Pengembangan Sistem Pakar Deteksi Dini Stunting Menggunakan Naive Bayes Di Rumah Sakit Assalam Cibinong
DOI:
https://doi.org/10.31599/5jf67f89Kata Kunci:
Anthropometric, Expert system, Naive bayes, Rule based system, StuntingAbstrak
Stunting is a chronic nutritional problem that has become a national priority in Indonesia, yet early detection at Assalam Hospital Cibinong is still performed manually, which slows the assessment process for healthcare workers and leaves parents without an independent means of monitoring their child’s growth. This study aims to develop a web-based expert system that accelerates stunting early detection in children aged 0–60 months for healthcare workers while simultaneously providing parents with independent monitoring access. The system was developed using the Prototype model with PHP and adopts a hybrid approach: the Naive Bayes algorithm for the Length/Height-for-Age (L/H-A) index and a Rule Based System for the Weight-for-Age (W-A) and Weight-for-Length/Height (W-L/H) indices, in accordance with Indonesian Ministry of Health Regulation Number 2 of 2020, using a validated dataset of 378 anthropometric medical records. Testing was conducted through Black Box Testing, 10-Fold Cross Validation, and User Acceptance Testing. The results show Naive Bayes accuracy of 76.72% (76.18% on 10-Fold CV), 100% Rule Based System consistency, and a UAT feasibility score of 96.5% (Highly Feasible). The system is proven to shorten the nutritional status assessment process for healthcare workers while providing parents with independent screening access. The novelty of this research lies in the simultaneous integration of three anthropometric indices (L/H-A, W-A, W-L/H) into a single hybrid web-based expert system validated with actual hospital medical records, distinguishing it from prior studies focusing on single-index classification or algorithm comparison without clinical implementation.
Unduhan
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