Design and Development of an Ai-Based Animal Classification App for Early Childhood Education in Rural Indonesia

Putra Bintang, Ghifari Anhar (2025) Design and Development of an Ai-Based Animal Classification App for Early Childhood Education in Rural Indonesia. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

This paper presents the design and development of an Artificial Intelligence (AI)- based animal classification application aimed at enhancing early childhood education in rural Indonesia. Utilizing Convolutional Neural Networks (CNNs) with the DenseNet-121 architecture, the web-based app allows kindergarten students to identify animals through image classification, featuring bilingual audio outputs (Indonesian and English) and multimedia feedback, including animal sounds and videos. Targeting rural settings like Pendidikan Anak Usia Dini (PAUD) Alamanda 105 in Jember Regency, the app addresses the limitations of traditional learning media by providing an interactive tool optimized for low-spec devices. A pilot study at Pendidikan Anak Usia Dini (PAUD) Alamanda 105 and a survey of 56 respondents indicated strong support, with 73.2% believing it bridges resource gaps and 53.6% valuing its educational benefits. The CNN model achieved 98% accuracy in classifying 33 animal species after 20 training epochs. This study highlights the potential of AI-driven tools to improve learning outcomes in underresourced areas, offering a scalable solution for educational equity in rural education.

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