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All Journal Jurnal Informatika dan Teknik Elektro Terapan JURIKOM (Jurnal Riset Komputer)
Cesilia, Yolinda
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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PENERAPAN METODE DEMPSTER SHAFER PADA SISTEM PAKAR UNTUK DIAGNOSIS STUNTING Nurdin, Nurdin; Cesilia, Yolinda; Agusniar, Cut
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.8074

Abstract

Stunting is one of the chronic nutritional problems that affects physical growth, cognitive development, and human productivity in the future. This condition is caused by prolonged nutritional deficiencies and health issues during the early stages of life. This study aims to develop an expert system for diagnosing stunting in toddlers using the Dempster Shafer method, which assists medical personnel in performing early detection based on symptoms and expert belief levels. The Dempster Shafer approach is applied due to its ability to handle uncertainty in data and combine multiple pieces of evidence to produce a rational diagnostic conclusion. The research data were obtained from the Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 30 test data samples. The system was developed using the Python programming language, Flask framework, and SQLite database. The testing results show that the system achieved an accuracy rate of 36.66%, with 11 out of 30 test data correctly classified according to expert diagnosis. Although the accuracy remains low, this study demonstrates the potential of the Dempster Shafer method as a foundation for evidence-based diagnostic systems in stunting detection.
Sistem Pakar Berbasis Web untuk Diagnosis Stunting pada Balita Menggunakan Metode Naïve Bayes Cesilia, Yolinda; Nurdin, Nurdin; Cut Agusniar
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9130

Abstract

Stunting is a health problem caused by chronic malnutrition that affects children's physical growth and cognitive development. This condition has become a serious concern because it impacts the quality of human resources in the future. This study aims to develop an expert system for diagnosing Stunting using the Naïve Bayes method to assist healthcare workers in the early detection of at-risk toddlers. The research data were obtained from Posyandu in Babul Makmur District, Southeast Aceh Regency, consisting of 170 training data and 30 testing data. The system was developed using the Python programming language with the Flask framework and SQLite database. The input variables consisted of seven symptoms (G01–G07), including age, weight, height, gender, and other supporting factors. The testing results showed that the Naïve Bayes method achieved an accuracy of 86.66%, with 26 out of 30 test data correctly classified according to expert diagnoses. This system can be used as a decision-support tool for healthcare workers to accelerate diagnosis and improve the effectiveness of Stunting management, particularly in areas with limited healthcare resources. 
Co-Authors Cut Agusniar Nurdin Nurdin Nurdin Nurdin