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All Journal Media Statistika Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Jurusan Arsitektur Jurnal Mahasiswa Statistik Journal of Natural A Natural B Jurnal Ekonomi & Studi Pembangunan Science and Technology Indonesia Jurnal Maneksi (Management Ekonomi Dan Akuntansi) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Media Mahardhika ComTech: Computer, Mathematics and Engineering Applications Jurnal Ilmiah Aquinas Industri Inovatif : Jurnal Teknik Industri Inferensi JIHAD : Jurnal Ilmu Hukum dan Administrasi Journal of Multidisciplinary Applied Natural Science Jurnal Ilmiah Manajemen dan Bisnis (JIMBI) Jurnal Dimamu Pangripta Jurnal Ilmiah Kajian Perencanaan Pembangunan Jurnal Sosial Ekonomi Perikanan Indonesian Journal of Statistics and Its Applications
Rahma Fitriani
Department Of Mathematics, University Of Brawijaya, Malang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY) Menufandu, Dahlia Gladiola Rurina; Fitriani, Rahma; Sumarminingsih, Eni
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 (399.767 KB) | DOI: 10.30598/barekengvol17iss1pp0487-0494

Abstract

Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach. The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.
CLUSTER FAST DOUBLE BOOTSTRAP APPROACH WITH RANDOM EFFECT SPATIAL MODELING Ngabu, Wigbertus; Fitriani, Rahma; Pramoedyo, Henny; Astuti, Ani Budi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0945-0954

Abstract

Panel data is a combination of cross-sectional and time series data. Spatial panel analysis is an analysis to obtain information based on observations affected by the space or location effects. The effect of location effects on spatial analysis is presented in the form of weighting. The use of panel data in spatial regression provides a number of advantages, however, the spatial dependence test and parameter estimators generated in the spatial regression of data panel will be inaccurate when applied to areas with a small number of spatial units. One method to overcome the problem of small spatial unit size is the bootstrap method. This study used the fast double bootstrap (FDB) method by modeling the poverty rate in the Flores islands. The data used in the study was sourced from the BPS NTT Province website. The results of Hausman test show that the right model is Random effect. The spatial dependence test concludes that there is a spatial dependence and the poverty modeling in the Flores islands tends to use the SAR model. SAR random effect model R2 shows the value of 77.38 percent and it does not meet the assumption of normality. Spatial Autoregressive Random effect model with the Fast Double Bootstrap approach is able to explain the diversity of poverty rate in the Flores Island by 99.83 percent and fulfilling the assumption of residual normality. The results of the analysis using the FDB approach on the spatial panel show better results than the common spatial panel.
ENHANCING WEIGHTED FUZZY TIME SERIES FORECASTING THROUGH PARTICLE SWARM OPTIMIZATION Zamelina, Armando Jacquis Federal; Astutik, Suci; Fitriani, Rahma; Fernandes, Adji Achmad Rinaldo; Ramifidisoa, Lucius
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/barekengvol18iss4pp2675-2684

Abstract

Climate change is a complex process that has far-reaching consequences for daily living. Temperature is one of the climatic features. Knowing its future value through a forecasting model is critical, as it aids in earlier strategic decision-making. Without considering spatial factors, this study investigates an Air Temperature variable forecasting. Weighted Fuzzy Time Series (WFTS) is one of the forecasting techniques. Furthermore, the length of the interval and the extent to which previous values (Order length) are utilized in predicting the subsequent value are pivotal factors in WFTS modelization and its forecasting accuracy. Therefore, this research investigates the interval length and the Order length of the WFTS through the Particle Swarm Optimization (PSO) approach. The variable used is the air temperature in Malang, Indonesia. The dataset is taken from BMKG-Indonesia. The forecasting performance of classical WFTS is enhanced by setting an appropriate order level and employing Particle Swarm Optimization (PSO) to determine the optimal interval fuzzy length. As indicated by the Evaluation matrices in the result section, the proposed optimization overtaken the classical WFTS in term of accuracy. The evaluation indicates a Mean Absolute Percentage Error (MAPE) value of 1.25 and a Root Mean Square Error (RMSE) of 0.32 for the Proposed model. In contrast, the classical WFTS demonstrates a MAPE of 2.26 and RMSE of 0.58. The implementation of the PSO provides solid insights for Air temperature forecasting accuracy.
CLUSTERING WITH SKATER METHODS AND UTILIZATION OF LISA ON UNEMPLOYMENT RATE Abdila, Naufal Shela; Fitriani, Rahma; Pratama, Muhamad Liswansyah
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/barekengvol19iss4pp2633-2646

