Ekspolarasi dan Karakterisasi Morfologi Jamur Makroskopis di Kawasan Hutan Adat Bukit Selebu, Kabupaten Merangin beserta Klasifikasi Potensinya Menggunakan K-Nearest Neighbors

Authors

  • Rozana Zuhri Program studi pendidikan biologi, Universitas Merangin
  • Deni Satria Program studi teknologi informasi, Universitas Merangin

DOI:

https://doi.org/10.25077/jbioua.11.2.84-94.2023

Keywords:

Karakterisasi morfologi, Fungi Makroskopis, K-Nearest Neighbors

Abstract

Fungi are one of Indonesia's potential natural resources which contain various benefits for human life. The existence of macroscopic fungi is not yet well known so that little information about the types and their benefits is known to local people. In fact, information about species of fungi is very important because fungi have economic value and are producers for the food and pharmaceutical sectors. Populations of macroscopic fungi can disappear due to climate change and environmental factors, therefore, it is necessary to collect data on the species of macroscopic fungi. One way to detect the species of fungus is to classify it based on the morphological characteristics of the fungus with K-Nearest Neighbor. The aim of this research is to explore and identify the morphological characteristics of fungi using K-Nearest Neighbor as a classifier found in the Bukit Selebu traditional forest area, Merangin Regency. It is hoped that the results of this research can be the first step in efforts to utilize fungi through further research. This research was carried out in the Bukit Selebu traditional forest area, Merangin Regency and identification continued at the Biology Laboratory of Merangin University. Then the data on the species of fungi found were analyzed using k-Nearest Neighbors (kNN). The results of this research are that there are 27 species of fungi found in the Bukit Selebu traditional forest area, Merangin Regency, consisting of 3 species of Ascomycota divisions and 24 species of Basidiomycota divisions. The K-Nearest Neighbor method is very good in classifying fungi through the extraction of morphological characteristics with the highest accuracy reaching 93%.

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Published

2023-11-24

Issue

Vol. 11 No. 2 (2023)

Section

Articles

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