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Fahmi Zain
Universitas Esa Unggul

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Prediksi Risiko Stunting Prakehamilan Menggunakan Sistem Pakar Certainty Factor Berbasis Mobile Muhamad Hadi Arfian; Noviandi Noviandi; Fahmi Zain
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.1191

Abstract

Stunting still a serious public health issue, and the risk often originates as early as the pre-prenant phase. However, digital solutions that specifically integrate individual risk screening, expert-based reasoning, and mobile access for expectant mothers are still limited, which hinders the ability to effectively address the stunting risk during the critical pre-prenant phase. This study aims to develop a Certainty Factor-based expert system mobile application capable of independently, quickly, and educationally predicting the risk of stunting during the pre-prenant phase. This study employs a design and development research approach encompassing needs identification, knowledge acquisition, the formulation of a knowledge base and inference rules, system design, and mobile application implementation. The knowledge base was designed for three user categories: women in the pre-prenant stage, women who have given birth, and 19-year-old adolescents. Evaluation results showed that system performance varied across groups, with an accuracy of 0.67 for the adolescent and postpartum groups and 0.52 for the pre-prenant group. The precision and recall values also show a similar pattern, indicating that the system performs better in groups with more structured risk characteristics than in groups with more complex risk determinants. These findings suggest that the integration of a CF-based expert system with a mobile platform is not only capable of supporting the early detection of stunting risk but also provides an adaptive approach to differences in user characteristics, so that it has potential to increase the effectiveness of life-cycle-based screening.
Co-Authors Muhammad Hadi Arfian Noviandi Noviandi