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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 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
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 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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A Spatiotemporal Analysis of Humidity Pattern in Bali using Space-Time Kriging with Seasonal Drift Nugroho, Salma Fitri; Fitriani, Rahma; Iriany, Atiek
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30874

Abstract

Climate plays a vital role in framing the characteristics of tourist activity. Humidity reflects the amount of moisture in the air relative to the maximum it can hold at a specific temperature, it has a direct influences on perceived comfort levels. Bali, one of the most popular destinations renowned for its breathtaking natural beauty and varied landscapes. However, this island is currently served by only four climate observation stations which are insufficient to capture the humidity across the island. Therefore, this research aims to model humidity levels in Bali based on four observed locations at 2019-2023 using the space-time kriging with seasonal drift and predict humidity at unobserved locations. This approach was choosen due to the strong seasonal pattern exhibited in climate data, which leading to non-stationary. The space-time kriging method is applied to the residuals. The most effective model identified was the exponential-exponential-Gaussian (Exp-Exp-Gau) model using a sum-metric structure. This model provided the lowest RMSE of 2.1442. Humidity contour maps suggest a gradual decline in humidity levels over time across Bali. This trend may have significant impacts for both environmental quality and the tourism sector. Lower humidity levels could lead to increased discomfort for tourists and potentially reduce the attractiveness of the destination. Theoretically, the development of the kriging model enhances the accuracy of predictions, as shows by the low RMSE. Practically, these findings emphasize the importance of integrating climate considerations into sustainable tourism planning and management strategies based on the humidity information.
Kombinasi Analisis Bibliometrik dengan Latent Dirichlet Allocation sebagai Pemodelan Topik Cashless Society Sepriadi, Hanifa; Rudiat Sekarsari, Cindy; Iriany, Atiek; Solimun; Rinaldo Fernandes, Adji Achmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 2: April 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2012129244

Abstract

Era digitalisasi dan komputasi telah dimulai, ditandai dengan munculnya teknologi digital yang merasuk ke berbagai aspek kehidupan, sementara data juga terus berkembang menjadi big data.  Setelah era covid 19, metode pembayaran non-tunai berkembang sangat pesat, sehingga banyak penelitian mengenai cashless society. Tujuan dari penelitian ini adalah memodelkan topik-topik yang berkaitan dengan cashless society untuk mendapatkan variabel dan indikator yang terkait dengan menggunakan analisis bibliometrik dan latent dirichlet allocation. Data penelitian ini berasal dari artikel publikasi ilmiah dan hasil web scrapping di twitter yang bertemakan cashless society. Hasil penelitian menunjukkan bahwa terdapat 5 variabel dan 21 indikator yang berhubungan dengan cashless society.   Abstract The era of digitalization and computing has begun, marked by the emergence of digital technology which permeates various aspects of life, while data also continues to develop into big data.  After the covid 19 era, non-cash payment methods developed very rapidly, so there were many studies on the cashless society. The purpose of this research is to model topics related to the cashless society to obtain related variables and indicators using bibliometric analysis and latent dirichlet allocation. This research data comes from scientific publication articles and web scrapping results on twitter with the theme of cashless society. The results showed that there are 5 variables and 21 indicators related to cashless society.
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.
Co-Authors A. Fahmi Indrayani Achmad Efendi Agus Dwi Sulistyono, Agus Dwi Alim, Viky Iqbal Azizul Amanda, Devi Veda Aniek Iriany Arditama Putra Rochmanullah Arianto, Danang Arifin Noor Sugiharto Aris Subagiyo Asaliontin, Lisa Ayu Aisyah Ashari 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 Eni Sumarminingsih Faddli Lindra Wibowo Fernandes, Adji Fernandes, Adji Achmad Rinaldo Firdaus, Cahyani Jannah Fudianita, Citra Hamdan, Rosita Haneinanda Junianto, Fachira Hartawati, Hartawati Henny Pramoedyo Henny Pramoedyo Hidayat, Kamelia Hidayatulloh, Moh. Zhafran Iwan Setiawan Junianto, Fachira Haneinanda Khoiril Anam, Khoiril Kusdarwati, Heni Maghfiro, Maulidya Maghfiro, Maulidya Maisaroh, Ulfah Marhen Andan Prasetyo Mellysa Isnaini 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 Ola, Petrus Kanisius Pramaningrum, Dea Saraswati Putra, Arditama Putri, Henida Ratna Ayu Rahma Fitriani Rinaldo Fernandes, Adji Achmad Riza, Sativandi Rosyida, Diana Rudiat Sekarsari, Cindy Sepriadi, Hanifa Solimun Solimun Solimun Solimun, Solimun Suci Astutik Sugiarto S 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