Pembuatan aplikasi seluler berbasis kecerdasan buatan untuk pemantauan tumbuh kembang anak

Teny Handhayani, Puspa Indah, Meirista Wulandari, Rakha Naufal Sujana, Benny Dwiyanto, Desi Arisandi, Wasino Wasino, Jeanny Pragantha

Abstract


Abstrak

Masyarakat kawasan perkotaan (urban) dihadapkan pada tantangan mobilitas tinggi dan maraknya misinformasi kesehatan, yang berdampak pada kesenjangan literasi terkait tumbuh kembang balita. Selama ini, pemantauan gizi anak seringkali hanya mengandalkan intuisi orang tua atau kunjungan Posyandu yang kerap terlewatkan. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk memecahkan masalah tersebut dengan mengembangkan dan mengimplementasikan aplikasi pemantauan tumbuh kembang balita berbasis Android bagi mitra. Metode pelaksanaan kegiatan mencakup observasi, wawancara, perancangan aplikasi cerdas, pengujian sistem, dan sosialisasi kepada 15 responden ibu balita. Sistem ini mengintegrasikan kecerdasan buatan menggunakan model machine learning Light Gradient Boosting Machine (LightGBM) dan Extreme Gradient Boosting (XGBoost) untuk mengklasifikasikan status gizi (malnutrisi, kurang gizi, normal, kelebihan gizi) serta mendeteksi indikasi stunting. Hasil evaluasi algoritma menunjukkan performa yang sangat baik, di mana LightGBM mencapai tingkat akurasi 93%–99% dan XGBoost mencapai 92%–98%. Implementasi aplikasi yang dirancang dengan penyimpanan data lokal (offline) ini berhasil menjamin keamanan privasi pengguna sekaligus mengubah kebiasaan warga dari pemantauan berbasis intuisi menjadi pencatatan berbasis data medis yang tervalidasi. Kehadiran aplikasi ini terbukti meningkatkan kemandirian dan literasi ibu-ibu urban dalam mendeteksi risiko malnutrisi sejak dini, sekaligus mendukung pencapaian Sustainable Development Goals (SDGs) poin ke-3 (kehidupan sehat dan sejahtera) dan ke-4 (pendidikan berkualitas).

 

Kata kunci: aplikasi mobile; machine learning; pemantauan gizi balita; stunting; kesehatan masyarakat urban

 

Abstract

Urban communities face the challenges of high mobility and widespread health misinformation, which impacts the literacy gap related to toddler growth and development. To date, child nutrition monitoring often relies solely on parental intuition or often-missed Posyandu visits. This Community Service (PKM) activity aims to solve this problem by developing and implementing an Android-based toddler growth and development monitoring application for partners. The implementation method includes observation, interviews, intelligent application design, system testing, and outreach to 15 respondents who are mothers of toddlers. This system integrates artificial intelligence using the Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) machine learning models to classify nutritional status (malnutrition, undernutrition, normal, overnutrition) and detect indications of stunting. The algorithm evaluation results showed excellent performance, with LightGBM achieving an accuracy level of 93%–99% and XGBoost reaching 92%–98%. The implementation of this application, designed with local (offline) data storage, has successfully guaranteed user privacy and shifted residents' habits from intuitive-based monitoring to validated medical data-based recording. The application has been proven to increase the independence and literacy of urban mothers in detecting the risk of malnutrition early, while also supporting the achievement of Sustainable Development Goals (SDGs) points 3 (healthy and prosperous lives) and 4 (quality education).

 

Keywords: mobile applications; machine learning; toddler nutrition monitoring; stunting; urban public health


Keywords


mobile applications; machine learning; toddler nutrition monitoring; stunting; urban public health

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References


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DOI: https://doi.org/10.31764/jpmb.v10i5.42024

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