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All Journal Media Statistika MATEMATIKA Statistika Kreano, Jurnal Matematika Kreatif-Inovatif Jurnal TIMES BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya 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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GENE MARKERS IDENTIFICATION OF ACUTE MYOCARDIAL INFARCTION DISEASE BASED ON GENOMIC PROFILING THROUGH EXTREME GRADIENT BOOSTING (XGBoost) Fajriyah, Rohmatul; Isnandar, Havidzah Asri; Arifuddin, Adhar
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.69-80

Abstract

One disease that can cause death is Acute Myocardial Infarction (AMI). AMI, also known as a heart attack, is a condition that causes permanent damage to heart muscle tissue due to prolonged ischemia or lack of blood flow that occurs due to blockage of the epicardial coronary arteries and results in blood clots and limiting blood supply to the myocardium. During the years the young AMI patients are increasing. One of the ways to diagnose early is providing information of biomarkers related to this disease by implementing the bioinformatics data analysis. The research was conducted to look at the genomic profile of patients suffering from AMI based on without recurrent events and normal control, using the XGBoost method, due to its scalability and efficiency.  Based on the grid search of tuning hyperparameters, the XGBoost method gives a classification accuracy of 88.89%, AUC 90 and kappa 0.7805. These results indicate that the XGBoost method can classify patients suffering from AMI well. This research has identified three genes that contribute the most to classifying AMI patients, namely calponin 2, ribosomal protein S11 and myotropin. Based on the heatmap visualization, information was obtained that the three genes are class markers without recurrent events.
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.
Perbandingan Dampak Bencana Angin Kencang Tahun 2020 Dan 2021 Daerah Istimewa Yogyakarta Berdasarkan Metode K-means Clustering: Perbandingan Dampak Bencana Angin Kencang Tahun 2020 Dan 2021 Daerah Istimewa Yogyakarta Berdasarkan Metode K-means Clustering Nanda Khofifah, Thalia; Fajriyah, Rohmatul
Emerging Statistics and Data Science Journal Vol. 2 No. 1 (2024): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol2.iss.1.art11

Abstract

Penelitian ini bertujuan untuk mengkalsifikasikan kecamatan-kecamatan yang terdapat di Daerah Istimewa Yogyakarta berdasarkan data jumlah kejadian angin kencang beserta dampaknya yang meliputi pohon tumbang, akses jalan, dan jaringan listrik sehingga bisa terlihat mana saja daerah di Yogakarta yang rawan bencana angin kencang dan mana daerah yang tiddak rawan terhadap bencana angin kencang. Metode yang digunakan yaitu K-means Clustering dengan menggunakan matriks yang relevan dan Silhouette score. Pada tahun 2020 cluster pertama terdapat 3 kecamatan dengan indikator rawan angin kencang tinggi pada setiap kecamatan tersebut, cluster dua terdapat 19 kecamatan dengan indikator rawan angin kencang sedang pada setiap kecamatan, dan cluster ketiga terdapat 56 kecamatan dengan indikator rawan bencana angin kencang rendah. Sedangakan pada tahun 2021 cluster pertama terdapat 3 kecamatan dengan indikator rawan angin kencang tinggi pada setiap kecamatan tersebut, cluster dua terdapat 10 kecamatan dengan indikator rawan angin kencang sedang pada setiap kecamatan, dan cluster ketiga terdapat 65 kecamatan dengan indikator rawan bencana angin kencang rendah.
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.
ANALYSIS OF PORTFOLIO FORMATION ON THE LQ45 STOCKS INDEX, USING THE MARKOWITZ AND SINGLE INDEX MODELS Gunawan, Asmawi; Fajriyah, Rohmatul; Bimakasa, M Albarra; Untari, Siti Nirmala
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2363-2374

