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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Alpha Epsilon Journal of Mathematics
Iis Setiana
Universitas Annuqayah
Mathematics

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Komparasi Metode Extreme Learning Machine (ELM) dan Multi-Support Vector Machine (Multi-SVM) pada Identifikasi Tanaman Herbal Luluk Sarifah; Lailiyatus Sa’adah; Iis Setiana
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37107

Abstract

In Indonesia, there are more than 2.039 species of herbal medicinal plants, which sometimes have similarities and make it difficult to identify the type of herbal plant. The purpose of this study is to facilitate the identification of herbal plant species by comparing the performance of the Extreme Learning Machine (ELM) and Multi-Support Vector Machine (Multi-SVM) methods. The ELM method was created to overcome the weaknesses of feedforward artificial neural networks, especially in terms of learning speed, while the Multi-SVM method is an advanced development of the SVM method. The stages of this research begin with image input which is through previous data acquisition, data preprocessing, and then the identification with ELM and Multi-SVM methods. Based on the simulations that have been carried out, the average accuracy on training data for the ELM method is 93%, while the Multi-SVM method is 44%. Also, the average accuracy on testing data for the ELM method is 85%, while the Multi-SVM method is 40%.
Prediksi Jumlah Stunting Kabupaten Pamekasan Menggunakan Metode Statistical Parabolic Iis Setiana; Luluk Sarifah
Alpha-Epsilon: Journal of Mathematics Vol 2 No 1 (2026): January
Publisher : Department of Mathematics, Faculty of Mathematics and Scince, Universitas Annuqayah

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Abstract

Indonesia is a country that is included in the target of high stunting management in the world, so stunting remains a problem that needs to be addressed. For example, the number of stunting in Pamekasan Regency. Currently, Pamekasan Regency is included in the target of stunting management with a stunting prevalence of 25.1% covering 21 health centers from 13 sub-districts. The purpose of this study is to predict the number of stunting in Pamekasan Regency in 2018-2024 using the statistical parabolic method. Statistical parabolic is one method that is able to make predictions based on past data, then in this study used data on the number of stunting in 2018-2024 obtained from the Pamekasan Regency Health Office. After calculating the predicted number of stunting in 2018-2024 based on the MAPE value obtained the result of 5.45%. Therefore, it can be concluded that the statistical parabolic method is good to be used to predict the number of stunting in 2025-2026.
Co-Authors Lailiyatus Sa’adah Luluk Sarifah