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All Journal Jurnal Teknik Informatika (JUTIF) E-Amal: Jurnal Pengabdian Kepada Masyarakat
Sari, Nimas Ratna
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions

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Optimalisasi Komunikasi Antar Pribadi Pada Kader Posyandu Melalui Pemanfaatan Media Konseling Berbasis Artificial Intelegence Sebagai Upaya Pencegahan Stunting. Sulistyorini, Etik; Sari, Nimas Ratna; Palupi, Fitria Hayu; Lefiyana, Lefiyana; A, Maulana Ilham
E-Amal: Jurnal Pengabdian Kepada Masyarakat Vol 4 No 3: September-Desember 2024
Publisher : LP2M STP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47492/eamal.v4i3.3609

Abstract

Penggunaan media konseling dalam Komunikasi Antar Pribadi (KAP) merupakan cara yang efektif untuk perubahan perilaku dan sosial masyarakat khususnya perilaku dalam pencegahan stunting. Saat ini, masih banyak Kader Posyandu yang belum bisa memanfaatkan media konseling berbasis teknologi secara optimal dalam berkomunikasi dengan masyarakat. Tujuan kegiatan ini: 1) meningkatkan ketrampilan kader posyandu dalam implementasi KAP melalui penggunaan media konseling (leaflet, poster, lembar balik); 2) pembuatan media konseling berbasis Artificial Intelegence (berupa aplikasi chatbot) sebagai sarana komunikasi yang efektif dan fast respon. Metode: Pelatihan, pendampingan implementasi KAP, dan pembuatan aplikasi “stunting care”. Hasil : 76 kader posyandu mengikuti pelatihan penggunaan media konseling berbasis teknologi, dan didampingi dalam implementasi KAP dengan hasil ketrampilan kader dalam implementasi KAP 84% baik, 16 % cukup, dan tidak ada yang kurang. Tersedia media komunikasi berupa aplikasi chatbot “stunting care” yang dapat dimanfaatkan oleh kader posyandu dan masyarakat untuk melakukan komunikasi yang efektif dan fast respon.
Explainable Artificial Intelligence Using SHAP and Multilayer Perceptron for Transparent Stunting Risk Prediction in Sukoharjo, Indonesia Sari, Nimas Ratna; Renowening, Yuniars; Ma’arif, Muhammad Zainul
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5481

Abstract

Childhood stunting remains a critical public health challenge in Indonesia, with national prevalence at 19.8% in 2024 per SSGI data, hindering human capital development toward Indonesia Emas 2045. This study addresses the opacity of AI models in stunting prediction by integrating machine learning with Explainable AI (XAI) to enhance transparency for non-technical stakeholders. Using a survey dataset of 273 children from Sukoharjo Regency, risk factors encompassing key stunting determinants consist of maternal characteristics, household socioeconomic conditions, sanitation practices, and sociodemographic, were preprocessed via cleaning, label encoding, min-max scaling, and train-test split. Three classifiers; Logistic Regression (LR), Naïve Bayes (NB), and a Multilayer Perceptron (MLP)  with ReLU/softmax were trained and evaluated on accuracy, precision, recall, and F1–score. MLP with 16 hidden nodes, achieved the highest performance: 82% accuracy, 87% precision, 82% recall, and 82% F1-score, outperforming baselines. Kernel SHAP was applied to decompose predictions, revealing mother's education, age, number of children, birth length, household size, and income as top influencers. This XAI enhanced framework promotes trust and actionability in public health interventions, advancing informatics by bridging high accuracy neural networks models with interpretable insights for targeted stunting reduction in resource–limited settings.
Co-Authors A, Maulana Ilham Etik Sulistyorini, Etik Fitria Hayu Palupi Lefiyana, Lefiyana Ma’arif, Muhammad Zainul Renowening, Yuniars