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All Journal Journal of Innovation and Applied Technology JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pertanian Agros ComTech: Computer, Mathematics and Engineering Applications MATHunesa: Jurnal Ilmiah Matematika Pakem : Jurnal Pengabdian Kepada Masyarakat ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Wigbertus Ngabu
Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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ANALISIS FAKTOR YANG MEMENGARUHI PRODUKTIVITAS JAGUNG DI DESA ANAENGGE KECAMATAN KODI KABUPATEN SUMBA BARAT DAYA Wigbertus Ngabu; Atiek Iriany
Agros Journal of Agriculture Science Vol 25, No 1 (2023): edisi JANUARI
Publisher : Fakultas Pertanian, Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37159/jpa.v25i1.2471

Abstract

Komoditas jagung merupakan bahan makanan utama kedua setelah beras. Selain itu, jagung juga digunakan sebagai bahan pakan ternak dan bahan baku industri. Jika pemenuhan bahan pakan terganggu, maka pada akhirnya akan mengganggu pemenuhan kebutuhan protein dan peningkatan gizi bagi masyarakat. Oleh karena itu, jagung dipandang sebagai komoditas yang cukup strategis seperti halnya beras. Jagung merupakan salah satu sumber karbohidrat yang cukup potensial terutama di Indonesia Timur. Selain sebagai sumber bahan pangan, jagung juga menjanjikan banyak harapan untuk dijadikan sebagai bahan baku berbagai macam keperluan industri. Salah satu daerah potensial untuk produksi jagung adalah Kabupaten Sumba Barat daya yang memiliki banyak lahan marjinal (lahan kering). Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi produktivitas Jagung di Kabupaten Sumba Barat Daya dengan benih unggul Brawijaya Nusa 01. Metode yang digunakan yaitu regresi linear berganda, dan diproleh hasil variabel yang signifikan berpengaruh terhadap produktivitas jagung di desa Anaengge Kabupaten Sumba Barat Daya Luas Lahan, Benih, NPK, Urea, Pestisida. Diproleh hasil R2 sebesar 93.26%, hasil ini menujukan variabel produktivitas jagung
Penerapan Aplikasi Sistem Administrasi Desa Untuk Mendukung Tata Kelolah Pemerintah Desa Di Desa Mulyoagung Kecamatan Dau Atiek Iriany; Wigbertus Ngabu; Solimun Solimun; Achmad Efendi; Danang Ariyanto; Arditama Putra Rochmanullah
Journal of Innovation and Applied Technology Vol 9, No 1 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2023.009.01.8

Abstract

Mulyoagung Village is a village in the Highlands which is located in Dau District, Malang Regency. The development of a tourist village requires a database and information that must be structured and systematic. One of the data and arrangement of the village administration system is related to the potential of the village or village services. Through situation analysis, this service activity tries to map the potential of the village, especially in village services. Development of village administration system applications as a tool for village officials in serving the community. The application of the village administration system application for the village of Mulyoagung is expected to be a means of providing excellent service to the community that is carried out more quickly, precisely, and accurately.  Based on the survey results, the village government is very satisfied with the village information system that has been created. 
Spatial Modeling of Fixed Effect and Random Effect with Fast Double Bootstrap Approach Wigbertus Ngabu; Henny Pramoedyo; Rahma Fitriani; Ani Budi Astuti
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 1 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i1.8033

Abstract

The use of panel data on spatial regression has many advantages. However, testing the spatial dependency and parameter presumption generated in spatial regression of panel data becomes inaccurate when applied to regions with large numbers of small spatial units. One method of overcoming problems of small spatial unit sizes is the bootstrap method. The research aimed to combine cross-section and time-series panel data. The analysis was performed to extract information based on observations modified by the influences of space or location, known as spatial analysis of panels. The influence of location effects on spatial analysis was presented in the form of weighting. The research applied the Fast Double Bootstrap (FDB) method by modeling poverty rates on Flores Island. The results of the Hausman test show the right model, which is a random effect. Meanwhile, spatial dependency testing concludes spatial dependence and poverty modeling in Flores Island, which is more likely to be the Spatial Autoregressive Random (SAR) model. SAR random effect in modeling value has R2 of 77,38% and does not meet the normality assumption. SAR effect in modeling the FDB approach can explain the diversity of poverty rate in the Flores Island with 88,64% and meets residual normality assumptions. The analysis with the FDB approach on spatial panels shows better results than the common spatial panels.
PENAMBAHAN METODE NEURAL NETWORK DALAM PEMODELAN GSTAR-SUR UNTUK MENGATASI KASUS NON LINIER PADA PERAMALAN DATA CURAH HUJAN Atiek Iriany; Adji Achmad Rinaldo Fernandes; Achmad Efendi; Henida Ratna Ayu Putri; Danang Ariyanto; Wigbertus Ngabu
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p226-236

