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All Journal Indonesian Journal of Geography J@TI (TEKNIK INDUSTRI) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Optimasi Sistem Industri PSYMPATHIC BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Aplikasi Statistika & Komputasi Statistik Komunikologi: Jurnal Pengembangan Ilmu Komunikasi dan Sosial Journal Psikogenesis Jurnal Statistika dan Matematika (Statmat) Jurnal Riset Statistika Bandung Conference Series: Statistics Schema: Journal of Psychological Research STATISTIKA Parameter: Jurnal Matematika, Statistika dan Terapannya
Dwi Agustin Nuriani Sirodj
Program Studi Statistika, FMIPA, Universitas Islam Bandung
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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MODELING THE INCIDENCE OF MALNUTRITION IN BOGOR REGENCY USING ZERO-INFLATED NEGATIVE BINOMIAL MIXED EFFECT MODEL Sirodj, Dwi Agustin Nuriani; Sadik, Kusman; Kurnia, Anang
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/barekengvol18iss2pp0961-0972

Abstract

Modeling response variables in the form of count data generally uses a model based on the Poisson distribution. However, some conditions, such as the presence of excess zero, can be found in the data that result in overdispersion, which will have an impact on the resulting variance in the model. In this paper, three approaches, namely the Poisson Mixed Model, the Negative Binomial (NB) Mixed Model, and the Zero-Inflated Negative Binomial (ZINB) Mixed Model, are used to model the incidence of malnutrition in Bogor Regency. The data used in this study are secondary data sourced from the West Java open data website. Based on the results of data analysis, it appears that the ZINB Mixed Model method is a method capable of accommodating random effects, overdispersion, and excess zero in modeling malnutrition in Bogor Regency. Variables that significantly affect the occurrence of malnutrition cases in villages in Bogor Regency include the Number of Children Weighed Routinely Every Month, Number of Children Measured for Length and Height Twice a Year, Number of Children under 12 Months Old Who Received Complete Basic Immunization, Number of Posyandu (Integrated Health Post), and Number of Parents/Caregivers Participating in Monthly Parenting (PAUD).
Identification of Earthquake Prone Zones in Sumatra using Density Based Spatial Clustering of Applications with Noise Sirodj, Dwi Agustin Nuriani; Aidi, Muhammad Nur; Sartono, Bagus; Syafitri, Utami Dyah; Pranata, Bayu
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.v10i2.36120

Abstract

This study investigates the spatial distribution of earthquakes in Sumatra using the DBSCAN clustering algorithm applied to seismic data spanning 1 January 2000 to 31 December 2023. The analysis identified two distinct seismic clusters: one in the northern region (Aceh and North Sumatra) and another in the southern region (Lampung, Bengkulu, and West Sumatra), while several events in central areas were classified as noise. Cluster validity assessment confirmed that the identified groups are compact and well separated, reflecting meaningful seismotectonic segmentation. Statistical testing further revealed significant differences in earthquake depth and magnitude between the clusters, supporting the robustness of the findings. Notably, the southern cluster corresponds to the Mentawai Fault system, whereas the northern cluster aligns with the subduction zone and the Sumatran Fault. DBSCAN proved particularly effective in this context as it can capture clusters of arbitrary shapes, consistent with the complex geological structures governing seismicity in Sumatra.
Perbandingan Kerja Binomial GLMM Tree dan BIMM Forest untuk Memodelkan Status Bekerja Penduduk Sirodj, Dwi Agustin Nuriani; Notodiputro, Khairil Anwar; Sartono, Bagus
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117531

