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All Journal Media Statistika The Journal of Experimental Life Sciences (JELS) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Mutiara Medika: Jurnal Kedokteran dan Kesehatan Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) ComTech: Computer, Mathematics and Engineering Applications Industri Inovatif : Jurnal Teknik Industri Pakem : Jurnal Pengabdian Kepada Masyarakat
Henny Pramoedyo
Prodi Statistika Jurusan Matematika FMIPA Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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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COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA Pramoedyo, Henny; Ariyanto, Danang; Aini, Novi Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (591.915 KB) | DOI: 10.30598/barekengvol16iss4pp1411-1422

Abstract

Soil texture is used to determine airflow, heat, instability, water holding capacity, and the shape and structure of the soil structure. Soil texture as an important attribute that determines the direction of soil management must be modeled accurately. However, soil texture is a soil attribute that is quite difficult to model. It is a compositional data set that describes the particle size of the soil mineral fraction (sand, silt, and clay). The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). The purpose of this study was to classify the distribution of soil texture using the Random Forest and Naïve Bayes methods to obtain the most accurate grouping results. This research was conducted in the area around Kalikonto River Basin, East Java Province. The performance-based tests show that the RF algorithm provides higher accuracy in predicting soil texture based on the Digital Elevation Model (DEM). The results of RF’s performance testing on training data and testing data gave an accuracy value of 92.55% and 87.5%. Classification using the Naïve Bayes method produces an accuracy value of 89.98% on testing data and 80.65% accuracy on training data.
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.
THE UNINFORMATIVE PRIOR OF JEFFREYS’ DISTRIBUTION IN BAYESIAN GEOGRAPHICALLY WEIGHTED REGRESSION Faisal, Fachri; Pramoedyo, Henny; Astutik, Suci; Efendi, Achmad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1229-1236

Abstract

When using the Bayesian method for estimating parameters in a geographically weighted regression model, the choice of the prior distribution directly impacts the posterior distribution. The distribution known as the Jeffreys prior is an uninformative type of prior distribution and is invariant to reparameterization. In cases where information about the parameter is not available, the Jeffreys' prior is utilized. The data was fitted with an uninformative Jeffreys' prior distribution, which yielded a posterior distribution that was utilized for estimating parameters. This study aims to derive the prior and marginal posterior distributions of the Jeffreys' and in Bayesian geographically weighted regression (BGWR). The marginal posterior distributions of and can be obtained by integrating the other parameters of a common posterior distribution. Based on the results and discussion, the Jeffreys prior in BGWR with the likelihood function is . On the other hand, the marginal posterior distribution of follows a normal multivariate distribution, that is, , while the marginal posterior distribution of follows an inverse gamma distribution, that is, . As further research, it is necessary to follow up on several limitations of the results of this research, namely numerical simulations and application to a particular case that related to the results of the analytical studies that we have carried out.
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.
PENDAMPINGAN PENATAAN SISTEM ADMINISTRASI DESA DENGAN MENGEMBANGKAN APLIKASI ADMINISTRASI DESA TERPADU DI KELURAHAN ARJOSARI Pramoedyo, Henny; Ngabu, Wigbertus; Wardhani, Ni Wayan Surya; Iriany, Atiek; Chairunissa, Abela
PAKEM : Jurnal Pengabdian Kepada Masyarakat Vol 5 No 2 (2025): Pakem : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pakem.5.2.131-142

Abstract

This community service activity aims to assist Kelurahan Arjosari in restructuring its administrative system more effectively and efficiently through the development of a village administration application built with Microsoft Excel and Visual Basic for Applications (VBA). Based on initial observations, the administrative processes in the kelurahan were still conducted manually, leading to slow public service delivery and a high risk of errors. The developed application is designed to integrate with the village's population database, automate the generation of documents based on National Identification Numbers (NIK), and provide automatic data validation features. Through Focus Group Discussions (FGD) and the official launching of the application, local officials were actively involved in the planning and training phases. Evaluation results show that the application is easy to use, accelerates the document service process, and improves administrative data accuracy. This initiative has had a positive impact on the quality of public services at the local level and serves as a model for applying simple but effective technology to support digital transformation in village governance
Transformasi Kota Cerdas dalam Mitigasi Banjir: Pemodelan Curah Hujan DKI Jakarta dengan Pendekatan Spatial Vector Autoregressive (SpVAR) dan Pemetaan Bobot Queen Contiguity Melanwati, Rinda Lolita; Sumarminingsih, Eni; Pramoedyo, Henny
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107537

