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All Journal Knowledge Engineering and Data Science
Dadan Ridwan Saleh
Pusat Riset Informatika, Badan Riset dan Inovasi Nasional
Computer Science & IT Engineering

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Non-Gaussian Analysis of Herbarium Specimen Damage to Optimize Specimen Collection Management Aris Yaman; Yulia Aris Kartika; Ariani Indrawati; Zaenal Akbar; Lindung Parningotan Malik; Wita Wardani; Tutie Djarwaningsih; Taufik Mahendra; Dadan Ridwan Saleh
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p1-16

Abstract

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, location, storage, and remounting status on the level of damage to herbarium specimens, especially those in the genus Excoecaria. Gaussian modeling is not good enough to model the counted data phenomenon (the amount of damage to herbarium specimens). Negative binomial regression (NBR) provides a better model when compared to generalized Poisson regression and ordinary Gaussian regression approaches. NBR detects non-uniformity in the storage process, causing damage to herbarium specimens. Natural damage to herbarium specimens is caused by differences in species and the origin of specimens.
Co-Authors Ariani Indrawati Aris Yaman Lindung Parningotan Manik Taufik Mahendra Tutie Djarwaningsih Wita Wardani Yulia Aris Kartika Zaenal Akbar