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All Journal The Journal of Experimental Life Sciences (JELS) Jurnal Sosial Ekonomi Pertanian CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer JURNAL ILMIAH MATEMATIKA DAN TERAPAN PERFORMA : Media Ilmiah Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Proceeding Simposium Nasional Kelautan dan Perikanan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Pertanian Agros Zero : Jurnal Sains, Matematika, dan Terapan ComTech: Computer, Mathematics and Engineering Applications Jurnal ABDINUS : Jurnal Pengabdian Nusantara Inferensi MATHunesa: Jurnal Ilmiah Matematika Pakem : Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Official Statistics ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Journal of Community Empowerment International Journal of Informatics Engineering and Computing
Atiek Iriany
Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

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CLASSIFICATION OF STUNTING USING GEOGRAPHICALLY WEIGHTED REGRESSION-KRIGING CASE STUDY: STUNTING IN EAST JAVA Iriany, Atiek; Ngabu, Wigbertus; Arianto, Danang; Putra, Arditama
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (494.872 KB) | DOI: 10.30598/barekengvol17iss1pp0495-0504

Abstract

Geographically Weighted Regression Kriging (GWRK) is a special case of Geographically Weighted Regression (GWR) model, which is modeling with the effect of spatial autocorrelation on the GWR model error. The purpose of this research is to obtain a GWRK model between the factors that affect stunting density for each site viewed from the district center point in East Java Province and to make a prediction map based on the GWRK modeling. The data used was obtained from Basic Health Research (RISKESDAS) and the East Java Health Profile Book for 2021. The units of observation in this study were 38 districts in East Java.. Based on the GWR modeling results, it was found that the GWR model error contained spatial autocorrelation so that GWR model can be formed. From the GWRK modeling using stunting prevalence data in East Java in 2021, it was found that the GWR model was better than the global regression. Through prediction and prediction mapping formed from the GWR-Kriging modeling, it could be seen that stunting in regencies in East Java was evenly distributed . The interpolation map showed that the stunting forecasting values using the Kriging GWR interpolation ranged from 27% to 46%.
STRUCTURAL EQUATION MODELING MULTIGROUP INDIRECT EFFECTS ON BANK MORTGAGE PAYMENT TIMELINESS Maisaroh, Ulfah; Fernandes, Adji Achmad Rinaldo; Iriany, Atiek
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2359-2366

Abstract

Structural Equation Modeling (SEM) is a multivariate statistical method that is used to thoroughly explain the relationship between latent variables simultaneously. Until now, SEM continues to grow in research. This research was conducted to examine the indirect effect on the timeliness of paying bank mortgages with a multi-group moderation approach. Analysis to identify factors that influence the timeliness of paying bank mortgages is an important step for banks before extending credit to prospective customers. The data used in this research is secondary data from research grants from National Competitive Basic Research. The data scale used is the Likert scale for exogenous, mediating endogenous, and pure endogenous variables. While the moderating variable uses a dummy variable. The results of the study show that the indirect effect of Capacity and Capital on Pay on Time for Bank Mortgage customers has a significant effect, both on non-current collectibility status and current collectibility status. This is evidenced by the Sobel test value greater than (1.96) on the indirect effect test, and the p-value of the Wald test is smaller than (0.05) on the moderation indirect effect test. Mediator variable is able to increase the effect of exogenous variables on endogenous variable Customers with current collectibility status have a stronger influence on timely payments than customers with non-current collectibility status.
RAINFALL MODELING USING THE GEOGRAPHICALLY WEIGHTED POISSON REGRESSION METHOD Iriany, Atiek; Ngabu, Wigbertus; Ariyanto, Danang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0627-0636

Abstract

Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.
PATH ANALYSIS OF FACTORS INFLUENCING CASHLESS SOCIETY DEVELOPMENT USING BOOTSTRAP RESAMPLING Pramaningrum, Dea Saraswati; Fernandes, Adji Achmad Rinaldo; Iriany, Atiek; Solimun, Solimun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2071-2082

