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All Journal Media Statistika MATEMATIKA Statistika Kreano, Jurnal Matematika Kreatif-Inovatif Jurnal TIMES BAREKENG: Jurnal Ilmu Matematika dan Terapan Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Bulletin of Applied Mathematics and Mathematics Education JNPM (Jurnal Nasional Pendidikan Matematika) STATISTIKA Emerging Statistics and Data Science Journal
Rohmatul Fajriyah
Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Transportation Veterinary

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Journal : Bulletin of Applied Mathematics and Mathematics Education

 1 
Identifying malaria disease through red-blood microscopic image with XGBoost and random forest methods Fajriyah, Rohmatul; Muhajir, Muhammad; Abdullah, Ahmad Hussain; Ayu, Devina Gilar; Rahman, Iqbal Fathur
Bulletin of Applied Mathematics and Mathematics Education Vol. 4 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v4i2.11740

Abstract

Blood cells that flow in the human body provide information to diagnose a disease. The information provided can be obtained through images of these blood cells using image processing techniques. Malaria is a very deadly disease and can affect everyone. Patients with malaria will experience anaemia because the red blood cells or erythrocytes are contaminated with plasmodium. This study offers an alternative solution to malaria disease identification through the image classification of red blood cells, by applying image processing and image classification methods with XGBoost and random forest. The research was conducted using the R programming language in RStudio and Python. The accuracy of XGBoost and random forest methods were 71.26% and 77.58%, respectively. Therefore, the random forest provided a better optimal classification model with higher accuracy. The model is used to build an application which is R web-based, RShiny. In practice, this application can be used by health workers in classifying patients based on red blood cell images such that the health centre would be easier to manage the existing patients.
Paper review: An overview on microarray technologies Fajriyah, Rohmatul
Bulletin of Applied Mathematics and Mathematics Education Vol. 1 No. 1 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (445.218 KB) | DOI: 10.12928/bamme.v1i1.3854

Abstract

Bioinformatics is a branch in Statistics which is still unpopular among statistics students in Indonesia. Bioinformatics research used microarray technology, because data is available through to microarray experiment on tissue sample at hand. Microarray technology has been widely used to provide data for bioinformatics research, since it was first introduced in late 1990, particularly in life sciences and biotechnology research. The emergence and development of the Covid-19 disease further reinforces the need to understand bioinformatics and its technology. There are two of the most advance platforms in microarray technology, namely, are the Affymetrix GeneChip and Illumina BeadArray.  This paper aims to give an overview about microarray technology on the two platforms and the advantage of using them on bioinformatics research.
Pipeline on microarray data analysis: Pre-processing Fajriyah, Rohmatul; Kongchouy, Noodchanath; Ayudhaya, Wanvisa Saisanan Na; Yotenka, Rahmadi; Danarwindu, Ghiffari Ahnaf
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i1.12539

Abstract

Bioinformatics is blooming and its data are store in some repository offline and or online. Yet some basic concepts are not fully disseminated. The paper intends to provide the reader with a review of one important concept in the pipeline bioinformatics data analysis of microarray, pre-processing. In pre-processing, there are four steps, background correction, normalization, probe correction and summarization. Each step consists of several methods, and we describe each method to give a better understanding on how it works theoretically. We focused on microarray data from Affymetrix platform with single-color chip.
Co-Authors Abdullah, Ahmad Hussain ACHMAD FAUZAN Adhar Arifuddin Aditya Ferdiana Arief Akbar, Wahyu Sa'dun Ayu, Devina Gilar Ayudhaya, Wanvisa Saisanan Na Azhari, M Fauzan Bimakasa, M Albarra Danarwindu, Ghiffari Ahnaf Dini, Sekti Kartika Gunawan, Asmawi Isnandar, Havidzah Asri Izzati Muhimmah Kongchouy, Noodchanath Lalu Mutawalli Lantip Rujito Moh Reza Syaifur Rizal Muhammad Muhajir, Muhammad Nanda Khofifah, Thalia Novendri Isra Asriny Pradhisa, Kireyna Cindy Punik Mumpuni Wijayanti Rahman, Iqbal Fathur Riefyal Arshyzha Mustain Sekti Kartika Dini Sheilta Alphenia Subanar . Untari, Siti Nirmala Wayan Tunas Artama Yotenka, Rahmadi