Abstract

Perubahan iklim dan cuaca ekstrem menjadi tantangan global, termasuk di Indonesia, dengan peningkatan banjir di DKI Jakarta. Penanggulangan membutuhkan peramalan curah hujan yang akurat. Model VAR digunakan untuk memahami hubungan variabel cuaca. Namun, data deret waktu sering memiliki dimensi spasial. Oleh karena itu, dikembangkan model Spatial Vector Autoregressive (SpVAR) yang mempertimbangkan dimensi spasial dan waktu. Pembobot queen contiguity digunakan untuk representasi yang lebih akurat. Penelitian ini memanfaatkan data BPS DKI Jakarta dari Januari 2017 hingga Desember 2021. Hasilnya menunjukkan pengaruh spasial dalam model SpVAR (1,3) dengan bobot queen contiguity. Curah hujan, suhu, dan kelembaban udara saling mempengaruhi di wilayah diprediksi dan lainnya. Model ini penting dalam strategi mitigasi banjir dan kebijakan kota cerdas untuk mengurangi risiko banjir di DKI Jakarta.   Abstract Climate change and extreme weather pose global challenges, including in Indonesia, leading to increased floods in DKI Jakarta. Addressing this requires accurate rainfall forecasts. The VAR model is used to understand the relationships between weather variables. However, time series data often have spatial dimensions. Therefore, a Spatial Vector Autoregressive (SpVAR) model has been developed considering both spatial and temporal dimensions. Queen contiguity weighting is used for more accurate representation. This study utilizes BPS DKI Jakarta data from January 2017 to December 2021. The results show spatial influence in the SpVAR (1,3) model with queen contiguity weighting. Rainfall, temperature, and humidity mutually influence predicted and other areas. This model is crucial for flood mitigation strategies and smart city policies to reduce flood risks in DKI Jakarta.
Geographically and Temporally Weighted Regression Modeling in Analyzing Factors Affecting the Spread of Dengue Fever in Malang Indrayani, Fahmi; Pramoedyo, Henny; Iriany, Atiek
The Journal of Experimental Life Science Vol. 8 No. 2 (2018)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1175.267 KB) | DOI: 10.21776/ub.jels.2018.008.02.01

Abstract

Geographically and Temporally weighted regression (GTWR) modeling has been developed to evaluate spatial heterogeneity and temporal heterogeneity in factors influencing the spread of dengue fever in Malang city. By using the monthly data in 2012-2015 as the temporal unit of each urban village in Malang and village is considered as a spatial unit. GTWR model is compared with the GWR model using several statistical criteria. GTWR model shows that the relationship between dengue incidence with population density and monthly average temperature significantly affects each Village in Malang.Keywords : DHF, GTWR, Spatiotemporal Pattern
Co-Authors Achmad Efendi Alfi Fadliana Amelia, Okky Ariska Ani Budi Astuti Ani Budi Astuti Ardianti, Deby Arif, Nur Fitriyah Atiek Iriany Chairunissa, Abela Danang Ariyanto Eni Sumarminingsih Fachri Faisal Fernandes, Adji Achmad Handoyo, Samingun Heni Kusdarwati Indrayani, Fahmi Jaka Pratama Musashi Jannah, Friendtika Miftaqul Khoirunnisa, Elsa Azizah Korniasari, Leli Dwi Kurniawan, Galih Widhi Kusdarwati, Heni Liduina Asih Primandari, Liduina Asih Loekito Adi Soehono Luthfatul Amaliana, Luthfatul Maghfiroh, Aulia Maharani, Kasih Megasari, Dannys Melanwati, Rinda Lolita Mitakda, Maria Bernadetha Mudjiono Mudjiono, Mudjiono Ni Wayan Surya Wardhani Ni Wayan Surya Wardhani Novi Nur Aini, Novi Nur Nur Azizah Rahma Fitriani Ramadhan, Apry Zakaria Ramadhan, Rajuliaddin Ridwan, Yusrina Layli Riza, Sativandi Rizkiyani, Cirairina Sa’adah, Zumrotus Saniyawati, Fang You Dwi Ayu Shalu Sariyya, Hanah Rista Septiani, Kurniawati Setiarini, An Nisa Dwi Shara, Yoeniarti Siswahyudianto Suci Astutik Sumarminingsih, Eni Widya Reza Wigbertus Ngabu Yanuari, Anindita Yuli Triana, Eka Putri