Abstract

Path analysis can be applied to various fields, one of which is the field of banking economics. This study is aimed to examine what factors significantly affect the development of cashless society both directly and indirectly. There are many studies related to the development of cashless society but there has been no research that analyzes the relationship between marketing mix variables, such as product, price and promotion, with the development of cashless society. The data used came from the results of questionnaires with respondents of bank customers in Jakarta. Direct influence tests are carried out using bootstrap resampling hypothesis tests so that they are free from data distribution assumptions. It was found that product and digitalization of electronic money had a significant direct effect on the development of cashless society while price had a significant indirect effect on the development of cashless society.
MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE Ola, Petrus Kanisius; Iriany, Atiek; Astutik, Suci
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp2035-2046

Abstract

Poverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing poverty levels in NTT Province. The MGWR model is an extension of the Geographically Weighted Regression (GWR), which allows some variables in the model to have local effects while others have global effects. The adaptive weighting function in the MGWR model enhances the analysis by providing different weights at each location according to its local characteristics, thus making the results more accurate and representative for each area. The data includes economic, social, and infrastructure variables from 22 districts/cities in NTT Province for 2023. The MGWR model with an adaptive weighting function is applied to model the relationship between these variables and poverty levels. The analysis integrates statistical software to manage and analyze spatial data. The study findings show that the MGWR model with an adaptive weighting function offers better estimates than the global regression and GWR models. The results revealed the smallest AIC value for the MGWR model at 104.1888, compared to the global regression model at 140.1427 and the GWR model at 117.6174. This model successfully identifies significant local and global variables and shows variations in influence at different locations in NTT Province. These findings provide valuable insights for policymakers and practitioners in designing and implementing more effective poverty alleviation strategies tailored to local conditions in NTT Province.
ANALYSIS OF PATH NONPARAMETRIC TRUNCATED SPLINE MAXIMUM CUBIC ORDER IN BANKING CREDIT OF RISK BEHAVIOR MODEL Amanda, Devi Veda; Iriany, Atiek; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2639-2652

Abstract

Path analysis tests the relationship between variables through cause and effect. The assumption of linearity must be met before conducting further tests on path analysis. If the shape of the relationship is nonlinear and the shape of the curve is unknown, a nonparametric approach is used, one of which is a truncated spline. The purpose of this study is to estimate the function and obtain the best model on the nonparametric truncated spline path of linear, quadratic, and cubic orders with 1 and 2-knot points and determine the significance of the best function estimator in banking credit of risk behavior model through the jackknife resampling method. This study uses secondary data through questionnaires to KPR debtor consumers, as many as 100 respondents. Based on the results of the analysis, it is known that the best-truncated spline nonparametric path model is the quadratic order of 2 knots with a coefficient of determination of 85.50%; the significance of the best-truncated spline nonparametric path estimator shows that all exogenous variables have a significant effect on endogenous variables.
Integrating Path Analysis and Kendall’s Tau-based Principal Component Analysis to Identify Determinants of Child Health Alim, Viky Iqbal Azizul; Iriany, Atiek; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun; Utomo, Candra Rezzining Wulat Sariro Weni
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (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.v10i2.31156

Abstract

This study develops a latent variable path analysis model using a Mixed-Scale Principal Component Analysis (PCA) approach based on Kendall’s Tau correlation to identify key determinants of child health in Batu City, Indonesia. Primary data were collected from 100 mothers with children under five years old through questionnaires. The variables examined include Family Demographics, Nutritional Consumption, and Child Health Condition, each measured using mixed-scale indicators (ordinal and numerical). Kendall’s Tau-based PCA was applied to reduce data dimensionality and construct latent variables, which were then integrated into a path analysis model. The results show that maternal age is the most dominant indicator in shaping the Family Demographics construct, while balanced nutritional food is the strongest indicator forming the Nutritional Consumption construct. Path analysis further reveals that Family Demographics significantly affect Child Health Condition both directly and indirectly through Nutritional Consumption, with a coefficient of determination of 77.62\%. These findings underscore the critical role of demographic and nutritional factors in determining child health outcomes and highlight the methodological advantage of Kendall’s Tau-based mixed-scale PCA for analyzing heterogeneous indicator data within a structural path framework.
PREDICTION OF SOIL PARTICLES USING A SPATIALLY ADAPTIVE GEOGRAPHICALLY WEIGHTED K-NEAREST NEIGHBORS ORDINARY LOGISTIC REGRESSION APPROACH Pramoedyo, Henny; Ngabu, Wigbertus; Iriany, Atiek; Riza, Sativandi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2815-2830

