MONITORING THE GROWTH OF BIOLOGICAL AGENT FUNGI IN ORGANIC MATERIAL MIXTURES WITH VARIED COMPOSITIONS USING AN ELECTRONIC NOSE: MONITORING PERTUMBUHAN JAMUR AGENSIA HAYATI DALAM CAMPURAN BAHAN ORGANIK DENGAN VARIASI KOMPOSISI MENGGUNAKAN ELECTRONIC NOSE - Indriani Lutfiyyatunnisa; Bambang Heru Iswanto; Agustin Sri Mulyatni (Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI, 2025)
The use of a mixture of biological agents and organic matter waste can be used as an alternative to chemical fertilizers as an environmentally friendly bioinsecticide product. However, the presence of biological agent organisms in organic materials and susceptible to contamination in certain compositions is difficult to detect early. In this study, an electronic nose (e-nose) is used to detect the presence of three growing fungal mycelium and contamination in a mixture of organic materials based on their aroma patterns. As a first step, a selection of features will be made that will be used to build a detection model. Features are extracted from e-nose response data taken from 28 samples of a mixture of bio-agent fungi with organic materials consisting of 9 variations in composition. E-nose used version 3 with a total of 16 Taguci type sensors. Experiments were conducted with three samples of biological fungi each mixed with bagasse organic matter with variations in the composition of dilutions 10(−1), 10(−2), and 10(−3)as well as dilution volumes of 2 ml, 4 ml, and 6 ml as well as control samples containing bagasse organic matter alone. There were a total of 28 samples with three repetitions with different sampling times. For principal component analysis, data processing begins with data pre-processing by performing baseline correction and normalization. Furthermore, data analysis is carried out using Principal Component Analysis (PCA) with descriptive statistical features of the minimum value. From the results of the analysis using PCA, the first two main components explained about 67.35% and the second main component explained about 18.56% of the data variation.