Abstract

In an investment, there will always be a return and risk, especially in the capital market in the form of stocks. The risk in an investment can be minimized by diversifying assets into several stocks to form a portfolio formation. Several models, such as the Single Index and Markowitz, can evaluate optimal portfolio formation. In this study we provide additional information and discourse on capital market studies and as the input for investors in making investment decisions in the form of stocks. The study shows that based on 25 companies, the Markowitz model gives 12 companies as the optimal portfolio with the largest proportion of funds owned by PT Bank Central Asia Tbk (BBCA), 82.22%. The portfolio of those 12 stocks can provide an expected return of 44.8% where its risk is about 13.77%. The Single Index model provides a formation based on 9 companies as the optimal portfolio with the largest proportion of funds owned by -again- PT Bank Central Asia Tbk (BBCA) which is 66.23%. The portfolio of these nine stocks can provide the expected return of 1.68% and its risk is 0.43%. The ratio of risk and return from each model justifies that the Single Index model gives better portfolio formation. This result should be further compared with other stock indexes, nationally and globally, and also needs to be compared with the period after the pandemic.
STUDI GENDER DALAM PEMBELAJARAN MATEMATIKA DI SEKOLAH MENENGAH: APAKAH ADA KETIMPANGAN? Marhaeni, Nafida Hetty; Nuryadi, Nuryadi; Fajriyah, Rohmatul; Suseno, Bayu; Khuzaini, Nanang
Jurnal Silogisme : Kajian Ilmu Matematika dan Pembelajarannya Vol 10 No 1 (2025): Juni
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan mengkaji hubungan antara gender dan berbagai aspek pembelajaran matematika, mencakup keikutsertaan dalam les tambahan, keterlibatan diskusi, kegemaran terhadap matematika, pemahaman materi, math-anxiety, self-efficacy, kebiasaan belajar, dan minat terhadap matematika; di tingkat sekolah menengah di Kota Yogyakarta. Penelitian dilakukan dengan metode survei dan pendekatan deskriptif-analitik. Populasi penelitian adalah siswa SMP kelas VII negeri dan swasta, dengan sampel 1.328 siswa yang dipilih menggunakan stratified random sampling untuk menjamin keterwakilan sekolah. Data dikumpulkan melalui kuesioner yang telah diuji validitas dan reliabilitasnya. Hasil analisis Pearson’s chi-squared menunjukkan bahwa gender tidak berhubungan signifikan dengan keikutsertaan dalam les tambahan, diskusi, kegemaran terhadap matematika, pemahaman materi, math-anxiety, maupun self-efficacy. Sebaliknya, gender memiliki hubungan signifikan dengan kebiasaan bertanya, preferensi terhadap penyajian materi berbasis aplikasi, kebiasaan menuliskan kesimpulan dan informasi pada penyelesaian soal, serta minat terhadap matematika. Secara umum, siswa perempuan lebih aktif bertanya dan lebih terorganisir, sedangkan siswa laki-laki lebih menyukai penyajian berbasis aplikasi serta menunjukkan minat yang lebih tinggi terhadap matematika.Temuan ini menegaskan pentingnya penerapan pendekatan pembelajaran inklusif yang mempertimbangkan perbedaan gender guna meningkatkan keterlibatan dan efektivitas pembelajaran matematika di tingkat sekolah menengah.
Classification of Tumor and Normal Tissue Gene Expression in Lung Adenocarcinoma Using Support Vector Machine and Gaussian Process Classification Yotenka, Rahmadi; Effendie, Adhitya Ronnie; Fajriyah, Rohmatul
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11763

Abstract

Lung adenocarcinoma (LUAD) is a major cause of cancer-related mortality worldwide. This study aims to identify potential LUAD biomarkers and develop robust classification models using the GSE151101 microarray dataset. Preprocessing included RMA normalization, ComBat batch-effect correction, and feature filtering based on annotation completeness, variability, and statistical significance. Support Vector Machine (SVM) and Gaussian Process Classification (GPC) models were constructed, with the polynomial GPC model achieving the best performance (accuracy 97.92%; F1-score 97.96%). Repeated 10-fold cross-validation confirmed its stability (mean accuracy 96.88%, SD ±1.97%, CV 2.03%), outperforming linear SVM, GPC-RBF, and Multiple Kernel Learning (MKL). Functional enrichment analysis showed that key discriminative genes; CDH13, CDKN2A, BCL2L11, MYL9, COL1A1, and AKT3; were enriched in pathways related to epithelial–mesenchymal transition, extracellular matrix remodelling, focal adhesion, PI3K/AKT signalling, and cell-cycle regulation, all of which are central to LUAD progression. In general, polynomial-kernel GPC is a stable and useful way to classify transcriptomes and rank biomarkers. Nevertheless, the translational potential of these signatures requires further validation in independent and clinically controlled cohorts.
COMPANY VALUATION AND PORTFOLIO ANALYSIS BASED ON K-MEANS CLUSTERING IN KOMPAS 100 STOCKS INDEX Fajriyah, Rohmatul; Tjen, Yoel Christopher
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0381-0396

Abstract

The capital market plays a vital role in investment, providing a platform for trading long-term financial instruments. Indonesia’s capital market has shown significant growth in recent years. This study aims not only to find stock clusters but to show that grouping stocks based on similar valuation characteristics can serve as a solid foundation for constructing superior-performing portfolios. The Kompas 100 index is used because it represents the most liquid and fundamentally stocks in Indonesia. The k-means clustering method is employed, and the number of clusters is determined using the elbow method. This approach resulted in four clusters, with the cluster identified as containing stocks with low PER, PBV, and PSR, representing the “best” portfolio each year based on valuation. Portfolios were formed from these clusters and compared to benchmark portfolios in Indonesia and globally. Global portfolios used as benchmarks include VSMPX, FXAIX, and SAM Equity. Over five years (2018–2022), the cluster-based portfolios outperformed Indonesian and global benchmarks in 2018, 2021, and 2022, while slightly underperforming global portfolios in 2019 and 2020 but still exceeding Indonesian benchmarks. This confirms that clustering techniques can deliver strong performance compared to conventional methods. A limitation of this study is that it focuses only on return performance without analyzing risk-adjusted returns, which future research should address.
Co-Authors Abdullah, Ahmad Hussain ACHMAD FAUZAN Adhar Arifuddin Adhitya Ronnie Effendie, Adhitya Ronnie Aditya Ferdiana Arief Akbar, Wahyu Sa'dun Ayu, Devina Gilar Ayudhaya, Wanvisa Saisanan Na Azhari, M Fauzan Bayu Suseno 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 Nafida Hetty Marhaeni Nanang Khuzaini, Nanang Nanda Khofifah, Thalia Novendri Isra Asriny Nuryadi Nuryadi Pradhisa, Kireyna Cindy Punik Mumpuni Wijayanti Rahman, Iqbal Fathur Riefyal Arshyzha Mustain Sekti Kartika Dini Sheilta Alphenia Subanar . Tjen, Yoel Christopher Untari, Siti Nirmala Wayan Tunas Artama Yotenka, Rahmadi