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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Informatika Mulawarman
Andi Muhammad Anwar
Universitas Hasanuddin
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.
Jumlah Langsung Subsemimodul Prima Andi Muhammad Anwar; Hanni Garminia; Irawati Irawati
Jurnal Matematika, Statistika dan Komputasi Vol. 18 No. 2 (2022): JANUARY 2022
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v18i2.16669

Abstract

Let be a commutative semiring. A semimodule over a semiring is a fully prime semimodule if each proper subsemimodule of is prime. This research aims to investigate the relationship between a direct sum of prime subsemimodules and , , and a fully prime semimodule.
Deteksi Citra X-Ray Paru-Paru Terinfeksi COVID-19 dengan Algoritma CNN berbasis Aplikasi Web Supri Bin Hj Amir; Sitti Nur Azizah Fitriani Akbar; Hendra Hendra; Andi Muhammad Anwar; Sulfayanti Sulfayanti
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 17, No 1 (2022): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v17i1.6534

Abstract

Pada penelitian ini menggunakan algoritma Convolutional Neural Network (CNN) untuk mendeteksi COVID-19 berdasarkan citra X-ray Paru-paru. Arsitektur CNN yang digunakan adalah EfficientNetB7 dan Resnet152V2 dengan memanfaatkan teknik Transfer Learning. Penelitian ini berfokus pada membandingkan kinerja kedua model arsitektur dalam mengklasifikasikan citra X-ray Paru-paru terinfeksi COVID-19. Selanjutnya mengimplementasikan model CNN tersebut ke aplikasi deteksi Citra X-ray paru-paru berbasis web. Dari hasil evaluasi kedua model tersebut disimpulkan bahwa Resnet152-V2 mencapai kinerja lebih baik dibanding arsitektur CNN EfficientNetB7 dengan akurasi 97% sedangkan EfficientNetB7 dengan akurasi 95%.
Co-Authors A. Muh. Amil Siddik Ainun Mawaddah Abdal Hanni Garminia Hendra Hendra Irawati Irawati Sitti Nur Azizah Fitriani Akbar Sulfayanti Supri Bin Hj Amir