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All Journal Limits: Journal of Mathematics and Its Applications Zero : Jurnal Sains, Matematika, dan Terapan Inferensi
Erfiani, Erfiani
IPB University
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Physics Social Sciences

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Evaluasi Kinerja Spectral Biclustering dalam Identifikasi Potensi Produksi Komoditas Hortikultura di Indonesia Lestari P, Merryanty; Sumertajaya, I Made; Erfiani, Erfiani
Limits: Journal of Mathematics and Its Applications Vol 21, No 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v21i3.21718

Abstract

Biclustering merupakan metode penggerombolan dua arah untuk menemukan subset baris dan kolom dari suatu matriks data. Spectral biclustering merupakan salah satu algoritma dari biclustering. Algoritma spectral mempunyai tiga metode normalisasi matriks antara lain independent rescaling of rows and columns, bistochastization, dan log. Penerapan spectral biclustering bertujuan untuk mengidentifikasi potensi produksi komoditas hortikultura jenis sayuran di Indonesia. Metode normalisasi bistochastization menghasilkan bicluster optimal dengan nilai rataan mean squared residue terkecil sebesar 0,079593. Bicluster yang dihasilkan sebanyak 5 bicluster. Bicluster 1 dan 2 terdiri dari wilayah Papua dan Sulawesi Tenggara memiliki potensi produksi jenis tanaman sayuran mayoritas kategori rendah di antaranya kentang, bawang merah, bawang putih, dan bawang daun. Bicluster 3 dan 4 terdiri dari sebagian besar wilayah Kalimantan, Riau, Sumatera Selatan, Nusa Tenggara Timur, dan Maluku dengan potensi produksi mayoritas terkategori sedang di antaranya cabai rawit, tomat, buncis, labu siam, dan melinjo. Bicluster 5 merupakan wilayah Jawa, Bali, Nusa Tenggara Barat, sebagian besar wilayah Sumatera dan Sulawesi, serta Kalimantan Selatan. Bicluster 5 memiliki potensi produksi terkategori tinggi pada jenis sayuran sawi, kacang panjang, terung, ketimun, dan jengkol.
The Continuum Regression Analysis with Preprocessed Variable Selection LASSO and SIR-LASSO Suruddin, Adzkar Adlu Hasyr; Erfiani, Erfiani; Sumertajaya, I Made
Inferensi Vol 8, No 1 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i1.21658

Abstract

Analyzing high-dimensional data is a considerable challenge in statistics and data science. Issues like multicollinearity and outliers often arise, leading to unstable coefficients and diminished model effectiveness. Continuum regression is a useful method for calibration models because it effectively handles multicollinearity and reduces the number of dimensions in the data. This method condenses data into autonomous latent variables, resulting in a more stable, precise, and reliable model. It is possible to use the dimensionality reduction method without losing any important information from the original data. This makes it a useful tool for making calibration models work better. In the initial phase, minimizing dimensions via variable selection is crucial. The study aims to build and test the Continuum Regression calibration model using LASSO and SIR-LASSO variable selection preprocessing methods. SIR-LASSO is a method that integrates SIR with the variable selection capabilities of LASSO. This technique aims to handle high-dimensional data by identifying relevant low-dimensional structures. LASSO improves variable selection by applying a penalty to regression coefficients, reducing the impact of less significant or redundant variables. The integration improves SIR's efficacy in assessing high-dimensional data while also enhancing model stability and interpretability. This approach seeks to address the issues of multicollinearity and model instability. We conducted simulations using both low-dimensional and high-dimensional datasets to assess the efficacy of CR LASSO and CR SIR-LASSO. RStudio version 4.1.3 was used for the analysis. The "MASS" package was used to create data with a multivariate normal distribution. The "glmnet" package was used for LASSO variable selection, and the "LassoSIR" package was used for SIR-LASSO variable selection. In the simulation itself, LASSO surpasses SIR-LASSO in variable selection by yielding the lowest RMSEP value in every scenario. On the other hand, SIR-LASSO becomes less stable as the number of dimensions increases, which suggests that it is sensitive to large changes in variables. As shown by lower median RMSEP values across a range of sample sizes and situations, CR LASSO is usually better at making predictions than SIR-LASSO. The RMSEP distributions for LASSO are consistently tighter, which means that its performance is more stable and reliable compared to SIR-LASSO, whose data has more outliers and more variation. Even with a growing sample size, LASSO maintains its advantage, particularly when setting the value at 0.5. SIR-LASSO, although occasionally competitive, generally yields more variable results, particularly with larger sample sizes. Overall, LASSO appears to be a more reliable option for CR model with pre-processed variable selection.
The Impact of Using A Linear Model for the Ordinal Response of Mixture Experiments Syafitri, Utami Dyah; Erfiani, Erfiani; Soleh, Agus M; Wigena, Aji Hamim
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.25760

Abstract

In a sensory test, the response is a Likert scale, which belongs to the ordinal scale. The ordinal response can be analyzed using a linear model approach; however, this approach can be misleading.  This research aims to compare three different methods for ordinal response: the average score, the second-order Scheffe model, and the ordinal logistic model. The case study focused on the response to the taste of cookies resulting from the mixture experiment. The mixture experiment is one type of experimental design which is commonly used for product formulation.  The research involved three ingredients with different lower bonds.  The D-optimal design which also the {3,2} simplex-lattice design was chosen for the experiment. The three methods were conducted, and they all yielded the same results for the optimum composition; however, the ordinal model provided more information about the data's characteristics. The optimal formulation of each ingredient was 10%, 20%, 70%. 
Co-Authors Aji Hamim Wigena I Made Sumertajaya Lestari P, Merryanty Soleh, Agus M Suruddin, Adzkar Adlu Hasyr Utami Dyah Syafitri