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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2024): July" : 5 Documents clear
Robust Panel Data Regression Analysis using the Least Trimmed Squares (LTS) Estimator on Poverty Line Data in Lampung Province Lestari, Windi; Widiarti; Utami, Bernadhita Herindri Samodera; Usman, Mustofa; Handayani, Vitri Aprilla
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241210

Abstract

Robust regression is an alternative method in regression analysis designed to produce stable parameter estimates, even when the data contain outliers or deviate from classical assumptions. One of its estimation techniques, the Least Trimmed Square (LTS),works by minimizing the smallest squared residuals, thereby assigning smaller weights to extreme data points. This method serves as a solution when classical approaches, such as Ordinary Least Squares (OLS), fail to meet the assumptions, especially in socio-economic data that are often complex and prone to outliers. This study employs robust regression with the LTS estimator on panel data to examine the impact of population size , population density , and registered job vacancies on poverty lines in Lampung Province. The data cover 15 districts and cities from 2019 to 2023. The analysis results show that the model obtained has a coefficient of determination of R2=0.8909. This means that the three predictor variables can explain 89.09% of the variation in the poverty line.
The Cleanness Property of The Integers Modulo n (ℤn) 'Aliyah, Dheva Ufiz; Puspita, Nikken Prima; Tjahjana, Redemtus Heru
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241214

Abstract

Assume that R is a ring with identity. A ring R is said to be clean when each of its elements can be written as the sum of an idempotent and a unit element within the ring. A stronger condition, known as strongly clean, requires that these elements commute under multiplication. As a special case, a module M over ring R is called a clean module when the endomorphism ring of the M is a clean module over R. Moreover, when the ring endomorphism of R-module M is a strongly clean, then the module M is referred to as a strongly clean. We know that the integers modulo n, denoted by ℤn, is a ring by the set of congruence classes modulo n, with standard addition and multiplication operations. In this study, we explore the cleanness properties of the ring ℤn and establish that it is a strongly clean ring. Furthermore, we study about the cleanness of ℤn as a module over ℤ and investigate the strongly cleanness of it module.
Application of Random Forest Method Classification for Glycosylation in Lysine Protein Sequences Fitriyana, Silfia; Syarif, Admi; Rossyking, Favorisen; Faisal, Mohammad Reza
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241218

Abstract

Grouping glycosylated lysine proteins into groups according to the type of glycosylation seen in the lysine protein sequence is known as glycosylation in the lysine protein sequence. In this work, the sensitivity, specificity, accuracy, and Matthew’s correlation coefficient (MCC) of the random forest approach for classifying the glycosylation of lysine protein sequences were examined. With 214 positive and 406 negative data, the lysine protein dataset derived from benchmark data contains 620 total proteins with a protein length of 15 sequences. 90% of the dataset is used for training, while 10% is used for testing. Using the R package BioSeqClass version 1.44.0, feature extraction employed protein descriptors, specifically AA Index, CTD, and PseAAC, with a total of 60 features. The Random Forest classification algorithm was used to reprocess the results with Mtry values of 4, 8, and 16. The number of trees (ntree) was randomly set to 250, 500, 750, and 1000. The best results were achieved with a dataset split of 90% training data and 10% test data, using Mtry of 42 and 1000 trees, resulting in 89.97% sensitivity, 92.79% specificity, 80.76% MCC, and 90.42% accuracy. These results demonstrate that the combination of feature extraction and the Random Forest algorithm is effective in classifying lysine proteins.
Algebraic Construction of Rough Semimodules Over Rough Rings Trisnawati, Evi; Fitriani; Faisol, Ahmad; Anwar, Yunita Septriana
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241219

Abstract

Let (℧, µ) be an approximation space, where ℧ is a non-empty set and µ is an equivalence relation on ℧. For any subset H ⊆ ℧, we can define the lower approximation and the upper approximation of H . A set H is called a rough set if its lower and upper approximations are not equal. In this study, we explore the algebraic structure that emerges when certain binary operations are defined on rough sets. Specifically, we investigate the conditions under which a subset H forms a rough semimodule over a rough semiring. We present several key erties of this structure and construct illustrative examples to support our theoretical results.
Comparison of Support Vector Regression and Random Forest Regression Performance in Vehicle Fuel Consumption Prediction Nurdin, Muhaymi; Wamiliana; Junaidi, Akmal; Lumbanraja, Favorisen Rossyking
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241221

Abstract

Predicting vehicle fuel consumption is an important aspect in improving energy efficiency and supporting sustainable transportation. This study aims to compare the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms in predicting combined vehicle fuel consumption (COMBINED, a combination of 55% urban and 45% highway). The Canadian government's Fuel Consumption Ratings dataset was used, with 2015-2023 data (9,185 entries) for training and testing, and 2024 data (764 entries) for further testing. Pre-processing involved StandardScaler for numerical features and OneHotEncoder for categorical features, followed by hyperparameter optimization using Grid Search, resulting in optimal parameters: SVR (C=100, epsilon=0.5, gamma=1) and RFR (n_estimators=200, max_depth=None, min_samples_split=2). Results show RFR is superior with R2 0.8845, RMSE 0.9671, and MAE 0.6566, compared to SVR with R2 0.8648, RMSE 1.0462, and MAE 0.7150. Evaluation on 2024 data and visualization of error distribution corroborate the superiority of RFR. This study concludes RFR is more effective for COMBINED prediction, although SVR is competitive post-optimization, and contributes to the selection of machine learning models for green vehicle technology.

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