Article Per Year (5 Year)
p-Index From 2021 - 2026
1.857
P-Index
This Author published in this journals
All Journal Media Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Innovation and Applied Technology JOIN (Jurnal Online Informatika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika)
Ani Budi Astuti
Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Brawijaya, Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Two-stage Gene Selection and Classification for a High-Dimensional Microarray Data Rochayani, Masithoh Yessi; Sa'adah, Umu; Astuti, Ani Budi
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.569

Abstract

Microarray technology has provided benefits for cancer diagnosis and classification. However, classifying cancer using microarray data is confronted with difficulty since the dataset has high dimensions. One strategy for dealing with the dimensionality problem is to make a feature selection before modeling. Lasso is a common regularization method to reduce the number of features or predictors. However, Lasso remains too many features at the optimum regularization parameter. Therefore, feature selection can be continued to the second stage. We proposed Classification and Regression Tree (CART) for feature selection on the second stage which can also produce a classification model. We used a dataset which comparing gene expression in breast tumor tissues and other tumor tissues. This dataset has 10,936 predictor variables and 1,545 observations. The results of this study were the proposed method able to produce a few numbers of selected genes but gave high accuracy. The model also acquired in line with the Oncogenomics Theory by the obtained of GATA3 to split the root node of the decision tree model. GATA3 has become an important marker for breast tumors.
Geographically Weighted Random Forest Model for Addressing Spatial Heterogeneity of Monthly Rainfall with Small Sample Size Damayanti, Rismania Hartanti Putri Yulianing; Astutik, Suci; Astuti, Ani Budi
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.32161

Abstract

Rainfall modeling often involves complex spatial patterns that vary across locations. Traditional spatial models such as Geographically Weighted Regression (GWR) assume linear relationships and may fall short in capturing nonlinear interactions among predictors and the small sample size is more challenging to fix the assumptions. To address this limitation, this study applies the Geographically Weighted Random Forest (GWRF) method is a hybrid approach that integrates Random Forest (RF), a non-parametric machine learning algorithm with geographically weighted modeling. GWRF is advantageous as it accommodates both spatial heterogeneity and nonlinear relationships, making it suitable for modeling monthly rainfall, which is inherently spatially varied and influenced by complex factors. This study aims to implement and evaluate the performance of the GWRF model in monthly rainfall prediction across East Java. The model is tested using various numbers of trees to determine the optimal structure, and its performance is assessed using Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), and corrected AIC (AICc). Results indicate that the model tends to overestimate the Out-of-Bag (OOB) Error at all tree variations, with the smallest RMSE (85.68) achieved at 750 trees. Humidity emerges as the most influential variable in predicting monthly rainfall in the region, based on variable importance analysis
Co-Authors Achmad Efendi Akrimah, Yasma Aziza Ul Alim, Viky Iqbal Azizul Alvia, Siva Nur Azizah, Laila Nur Candra Dewi Damayanti, Rismania Hartanti Putri Yulianing Elfrida Ratnawati Eni Sumarminingsih Guci, Abdi Negara Handayani, Sri Henny Pramoedyo Kalangi, Olyvia Maria Maisaroh, Ulfah Masithoh Yessi Rochayani Mitakda, Maria Bernadetha T. Mohammad Nuh Muhammad, Alifiandi Rafi Pratikno, Mochammad Yosi Putri, Meirida Karisma Putro, Bayu Hangga Trio Eko Rahma Fitriani Rahmi, Nur Silviyah Rifan, Mohamad Rohmah, Devi Azarina Manzilir Rotchildi, Gusti Ayu Putu Rawi Sa'adah, Umu Sa'diyah, Nur Kamilah Seftyaningtyas, Endryana Setiarini, An Nisa Dwi Suci Astutik Waego Hadi Nugroho Wigbertus Ngabu