Abstract

Salah satu model peramalan yang dapat yang menggabungkan unsur spasial (spatial) dan temporal (time) adalah Generalized Space Time Autoregressive (GSTAR). Pendugaan parameter yang digunakan adalah Seemingly Unrelated Regression (SUR). Peramalan iklim pada tanaman hortikultura pada masa kini sulit untuk diprediksi karena memiliki pola dan karakteristik yang sulit diidentifikasi dan dapat disebut aktivitas non linier. Unsur non linier ini dapat ditangkap oleh metode neural network. Penelitian ini ingin mengetahui hasil peramalan curah hujan pada 6 wilayah di Tengger menggunakan model GSTAR dengan pendugaan parameter menggunakan metode SUR dan digabungkan dengan neural network agar hasil peramalan yang lebih akurat. Data yang digunakan dalam penelitian ini adalah data curah hujan enam lokasi di wilayah Tengger, yakni Ngadirejo, Puspo, Wonokitri, Argosari, Ngadas, dan Wonokerto. Model yang tepat dalam melakukan peramalan pada data curah hujan pada 6 lokasi Tengger adalah model GSTAR (1,2,3,4,5,6,7,36(1)) Backpropagation Neural Network (96-120-6).
Location Based Stunting Modeling Using Geographically Weighted Panel Regression in Blitar Regency Pramoedyo, Henny; Ngabu, Wigbertus; Iriany, Atiek
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 21 No. 2 (2024)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2024.v21.i2.17446

Abstract

Stunting remains a significant public health issue in Blitar Regency, Indonesia, particularly in rural areas where chronic malnutrition and inadequate access to healthcare services persist as major challenges. This study aims to explore the spatial and temporal factors influencing stunting using the Geographically Weighted Panel Regression (GWPR) method. By integrating cross-sectional and time-series data from 2021 to 2023, the study evaluates various factors, including the stunting prevalence rate and independent variables such as maternal education level, per capita income, the number of postpartum mothers receiving Vitamin A supplements, immunization coverage, and the availability of healthcare personnel. The findings reveal that stunting prevalence is significantly influenced by location-specific variables, with healthcare access and nutrition being dominant factors in rural areas, while economic conditions exert a greater influence in urban areas. The GWPR model provides deeper insights into spatial heterogeneity and offers valuable guidance for designing targeted and region-specific policies to reduce stunting rates in Blitar Regency. The results indicate that the R-Square value of the GWPR model is 0.9123, meaning that 91.23% of the stunting prevalence in Blitar Regency can be explained by the independent variables in this study
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%.
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.
NON HIERARCHICAL K-MEANS ANALYSIS TO CLUSTERING PRIORITY DISTRIBUTION OF FUEL SUBSIDIES IN INDONESIA Astuti, Ani Budi; Guci, Abdi Negara; Alim, Viky Iqbal Azizul; Azizah, Laila Nur; Putri, Meirida Karisma; Ngabu, Wigbertus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1663-1672

Abstract

The growth rate of inflation in Indonesia continues to increase from day to day. The inflation rate in Indonesia reached 1.17% in September 2022 which is the highest inflation rate in the last seven years. One of the causes of high inflation is caused by the increasing demand for motor vehicle fuel. Therefore, there is a need for appropriate action from the government in determining related policies. K-Means multivariate cluster analysis is a non-hierarchical cluster method that is popularly used, one of which is used in Machine Learning algorithms, especially Unsupervised Learning. The purpose of this research is to clustering that are priority distribution of subsidies in Indonesia based on the characteristics formed. The data in this study consist of the percentage of poverty, the percentage of total transportation, the percentage of transportation use, and the percentage of area. Data were analyzed using multivariate cluster analysis with the K-Means method. Based on the research results, information was obtained that the data fulfilled a representative sample with value of KMO >50%. In addition, there are 4 optimal clusters which are the results of the calculation of the Elbow and Silhoutte methods, so 4 provincial clusters are formed with their respective characteristics. Cluster 1 is a province that is highly prioritized to receive fuel subsidies, Cluster 2 is a province that is not highly prioritized for fuel subsidies, Cluster 3 is a province that is prioritized to receive fuel subsidies, and Cluster 4 is a province that is not prioritized to receive fuel subsidies.
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.
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 Achmad Efendi Achmad Efendi Adji Achmad Rinaldo Fernandes Agung Sugeng Widodo Agus Budiarto Akbar Pamungkas, Ilham Alim, Viky Iqbal Azizul Ani Budi Astuti Ani Budi Astuti Arditama Putra Rochmanullah Arianto, Danang Arifin Noor Sugiharto Atiek Iriany Atikah, Husna Azizah, Laila Nur Chairunissa, Abela Danang Ariyanto Danang Ariyanto Doppy Roy Nendissa Guci, Abdi Negara Henida Ratna Ayu Putri Henny Pramoedyo Henny Pramoedyo Kartikaningrum, Widhistya Khoiril Anam, Khoiril Maimau, Melianus Muhamad Ridwan Ni Wayan Surya Wardhani Nur Hidayat Pamungkas, Ilham Akbar Pratama, Muhammad Fhikri Yudha Putra, Arditama Putri, Meirida Karisma Rahma Fitriani Rahma Fitriani Riza, Sativandi Solimun Solimun Susinggih Wijana Susinggih Wijana Yudo Tyasmoro, Setyono