Abstract

Model prediksi berbasis pada pohon keputusan saat ini banyak dikembangkan di berbagai bidang. Pengembangan metode yang dilakukan diantaranya memasukkan pengaruh acak ke dalam model.  Generalized linier mixed model (GLMM) Tree menjadi salah satu model yang dapat mengakomodasi adanya pengaruh acak dan dilakukan dengan metode partisi rekursif hanya saja waktu komputasi yang dibutuhkan relatif lebih lama. Selanjutnya metode alternatif lainnya adalah Binary Mixed Model (BiMM) Forest yang menggabungkan prinsip kerja Bayesian GLMM dan Random Forest. Dari kedua metode yang akan digunakan maka permasalahan yang dihadapi adalah bagaimana kinerja dari metode GLMM Tree dan BiMM Forest jika diterapkan untuk klasifikasi status bekerja penduduk di Kabupaten Bogor dan Kabupaten Pangandaran. Dari hasil analisis tampak bahwa metode BiMM Forest memiliki kinerja yang lebih baik di bandingkan dengan GLMM Tree untuk kedua daerah. Selain itu ditunjukkan pula bahwa peubah yang penting dalam proses klasifikasi status bekerja penduduk di Kabupaten Bogor dan Kabupaten Pangandaran adalah peubah terkait aspek pendidikan, sosial, dan ekonomi.
Recycling Solid Waste of Coconut Oil Industry: A Response Surface-Goal Programming Approach Dwi Agustin Nuriani Sirodj; Ivan Gunawan
Jurnal Optimasi Sistem Industri Vol. 19 No. 2 (2020): Published in October 2020
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/josi.v19.n2.p111-121.2020

Abstract

Waste minimization is a key success factor to achieve sustainability, including in the edible oil industry. One type of solid waste produced substantially in this industry is spent bleaching earth (SBE), which comes from a mixture of bleaching earth (BE) and activated carbon (CA) used in the edible oil refining process. SBE that is recycled through a heating process is known as heat regenerated SBE (HRSBE). The process is influenced by two factors as stated in previous studies: temperature and time. In the current study, we report the results of experiments to find the optimal combination of temperature and time in restoring the absorbent quality of SBE by observing four quality parameters: colors (red and yellow), Free Fatty Acid (FFA), and Peroxide Value (PV). The current study uses Response Surface Methodology (RSM) to design the experiments and to find the equations of the relationship between the factors for each response; as well as Goal Programming (GP) to find the most optimal combination of factors in order to achieve aggregated quality targets. The findings show that 3.414 hours of heating at a temperature of 151.64oC allow HRSBE to produce coconut oil in accordance with the expected quality targets.
Spatial Interpolation of the Probability of Mercury Threshold Exceedance Using Indicator Kriging Zhahirulhaq, Mufdhil Afta; Sirodj, Dwi Agustin Nuriani
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp395-408

Abstract

Indicator Kriging (IK) is a spatial interpolation method used to estimate the probability that a variable exceeds a specified threshold. This study applies IK to assess the probability of mercury (Hg) concentrations exceeding environmental thresholds in river systems across DKI Jakarta. Given the skewed and non-normally distributed nature of mercury data, IK was selected due to its robustness in handling non-parametric data and its sensitivity to extreme values. Mercury concentration measurements were first transformed into binary indicator data based on a predefined threshold. An experimental semivariogram was then computed to analyze the spatial dependence of the indicator values, followed by the fitting of theoretical semivariogram models (Gaussian, Spherical, and Exponential). The best-fitting model was selected using the Leave-One-Out Cross-Validation (LOOCV) approach, with the Spherical model yielding the lowest root mean square error (RMSE). The final probability map generated through IK reveals five unsampled locations with a probability greater than 0.5 of mercury concentration exceeding the threshold: two located along the Ciliwung River and three along the Sunter River. These findings highlight critical zones requiring monitoring and support the use of IK as an effective geostatistical tool for environmental risk assessment of heavy metal contamination in urban river systems.
Geostatistical Co-Kriging Approach for Estimating Total Coliform Bacteria in the Rivers of DKI Jakarta Salsabila, Salwa; Sirodj, Dwi Agustin Nuriani
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34391