Abstract

Spatial cluster analysis is an analysis used to identify a spatial pattern or geographical grouping of data. One method that can be used in spatial cluster analysis is Spatial Cluster Analysis by Tree Edge Removal (SKATER). This research aims to analyze the spatial pattern of the Unemployment Rate in East Java by utilizing the SKATER method. The clustering results are then used to create a weighting matrix, which is used to find local spatial autocorrelation values ​​using the Local Indicators of Spatial Association (LISA) index. The data is taken from BPS East Java with variables including unemployment rate, education level, minimum wage, Human Development Index, and population density. The results show that this approach is able to identify significant local spatial patterns. However, the selection of the number of clusters and input variables proved to be very influential on the results, so care needs to be taken.
Rancang Bangun Sistem Informasi Keuangan Sekolah Berbasis Client Server di SMK Ganesha Cimanggung Fitriani, Rahma; Supriatna, Encep
Jurnal Dimamu Vol. 4 No. 3 (2025)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/dimamu.v4i3.1619

Abstract

In this study it was found that the system used in the data processing process was still manual and semi-computerized using paper and Mirosoft Excel. This still causes difficulties for employees when looking for data on students who have not made payments and who have made payments, recording expenses, and when compiling reports. The method used in this research is System Depelopment Life Cycle (SDLC) which includes planning, needs analysis, design, code, test, maintenance, and hardware and software requirements. The author uses Flowmap analysis, Data Flow Diagram (DFD), Entity Relationship Diagram (ERD), Data Dictionary, Structure Chart. The implementation of the school financial information system in smk ganesha cimanggung is built using Microsoft Visual Foxpro 9.0. The results of designing this information system to facilitate employees in finding the balance of receipts, payments, expenses and making reports
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.
Dual Optimization of Weighted Fuzzy Time-Series Forecasting: Particle Swarm Optimization and Lagrange Quadratic Programming Zamelina, Armando Jacquis Federal; Astutik, Suci; Fitriani, Rahma
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22554

Abstract

Time series Forecasting is one of crucial techniques that helps with strategic decision-making and mitigating potential risks –One of which is Weighted fuzzy time series (WFTS). Moreover, the interval length of the WFTS plays a crucial role in its modelization and accuracy in predicting future values. Therefore, this research implements a dual optimization on WFTS, which are (1) Particle Swarm Optimization to find the optimum interval length of the WFTS and (2) a Lagrange quadratic to optimize the weight of the fuzzy interval. In this research, a univariate Average Air Temperature located in Malang is used to perform forecasting model. The dataset is taken from BMKG-Indonesia. This research aims to acquire an optimized interval length on fuzzy time series forecasting, i.e., improving its accuracy by finding the optimal interval length. Based on the result, the proposed dual optimization model outperforms the classical WFTS on forecasting. The proposed model excels based on the evaluation matrix values. It has been noticed also that implementing PSO to find the optimum interval length has improved the accuracy of the classical WFTS. The classical WFTS has MAPE and RMSE of 2.4 and 0.73, respectively, while the proposed dual optimized model has 1.01 and 0.3. This approach identifies the best interval values and provides optimum weights related to each data point, providing solid insights for air temperature forecasting. 
Integration of DBSCAN Cluster Analysis with Multigroup Moderation Path Analysis Al Jauhar, Hafizh Syihabuddin; Solimun, Solimun; Fitriani, Rahma
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29847

Abstract

This study examines the application of integration between DBSCAN cluster analysis and multigroup moderation path analysis to analyse patterns of waste management behaviour in Batu City. DBSCAN was used to cluster the data based on density, resulting in two main clusters as well as some noise data. The first cluster consisted of 189 respondents, while the second cluster included 196 respondents, with the remaining 10 data identified as noise. The DBSCAN clustering results showed a silhouette index of 0.664, indicating good clustering quality in terms of compactness and separation between clusters. After the data was clustered, each cluster was analysed using multigroup moderation path analysis to assess the relationship between environmental quality, understanding of 3R-based waste management, and economic usefulness of waste with facilities and infrastructure variables as moderators. The results showed that clusters with good quality facilities had a stronger understanding of 3R-based waste management and its economic usefulness. This finding underscores the importance of facilities and infrastructure in influencing community waste management behaviour patterns.
Spatial Clustering with Autocorrelation-Based Weighting for Regional Socio-Economic Pattern Analysis: A Case Study of East Java Fitriani, Rahma; Sumarminingsih, Eni; Amaliana, Luthfatul
Journal of Multidisciplinary Applied Natural Science Vol. 6 No. 2 (2026): Journal of Multidisciplinary Applied Natural Science
Publisher : Pandawa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47352/jmans.2774-3047.345

