Analisis Efisiensi Metode K-Nearest Neighbor dan Forward Chaining Untuk Prediksi Stunting Pada Balita

Authors

DOI:

https://doi.org/10.30872/jim.v18i2.10169

Keywords:

Stunting, sistem pakar, forward chaining, k-nearest neighbor

Abstract

Permasalahan gizi buruk yang terjadi di Indonesia menjadi sebuah tantangan yang nyata bagi pemerintah maupun masyarakat. Salah satu gizi buruk kronis yang menyerang balita di Indonesia yaitu stunting (tubuh pendek). Masalah stunting sering dialami oleh anak-anak di Indonesia. Dalam satu dekade terakhir, balita yang terindikasi stunting cenderung tidak mengalami perbaikan. Jika terus dibiarkan stunting akan mengakibatkan masalah serius yang dapat mengganggu pertumbuhan dari anak. Oleh karena itu, diperlukan pencegahan sejak dini terkait masalah stunting pada anak. Pada penelitian kali ini akan dibuat sebuah website untuk melakukan deteksi stuntung pada balita. Penelitian dilakukan dengan membandingkan metode sistem pakar forward chaining yang menggunakan pengkondisian manual pada bahasa pemrograman python dan machine learning menggunakan K-Nearest Neighbor. Hasil dari sistem pakar forward chaining memiliki akurasi prediksi yang lebih baik karena dapat memprediksi dengan benar 10/10 pengujijan yang diberikan, sedangkan dengan menggunakan machine learning K-Nearest Neighbour hanya dapat memprediksi benar 8/10 pengujian yang telah diberikan. Kemudian metode yang terpilih akan dilakukan deploying kedalam website. Pada tahap deploying, metode yang digunakan adalah forward chaining.

References

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Published

11-08-2023

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Vol. 18 No. 2 (2023): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer

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