cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
AS Ahmar
Contact Email
journal@ahmar.id
Phone
-
Journal Mail Official
arrusmathscience@ahmar.id
Editorial Address
Jalan Karaeng Bontomarannu No. 57 Kecamatan Galesong, Kabupaten Takalar Provinsi Sulawesi Selatan, Indonesia
Location
Unknown,
Unknown
INDONESIA
ARRUS Journal of Mathematics and Applied Science
Published by Yayasan Ahmar Cendekia Indonesia
ISSN : 27767922     EISSN : 28073037     DOI : https://doi.org/10.35877/mathscience.v1i1
Core Subject : Science, Education,
Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Mathematics Physics
Aim: To drive forward the fields related to Applied Sciences, Mathematics, and Its Education by providing a high-quality evidence base for academicians, researchers, scholars, scientists, managers, policymakers, and students. Scope: The focus is to publish papers that are authentic, original, and plagiarism free and should in interest of society and the world.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "Vol. 6 No. 1 (2026)" : 2 Documents clear
A Public Sector Innovation: Determinants analysis of sustainability for Examining the Role of User Impact Mediation in the Dolan Banyumas Application Lestari, Suci; Tobirin; Nurdin, Arif Muhammad; Huriyah, Siti Balqis
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4785

Abstract

This study examines the determinants of sustainability of public sector innovation by examining the mediating role of user innovation impact in the Dolan Banyumas application. This study uses a quantitative approach with the Structural Equation Modeling method based on Partial Least Squares (SEM-PLS). Data was collected through an online survey of 60 respondents who used the Dolan Banyumas application who were selected using the purposive sampling technique. The research model examines the relationship between three main constructs, namely the perception of innovation novelty, the impact of innovation, and the sustainability of innovation. The results of the study show that the perception of innovation novelty has a positive and significant effect on the impact of innovation and the sustainability of innovation. In addition, the impact of innovation has been shown to have a significant effect on the sustainability of innovation and effectively mediates the relationship between the perception of innovation novelty and innovation sustainability. The value of the determination coefficient shows that this research model is able to explain 73.9% variation in innovation impact and 73% variation in innovation sustainability. The main findings of this study show that the sustainability of digital innovation in the public sector is not only determined by the aspect of technological novelty alone, but is highly dependent on the benefits or real impacts that are directly felt by service users. Keywords: Public Sector Innovation, Digital Governance, Innovation Sustainability, Innovation Impact, SEM-PLS.
Application of LASSO Regression for the Identification of Underdeveloped Regions in Central Sulawesi Alfairus, Muh. Qodri; Amira, Husnul; Utomo, Agung Tri; Abbas, Nur Abshari
ARRUS Journal of Mathematics and Applied Science Vol. 6 No. 1 (2026)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4813

Abstract

This study aims to identify the main factors influencing regional underdevelopment in Central Sulawesi through Human Development Index (HDI) modeling and to develop a robust predictive model. To address the challenges of multicollinearity and the limited number of observations (13 districts/cities with 10 variables), this study employs LASSO (Least Absolute Shrinkage and Selection Operator) regression, which is capable of simultaneously shrinking coefficients and selecting variables. The data used are sourced from the 2019 publication of the Central Statistics Agency (BPS). The analysis was conducted using descriptive statistics, Ordinary Least Squares (OLS) modeling, VIF tests, and LASSO regression with cross-validation (leave-one-out cross-validation). The results indicate that very high multicollinearity (VIF > 10 for most variables) renders the OLS model unstable. Conversely, LASSO regression yielded better performance with superior RMSE (1.282), MAE (1.075), and R² (0.918) values compared to OLS (RMSE 21.67; MAE 9.85; R² 0.78). Thus, LASSO is more suitable for limited data with high multicollinearity. The selected significant variables include the percentage of the poor population, the open unemployment rate, shopping facilities, the presence of hospitals, the population density ratio, and the number of elementary and secondary schools.

Page 1 of 1 | Total Record : 2

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 6 No. 1 (2026) Vol. 5 No. 2 (2025) Vol. 5 No. 1 (2025) Vol. 4 No. 2 (2024) Vol. 4 No. 1 (2024) Vol. 3 No. 2 (2023) Vol. 3 No. 1 (2023) Vol. 2 No. 2 (2022) Vol. 2 No. 1 (2022) Vol. 1 No. 2 (2021) Vol. 1 No. 1 (2021) More Issue