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All Journal Journal of Fundamental Mathematics and Applications (JFMA) Nusantara Journal of Computers and its Applications
Hartiansyah, Fiqih Rahman
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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ALGEBRAIC STRUCTURES IN HEREDITY HUMAN BLOOD GROUP SYSTEM Wasil, Moh.; Hartiansyah, Fiqih Rahman; Alifia, Istianah
Journal of Fundamental Mathematics and Applications (JFMA) Vol 7, No 1 (2024)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jfma.v7i1.20552

Abstract

Marriage or in this case the researcher calls it "cross-operation" between two individuals (male and female) who have the same or different blood type has the probability to produce children (offspring) with the same blood type as one of the parents or even have a completely different blood type with both of them, whether it is the ABO blood type system or MN if it is associated with the rhesus system or not. The cross-operation between two individuals can be viewed from a mathematical perspective as an algebraic structure with one closed binary operation (OB). The cross-operation of ABO blood group system is an algebraic structure in groupoid form. The cross-operation of MN blood group system is an algebraic structure in groupoid form. And finally, the cross-operation of ABO and MN blood group systems when associated with the rhesus blood group system is an algebraic structure in groupoid form.
Use of Deep Learning and k-Nearest Neighbor Algorithms for Recognition of Fruit Types Sulthan, M Burhanis; Hartiansyah, Fiqih Rahman; Hafin, Aqid Fahri
NJCA (Nusantara Journal of Computers and Its Applications) Vol 10, No 1 (2025): Edisi Juni 2025
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v10i1.293

Abstract

fruit recognition was done in this research specifically for fruit image. The recognition of fruit in this study can be implemented to know the number of fruits that exist. Fruit image trained into several labels (fruit types) that are classified by data testing. There are several processes and methods undertaken in this research until the classification process, one of this i.e. Gaussian filter to improve the quality of fruit image recognition. Furthermore, the feature extraction process uses Gabor filter and for feature selection, PCA technic is respectively used to select some of the best features. The selected feature will be classified using deep learning and k-nearest neighbor (k-NN) method. Moreover, the results of the processes done carried out in achieving an accuracy of 95.01%.
Co-Authors Alifia, Istianah Hafin, Aqid Fahri Sulthan, M Burhanis Wasil, Moh.