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Each organism has a different role as an integral part of an ecosystem. Nevertheless, this concept tends to be random and inaccurate in building a complete understanding of ecosystem interaction pattern. This research aims to develop learning video on ecosytem interaction, specifically on functional feeding group of macrobenthos. The feasibility of video as a learning medium was seen from five aspects of simplicity, audio, integration, balance, and effectiveness. The development of learning video is divided into two stages of the preliminary and the formative evaluation stage. In the preliminary stage, a field survey was conducted in the examples of interaction forms presented in 3 commonly used Biology textbooks. The formative evaluation stage was conducted to test the effectiveness of the learning product through self-evaluation and expert review. 5 validators of 2 lecturers and 3 high school biology teachers were assign. Data analysis was done with Lawshe method. An ICC (Interclass Correlation Coefficient) reliability test was carried out to determine the reliability of the validation instrument. The results of the reliability test using the ICC yielded an ICC value of 0.758 which puts it in the excellent category. All criteria from the aspects of the learning video media validation sheet are declared valid with CVR and CVI values ​​of 1. Therefore, this learning video can be considered valid to be tested in limited trials.

This study analyzes macrozoobenthos diversity and functional feeding group in Berembang River, Kubu Raya Regency, West Kalimantan. The obtained macrozoobenthos were identified to the genus level, and their functional feeding group was determined through a literature study. Data on diversity, evenness, and dominance were analyzed descriptively. Diversity (H') and Evenness (E) of macrozoobenthos in Berembang River were low (H'=0.73 and E=0.15) while Dominance (C) was high (C=0.81) as evidenced by a large number of genera Corbicula and Gammarus. Macrozoobenthos functional feeding groups found at both stations can be divided into six categories, namely shredders, gathering collectors, deposit feeders, sub-surface deposit feeders, filtering collectors, and scrapers.