Abstract

Spatial statistics and geostatistics are essential for analyzing spatially distributed data, particularly in environmental studies where data gaps are prevalent. However, limited studies have applied multivariate geostatistical approaches, particularly Co-Kriging (CK), to assess microbial contamination in tropical urban river systems, where pollution patterns are highly variable and data gaps are frequent. This study employs CK, a multivariate geostatistical interpolation technique, to estimate Total Coliform Bacteria concentrations in the rivers of DKI Jakarta, Indonesia. Total Coliform Bacteria served as the primary variable, with Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) incorporated as secondary variables. A total of 120 sampling points were analyzed, with data collected by Dinas Lingkungan Hidup DKI Jakarta during the second monitoring period in June 2022. Semivariogram modelling identified the Gaussian model as the best fit, yielding the lowest root mean square error (RMSE) of 11.468, which performed better than both the Spherical and Exponential models. Model performance was further evaluated through Leave-One-Out Cross-Validation (LOOCV), in which one data point was systematically removed and re-estimated in multiple iterations to calculate the residuals and assess model accuracy. The CK analysis was performed using RStudio software. CK predictions closely matched observed concentrations, demonstrating strong model performance. At unsampled locations, the estimated mean Total Coliform Bacteria concentration was 7.711 × 10⁶ MPN/100 ml with a standard deviation of 4.406 × 10⁶ MPN/100 ml. The high variance indicates substantial spatial heterogeneity, likely driven by data outliers, weak spatial autocorrelation in COD, and low correlations between Total Coliform–COD and BOD–COD pairs. These findings highlight the potential of geostatistical CK to provide reliable spatial predictions of microbial contamination in urban river systems, thereby supporting evidence-based water quality monitoring and management in densely populated regions. The insights generated in this study can help environmental authorities in DKI Jakarta optimize monitoring strategies, prioritize pollution hotspot interventions, and strengthen urban river health management to protect public health and guide sustainable urban water governance.
Integrating Support Vector Regression and Kriging in Spatial Interpolation of Statistical Seismicity Parameters Sirodj, Dwi Agustin Nuriani; Aidi, Muhammad Nur; Sartono, Bagus; Syafitri, Utami Dyah; Pranata, Bayu
Indonesian Journal of Geography Vol 57, No 3 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.102153

Abstract

Spatial interpolation methods, such as Inverse Distance Weighting (IDW) and kriging, are commonly used in various fields. In Kriging method, semivariogram fitting is an important step, where empirical data are used to derive a theoretical model. However, when the known theoretical semivariogram model does not provide a satisfactory fit, the bias in the estimated values is increased. To address this limitation, Support Vector Regression (SVR) can be used to model the empirical semivariogram with a machine-learning method. This method has been applied in ordinary kriging interpolation for semivariogram fitting to estimate parameters related to the potential occurrence of earthquake. Specifically, the calculated parameters, based on the Gutenberg-Richter law, include the seismic activity (a-value) and rock fragility (b-value) in the Sumatera region. The results showed that SVR can model the empirical semivariogram better than the theoretical. The integration of SVR-Ordinary Kriging provides the best performance compared to other methods, such as IDW, with the smallest RMSEP values for both the b-value and a-value measuring 0.1378 and 0.7423, respectively. Aceh and Mentawai Islands tend to show low a and b values, suggesting that these areas are more vulnerable to earthquake with large magnitudes.
Co-Authors Ainun Hayat Damayanti Alfiari Firdaus Anang Kurnia Andhita Nurul Khasanah Anwar Musadad Rosidin Bagus Sartono Bayu Pranata, Bayu Dian Trihastuti Gadama, Melsan I Made Sumertajaya Ig. Jaka Mulyana Ihsana Sabriani Borualogo Ivan Gunawan Kusman Sadik Miki Amrilya Wardati Moh. Wigi Destriansyah Muhamad Nurhikmat Zain Muhamad Nurhikmat Zain Muhammad Nur Aidi Nadia Ruhul Mujahadah Noor, Farras Rachmanisa Permana, Rizka Hadian Rizka Hadian Permana Salsabila, Salwa Sulisworo Kusdiyati Sumaryati, Indri Utami Susandari Susandari Temi Damayanti Djamhoer Tiara Suci Lestari Utami Dyah Syafitri Vici Sofianna Putera Yehezkiel Prakoso Putra Yunen Yuli Aslamawati, Yuli Zhahirulhaq, Mufdhil Afta