Identifikasi Mikro Protein Menggunakan Fuzzy Logic untuk Mendeteksi Kecenderungan Risiko Stunting melalui Telegram Bot

JEKE, Mathias Leonardo (2025) Identifikasi Mikro Protein Menggunakan Fuzzy Logic untuk Mendeteksi Kecenderungan Risiko Stunting melalui Telegram Bot. Undergraduate thesis, Universitas Katolik Widya Mandira.

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Abstract

Stunting is a global health problem that affects children's growth and development, with serious long-term impacts if not handled properly. This study aims to develop a fuzzy logic-based protein identification model to accurately detect stunting risk tendencies and build a Telegram bot application that facilitates access and monitoring of protein data. Fuzzy logic is applied to overcome the complexity and uncertainty in nutritional data, especially protein, which plays an important role in early detection of stunting risk. This system allows real-time input of nutritional data and classification results through a Telegram bot, which is optimized for use even in remote areas with limited access to laboratory facilities. This study integrates machine learning techniques in the health sector to improve the accuracy of nutritional diagnosis and monitoring. The main contributions of this study include early detection of stunting risk tendencies, increasing diagnostic accuracy using fuzzy logic, and more effective nutritional monitoring through the Telegram bot. With this system, health workers can access information quickly and accurately and provide appropriate nutritional recommendations, even in locations with limited facilities. The results of this study are expected to support stunting prevention and increase public awareness of the importance of nutritional intake for children.

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