Aug 2026· Jurnal Pendidikan Indonesia Gemilang· Vol 6, pp. 304-318· 0 citations· 31 references
TL;DR
An ethnobotanical survey of local food plants found in Banten Province, Indonesia is carried out and a pedagogical reconstruction concept model that incorporates TEK into biology lessons at secondary and tertiary levels is developed.
Abstract
The botanical diversity found in Indonesia is contrasted by the country’s dependency on imported foodstuffs. This paper aims to conduct an ethnobotanical survey of local food plants found in Banten Province, Indonesia, and develop a pedagogical reconstruction concept model that incorporates TEK into biology lessons at secondary and tertiary levels. An ethnobotanical survey of local food plants was carried out in March to May 2026 in the coastal, lowland agroforestry, and highland ecosystems of Pandeglang, Lebak, Serang, and Cilegon regions. Data were gathered using purposeful sampling through field observations, specimen collection, semi-structured interviews involving 45 local informants (elderlies, farmers, and indigenous community leaders), and secondary literature validation. Species recorded were identified taxonomically using Plants of the World Online (POWO) database and categorized in core biological subjects within TPACK framework. The ethnobotanical survey revealed 32 species of local food plants belonging to 7 functional groups. Some of the important findings include the underexploitation of valuable functional species like Xanthosoma undipes (Taro Beneng; starch 80.6%, GI 48) and Sonneratia caseolaris (Pedada), as well as species requiring conservation efforts, like Garcinia dulcis (Mundu) and Baccaurea racemosa (Jatake). The cyanogenic detoxification of Dioscorea hispida and the Leuit post-harvesting storage practice of indigenous people were conceptually linked to biochemistry, plant physiology, ecology, and taxonomy lessons. In order to translate traditional knowledge into a form compatible with contemporary digital learning, the framework considers the use of future AI-supported smart identification through the use of the Pl@ntNet API.
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