Abstract

Clustering, an unsupervised machine learning technique, categorizes objects into groups based on shared characteristics. When applied to spatial data, the assumption of independence is often violated due to similarities among adjacent regions—a phenomenon known as spatial autocorrelation. To address this, spatial clustering incorporates both non-spatial attributes (e.g., socio-economic indicators) and spatial attributes (e.g., geographic location), with spatial attributes weighted based on their influence in defining clusters. In regional economic development, creating clusters that are both spatially coherent and socio-economically homogeneous is critical for effective policy design. Strong interactions among neighboring regions can promote more integrated and balanced growth. This study proposes a spatial clustering framework that optimizes spatial attribute weighting according to the degree of spatial autocorrelation. A simulation study using 2023 data from East Java’s 38 regencies/municipalities determines optimal weights under varying spatial dependence levels. The results show that optimal spatial weights increase with the number of clusters and vary according to the strength of spatial autocorrelation. Applied to East Java, the method produced clusters with higher socio-economic homogeneity than official zones, though with reduced spatial contiguity. These findings highlight the importance of adaptive, autocorrelation-aware clustering to improve regional planning and support more evidence-based development strategies.
Block Bootstrap for Spatiotemporal Data in Generalized Space Time Autoregressive (GSTAR) Sumarminingsih, Eni; Fitriani, Rahma; Darmanto; Maulana, Eka Dani; Aulia, Natasha; Ruszardi, Luzar Dwain
Science and Technology Indonesia Vol. 11 No. 2 (2026): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.2.701-731

Abstract

Generalized Space-Time Autoregressive is a model that can be used for data with spatial and temporal dependence. The GSTAR model is widely used in various phenomena such as rainfall, temperature, inflation, and others. GSTAR assumes normality of errors and non-autocorrelation. If the assumption of normality of errors is not met, then inference on parameters cannot be made. One solution to this problem is to use bootstrapping. However, bootstrapping for spatiotemporal data in the GSTAR model has not been developed. Therefore, this study aims to develop a bootstrapping method for spatiotemporal data in the GSTAR model. This development is done by adapting bootstrapping methods for time series data, namely, the non-overlapping block bootstrap (NBB) and the moving block bootstrap (MBB). This research continued with a series of simulations to evaluate the performance of the block bootstrap method as the number of observations, block length, and number of bootstrap replications were varied. Furthermore, the method’s effectiveness was tested using rainfall data from Malang Regency. Simulation results show that both resampling schemes satisfy the asymptotic condition, where the bias decreases monotonically with increasing sample size (T) and block length. MBB consistently produces lower bias than NBB due to its more intensive use of overlapping data, which effectively reduces boundary effects. Although inference on autoregressive parameters can be accurate, inference on spatial autoregressive parameters yields less satisfactory results, indicating the limitations of time blocks in capturing complex spatial dependencies. Increasing the number of replications above B=100 does not significantly improve the precision of the variance estimate, indicating computational efficiency at that threshold. The t-test results confirm that there is no statistically significant difference in performance between NBB and MBB. Nevertheless, MBB is more recommended for practical applications due to its higher information density and better estimation stability.
Co-Authors Abdila, Naufal Shela Agung Murti Nugroho Agus Dwi Sulistyono, Agus Dwi Akhmad Mansyur, Akhmad Al Jauhar, Hafizh Syihabuddin Alfi Fadliana Ani Budi Astuti Antonius Totok Priyadi Atiek Iriani, Atiek Atiek Iriany Aulia, Natasha Azizah, Amelia Nur Darmanto Darmanto Darmanto Dianiati, Aldila Nur Eddi Basuki Kurniawan Encep Supriatna Eni Sumarminingsih Fernandes, Adji Achmad Rinaldo Firdausi, Rizka Firsa, Pocut Zahran Nada Gusganda Suria Manda Handoyo, Samingun Hapsari, Ulfalina Henny Pramoedyo Herman Cahyo Diartho Hermanto, Tutut Istiqomah, Nur Jaka Pratama Musashi Jannah, Friendtika Miftaqul Jannah, Friendtika Miftaqul Korniasari, Leli Dwi Kusdarwati, Heni La Onu, La Ola Lestari, Kartika Ayu Liduina Asih Primandari, Liduina Asih Loekito Adi Soehono Luthfatul Amaliana, Luthfatul Maharani, Kasih Mahendra, Di Aidil Maulana, Eka Dani Menufandu, Dahlia Gladiola Rurina Mitakda, Maria Bernadetha Ni Wayan Surya Wardhani Nofriadi Nofriadi Nugroho, Salma Fitri Nur Aisyah Nurachmad Sujudwijono Pasca, Paunfia Meiditha pramoedyo, henny Pratama, Muhamad Liswansyah Pribadi, Teddy Ramadhan, Apry Zakaria Ramifidisoa, Lucius Risfandi Ruszardi, Luzar Dwain Ry, Mohd Dzaky Sari , Imelda Sarini Yusuf Septya Hadiningrum Sesilia Seli Sholihah, Suma Suci Solimun, Solimun Suci Astutik Sumarminingsih, Eni Sundyni, Reza Chyta Syukrilla, Wara Alfa Tampubolon, Risma Hartati Tri Pratiwi, Elly Andita Umami, Asri Rizza Vierkury Metyopandi Vivit Senja, Dinda Rinai Waego Hadi Nugroho Widya Reza Wigbertus Ngabu Wilandari , Angestika Yusrina Nur Dianati Zakaria Zamelina, Armando Jacquis Federal