Abstract

Soil particle prediction is crucial in various fields, including agriculture, environmental management, and geotechnical applications. The spatial variation of soil texture significantly affects land fertility, erosion risk, and construction feasibility. However, conventional statistical methods and machine learning techniques often fail to capture the complex spatial heterogeneity in soil distribution. This study proposes the Geographically Weighted K Nearest Neighbors Ordinary Logistic Regression (GWKNNOLR) method to improve the accuracy of soil particle classification by integrating geographically weighted regression with an adaptive spatial weighting mechanism using the K Nearest Neighbors (KNN) algorithm. The objective of this research is to develop and evaluate a spatially adaptive classification model that more accurately predicts soil particle categories, namely sand, silt, and clay, by incorporating local spatial dependencies using GWKNNOLR in the Kalikonto watershed (DAS Kalikonto) in Batu. This study utilizes field measurement data combined with digital terrain modeling to analyze the relationship between local morphological variables and soil texture classification (sand, silt, and clay). The study area includes 50 observation points and 8 test variables. The model's performance is compared to the Ordinary Logistic Regression (OLR) method. The results indicate that GWKNNOLR achieves a classification accuracy of 88 percent, outperforming OLR, which only reaches 80 percent. Integrating KNN as a spatial weighting mechanism enhances adaptability to variations in sample distribution, leading to more accurate predictions. These findings emphasize the importance of considering spatial dependencies in soil texture modeling. The proposed method can support sustainable land resource management, erosion risk mitigation, and precision agriculture by providing more reliable soil classification. Future research may explore further optimization of spatial weighting mechanisms and the application of this method in different geographical regions.
Assessing Solar Energy Potential through Sunshine Hour Interpolation using Spatiotemporal Kriging with Local Drift Nugroho, Salma Fitri; Fitriani, Rahma; Iriany, Atiek
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 4 (2025): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.32048

Abstract

Solar energy is a key renewable resource, particularly valuable in tropical regions like Bali, where sunlight is consistently available throughout the year. Accurate estimation of sunshine duration is essential for assessing solar energy potential, as it directly affects photovoltaic (PV) system performace and informs strategic planning for renewable energy development. This study aims to develop a spatiotemporal statistical interpolation model to estimate and predict sunshine duration patterns across Bali, thereby enhancing the planning and deployment of solar energy infrastructure. This quantitative research applies space-time kriging with local drift using sunshine duration data (in hours) collected from four meteorological stations between 2019 and 2023. The method effectively captures spatial and temporal dependencies by integrating local drift as a deterministic trend component. Among several models tested, the Gaussian-Gaussian-Gaussian (Gau-Gau-Gau) combination delivered the best performance, with an RMSE of 2.3085. The results show a clear seasonal cycle, with higher sunshine duration during the dry season (May–October) and lower values in the wet season (November–March). Northern and eastern Bali, particularly Buleleng and Karangasem, demonstrate the highest solar potential, while central mountainous areas show lower sunshine exposure due to cloud coverage. These results offer not only a methodological contribution through the application of spatiotemporal kriging with local drift, but also a practical framework for decision-makers. The insights can guide strategic placement of solar farms, optimize energy yield forecasts, and support resilient infrastructure planning in line with Bali’s climatic realities and energy needs.
The Application of Truncated Spline Semiparametric Path Analysis on Determining Factors Influencing Cashless Society Development Pramaningrum, Dea Saraswati; Fernandes, Adji Achmad Rinaldo; Iriany, Atiek; Solimun, Solimun
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19913

