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Integra: Journal of Integrated Mathematics and Computer Science
Published by Universitas Lampung
ISSN : -     EISSN : 31091792     DOI : https://doi.org/10.26554/integrajimcs
Core Subject : Science, Education,
Computer Science & IT Mathematics
Integra : Journal of Integrated Mathematics and Computer Science is the international journal in the field of Mathematics and Computer Science. Integra : Journal of Integrated Mathematics and Computer Science publish original research work both in a full article or in a short communication form, review article, and technical article in the field of Mathematics and Computer Science. Scope of this journal is : Mathematics Applied Mathematics Statistics Applied Statistics Data Science Computer Science
Arjuna Subject : Matematika - Matematika Komputer
Articles 43 Documents
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Application of Linear Side Conditions Tβ=0 on Non-Full Rank Linear Models Meliyana Bohori; Mustofa Usman; Riza Sawitri; Edwin Russel; Jamal Ibrahim Daoud
Integra: Journal of Integrated Mathematics and Computer Science Vol. 3 No. 2 (2026): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20263254

Abstract

Linear model is one of the foundation of statistical analysis, including analysis of variance (ANOVA). However, under non-full rank conditions, the design matrix exhibits linear depen dence, preventing the unique estimation of model parameters. This study aims to address this issue and to cope the problem by applying side conditions in the form of the linear constraint TB=0 without altering the model structure. The methods employed include the formulation of a restricted linear model, the derivation of parameter estimators, and simulation studies. The results show that side conditions produce unique, stable, and nearly unbiased parameter estimators. Furthermore, the results indicate that the power of test increases as differences between parameters increase and decreases as the variance of the error term increases. Thus, this approach is effective in ensuring the uniqueness of parameter estimates and improving the quality of analysis in ANOVA models with non-full rank design matrices.
Confidence Interval Estimation of Linear Model Parameter Ratios Via Fieller’s Method Under Heteroscedasticity Using Weighted Least Squares (Simulation Study) Pretty Enjelina Br Pelawi; Mustofa Usman; Widiarti; Edwin Russel; Luvita Loves
Integra: Journal of Integrated Mathematics and Computer Science Vol. 3 No. 2 (2026): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20263255

Abstract

Heteroskedasticity in linear models is a common problem that can cause parameter estimators to become inefficient and the estimation of the variance-covariance matrix to become inconsistent. This condition leads to incorrect and inaccurate statistical inference, including the construction of confidence intervals. Confidence interval estimation is not only performed for a single parameter but can also involve ratios, in which the Fieller method is commonly used for constructing confidence intervals for parameter ratios in linear models. This study examines the estimation of confidence intervals for parameter ratios using the Fieller method under heteroskedastic conditions by applying the Weighted Least Squares (WLS) method. Through simulations with variations in heteroskedasticity levels of λ = 0, 1, 3, and 5 and sample sizes of n = 30 and n = 50, the performance of the method was evaluated based on bias, Coverage Probability (CP), and Average Length (AL). The results show that the WLS method produces unbiased parameter estimators at all levels of heteroskedasticity. In addition, the Fieller method produces CP values around 0.95 with only small fluctuations and tends to yield shorter AL values. Therefore, the combination of the WLS and Fieller methods is proven to be stable and efficient for parameter estimation and confidence interval estimation of parameter ratios under heteroskedastic conditions.
Classifying Electronic Health Records with Long Short-Term Memory and Gated Recurrent Unit Networks Dian Kurniasari; Zida Bunga Sobara; Riza Sawitri
Integra: Journal of Integrated Mathematics and Computer Science Vol. 3 No. 2 (2026): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20263262

Abstract

Electronic Health Records (EHR) are a complete digital store of medical information which contains valuable information about a person’s health status. These data are an important tool for health care professionals to decide the management of the patient, i.e. the need for hospitalization (inpatient care) or outpatient treatment. The importance of EHR data underscores the need for hospitals to act quickly and develop appropriate follow-up for patients. The aim of this study is to assess and compare the performance of two deep learning models, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) for accurate classification of EHR data to predict whether a patient is hospitalized (inpatient or outpatient) based on laboratory test results. This study aims to compare these models to identify the best way to classify patient treatment needs. The data set used in this study is EHR data from a private hospital in Indonesia which contains haematocrit, haemoglobin, erythrocytes, leukocytes, thrombocytes, MCH, MCHC, MCV, age, gender, and source (inpatient or outpatient). The data was pre-processed by labelling, variable selection and handling missing and duplicate values. The data set is split for training and testing the models, LSTM and GRU. Performance evaluation is based on accuracy, training time and other relevant metrics to evaluate both the classification precision and computational efficiency of the models. The classification accuracies of the two models were close to each other with LSTM being 75.57% and GRU being 75.34%. However, GRU showed better training efficiency, executing faster than LSTM model. In the cases where the training time efficiency is the priority, the GRU model is the better choice for its faster speed. However, LSTM performs slightly better in terms of accuracy, especially in classifying outpatient cases, making it more appropriate for applications where accuracy is of high importance.

Page 5 of 5 | Total Record : 43

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