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All Journal Jurnal Sarjana Teknik Informatika Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA SKANIKA: Sistem Komputer dan Teknik Informatika Science in Information Technology Letters Jurnal Pendidikan dan Teknologi Indonesia Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal of Artificial Intelligence and Digital Business
Rosyda, Miftahurrahma
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Comparison of Machine Learning Algorithms for Species Family Classification using DNA Barcode Riza, Lala Septem; Rahman, M Ammar Fadhlur; Prasetyo, Yudi; Zain, Muhammad Iqbal; Siregar, Herbert; Hidayat, Topik; Abu Samah, Khyrina Airin Fariza; Rosyda, Miftahurrahma
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate families, with an accuracy rate exceeding 97%, except for the Naïve Bayes model. Regarding computational time, the Random Forest model requires significantly more time for training than other models. Regarding memory usage, the Least Squares Support Vector Machine with a polynomial kernel, and Regularized Logistic Regression consume more memory than other models. These machine learning models exhibit strong concordance with NCBI's classifications when predicting families using the test dataset, effectively categorizing species into the Amaryllidaceae and Liliaceae families.
Co-Authors Abu Samah, Khyrina Airin Fariza Amallia, Alfina Habibah, Umu Hairani Hairani Lala Septem Riza Nurfahrani, Nurfahrani Rahma, Ghaitsa Zahira Rahman, M Ammar Fadhlur Samah, Khyrina Airin Fariza Abu Siregar, Herbert Suparno, Daffa Setiawan Topik Hidayat Wardani, Ade Yudadiningrat, Rizki Nur Rachmadi Yudi Prasetyo Zain, Muhammad Iqbal