Abstract

Semiparametric path analysis is a combination of parametric and nonparametric path analysis. Semiparametric path analysis is used when there are partially nonlinear and unknown patterns of relationships. One approach to semiparametric pathways is truncated spline. Truncated spline approach tends to search for their own estimation of regression functions according to the data. This is because in the truncated spline there are knot points, which are intersection points that indicate changes in data behavior patterns. Truncated spline semiparametric path analysis will be applied to this study to determine the variables that have a significant effect on the development of the Cashless Society so that the result can be used as a reference for banks and the government in maximizing non-cash-based community development. The data used is the result of a questionnaire with 100 respondents of mobile banking users in Jakarta and will be analyzed using R Studio. Based on the results, it was found that the optimal knot point in the truncated spline function is 3 with many knots is 1, thus dividing the condition of digitizing electronic money into 2 regimes. It was concluded that the product and digitalization of electronic money had a significant effect on the development of cashless society where the modeling obtained could explain 83.87548% of the data. However, when electronic digitalization increases through the value of knot points, the development of cashless society tends to stagnate. This could be due to people who are not ready when the condition of digitizing electronic money is increasingly sophisticated because the available electronic money features are increasingly complex. Therefore, it is important for banks to pay attention to the sophistication of electronic money features provided to customers and adjust the target market so that customers are more accustomed and comfortable to use electronic money in the future.
Co-Authors Achmad Efendi Adji Achmad Rinaldo Fernandes Agung Sugeng Widodo Agus Dwi Sulistyono, Agus Dwi Alim, Viky Iqbal Azizul Amanda, Devi Veda Ani Budi Astuti Aniek Iriany Arditama Putra Rochmanullah Arianto, Danang Arifin Noor Sugiharto Aris Subagiyo Asaliontin, Lisa Ashari, Ayu Aisyah Ayunda Sovia, Nabila Bambang Dwi Argo Bestari Archita Safitri Budi Astuti, Ani Cecep Kusmana Chairunissa, Abela Danang Ariyanto Darmanto Darmanto David Forgenie Dewi, Anggi Seftia Dhanny Septimawan Sutopo Elok Waziiroh Eni Sumarminingsih Faddli Lindra Wibowo Fernandes, Adji Fernandes, Adji Achmad Rinaldo Firdaus, Cahyani Jannah Fudianita, Citra Hamdan, Rosita Haneinanda Junianto, Fachira Hartawati, Hartawati Heni Kusdarwati Henny Pramoedyo Hidayat, Kamelia Hidayatulloh, Moh. Zhafran Indrayani, Fahmi Iwan Setiawan Junianto, Fachira Haneinanda Kamelia Hidayat Khoiril Anam, Khoiril Maghfiro, Maulidya Maghfiro, Maulidya Maisaroh, Ulfah Marhen Andan Prasetyo Mellysa Isnaini Moh. Zhafran Hidayatulloh Muhamad Firdaus Muhamad Ridwan Ni Wayan Surya Wardhani NI WAYAN SURYA WARDHANI Nikmatul Khoiriyah Novi Nur Aini Novi Nur Aini, Novi Nur Nugroho, Arief Budi Nugroho, Salma Fitri Nur Silviyah Rahmi Oktavia , Nur Sofi Sely Ola, Petrus Kanisius Pramaningrum, Dea Saraswati Prayudi Lestantyo Putra, Arditama Putri, Henida Ratna Ayu Rahma Fitriani Ridlo, Mahmuddin Rinaldo Fernandes, Adji Achmad Riza, Sativandi Rosyida, Diana Rudiat Sekarsari, Cindy Sepriadi, Hanifa Solimun Solimun Solimun Solimun, Solimun Suci Astutik Sugiarto S Sukamto, Ika Sumiyarsi Suryawardhani, Ni Wayan Sutopo, Dhanny Septimawan Ullah, Mohammad Ohid Utomo, Candra Rezzining Wulat Sariro Weni Waego Hadi Nugroho Wardhani, Ni Wayan Surya Wigbertus Ngabu Yuliana, Mila