with the COVID-19 pandemic which has had quite an impact on the economic sector. For this reason, research on stunting is still a very important topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm, as well as build a website-based early detection application for stunting toddler cases using the CodeIgniter framework with the PHP programming language. The results of the research using the k-Nearest Neighbor Algorithm trial obtained a fairly high accuracy of 92.45%. The implementation of an early detection system for stunting cases has proven to help and facilitate health workers in classifying toddlers as stunted or not. This application is also useful as an archive and facilitates data reporting. In the application there are 8 main menus, namely the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, stunting early warning menu which contains malnourished toddlers, stunted toddlers.
Stunting Early Warning Application Using KNN Machine Learning Method Nani Purwati; Gunawan Budi Sulistyo
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1223.901 KB) | DOI: 10.34288/jri.v5i3.228

Abstract

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still relatively high, coupled with the COVID-19 pandemic, which has impacted the economic sector. For this reason, research on stunting is still a critical topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm and build a website-based early detection application for toddler stunting cases. The research results using the k-Nearest Neighbor Algorithm trial obtained a relatively high accuracy of 92.45%. Implementing an early detection system for stunting cases has proven to help health workers classify toddlers as stunted or not. This application is also helpful as an archive and facilitates data reporting. The application has eight main menus: the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, and stunting early warning menu, which contains malnourished and stunted toddlers.
Co-Authors - UBSI Yogyakarta, Linda Saputri Adjie, Bayu Kresna Akhmad Syukron Anggraeni Sari Rumpoko Artatik, Finayusli Andriani Atabi, Kuffi Bayu Kresna Adjie Candra Agustina eka rahmawati Elly Muningsih Fauzi, Muhammad Naufal Zul Fransisca Heny Widayati UBSI Kuwwamri, Sawwara Lina Ayu Safitri Marcha Putty Kusumaning Tyas - Universitas Bina Sarana Informatika Miftahul Luthfi Muhammad Naufal Zul Fauzi Nani Purwati Noor Hasan Noor Hasan Pudji Widodo Pudji Widodo Reza Ardianto Sardiarinto Sardiarinto Sri Kiswati Tri Wahyudi