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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Matematika UNAND
A. Muh. Amil Siddik
Hasanuddin University
Computer Science & IT Mathematics

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Prime Ideals in Matrices over Γ-Semihyperrings Andi Muhammad Anwar; Andi Muhammad Amil Siddik; Ainun Mawaddah Abdal
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 1 (2020): JMSK, SEPTEMBER, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i1.11066

Abstract

The semihyperring structure is a common form of the hyperring structure with weakening properties. The more general structure is Γ-semihyperring, whose concept is generalized from Γ-semiring. This paper will show that the top matrix Γ-semihyperring is also Γ-semihyperring. The linkage between prime ideal of Γ-semihyperring with prime ideal of a matrix on Γ-semihyperring will also be discussed in this paper.
Kestabilan Model Mangsa Pemangsa dengan fungsi respon Holling tipe IV dan penyakit pada pemangsa A. Muh. Amil Siddik; Syamsuddin Toaha; Andi Muhammad Anwar
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11716

Abstract

Stability of equilibrium points of the prey-predator model with diseases that spreads in predators where the predation function follows the simplified Holling type IV functional response are investigated. To find out the local stability of the equilibrium point of the model, the system is then linearized around the equilibrium point using the Jacobian matrix method, and stability of the equilibrium point is determined via the eigenvalues method. There exists three non-negative equilibrium points, except , that may exist and stable. Simulation results show that with the variation of several parameter values infection rate of disease , the diseases in the system may become endemic, or may become free from endemic.  
Comparison of Transfer Learning Algorithm Performance in Hand Sign Language Digits Image Classification A. Muh. Amil Siddik
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.26503

Abstract

Image hand sign classification has become an interesting topic in image processing and machine learning. However, to achieve optimal performance in hand sign image classification tasks, a large and diverse dataset as well as powerful learning algorithms are required. One popular technique for improving the performance of classification models is transfer learning, which allows the use of knowledge learned from previous models and applies it to new tasks. In this study, the performance of two different transfer learning algorithms, ResNet-50 and VGG-16, was compared on the Sign Language Digits Dataset, which consists of 10 different types of handwriting images. The results of the experiment showed that both tested transfer learning algorithms had good performance. However, VGG-16 provided the best results with an accuracy of 97,29%, precision of 97,38%, recall of 97,45%, and an F1 score of 97,36%, while ResNet-50 achieved an accuracy of 94,57%, precision of 94,75%, recall of 94,96%, and an F1 score of 94,78%. In conclusion, transfer learning algorithms are effective techniques for improving the performance of hand sign image classification models. Choosing the appropriate transfer learning algorithm and dataset can help generate more accurate classification models.
MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER ADHIYAKSA PRANANDA; Siswanto Siswanto; Sri Astuti Thamrin; A. Muh. Amil Siddik
Jurnal Matematika UNAND Vol 13, No 2 (2024)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.13.2.131-141.2024

Abstract

In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus. Indonesia is one of the countries that is aggressively implementing the COVID-19 vaccination. The vaccination process which has been carried out from February 2021 until the end of 2021 has covered approximately 160 million people or 76.83% of the target set by the government. Vaccine recipients have criteria to be able to get vaccinated to avoid side effects or complications. So it is necessary to classify groups that can receive vaccines and also delay vaccination. This research aims to determine the performance of the learning vector quantization classification method. Learning vector quantization method classification produces 95% accuracy, 97% precision, and 96% sensitivity. From these performance measurements, it can be concluded that the learning vector quantization method is very good and can be used in the classification of COVID-19 vaccination recipients at the Parumpanai Public Health Center, East Luwu Regency.
Co-Authors ADHIYAKSA PRANANDA Ainun Mawaddah Abdal Andi Muhammad Anwar Andi Muhammad Anwar Kasbawati Kasbawati Oswaldus Dadi Rian Ade Pratama Siswanto Siswanto Sri Astuti Thamrin Syamsuddin Toaha