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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jambura Journal of Mathematics Journal of Research on Community Engagement
Choiruddin, Achmad
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Parameter Estimation and Hypothesis Testing of GTW Compound Correlated Bivariate Poisson Regression Model: A Theoretical Development Hargandi, Priyanka Ratulangi; Purhadi, Purhadi; Choiruddin, Achmad
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.32712

Abstract

Each observation location and time possesses distinct characteristics, reflecting heterogeneity at every observation point, both spatially and temporally. This condition renders the Compound Correlated Bivariate Poisson Regression (CCBPR) model inadequate for representing data dynamics that exhibit spatial and temporal heterogeneity. To address this limitation, the Geographically and Temporally Weighted Compound Correlated Bivariate Poisson Regression (GTWCCBPR) model is employed, which allows parameter variation across locations and time periods. This model also incorporates the exposure variable as a weighting factor to adjust for differences in risk across observational units. This study aims to estimate the parameters of the GTWCCBPR model using the Maximum Likelihood Estimation (MLE) approach. Due to the complex structure of the model, the log-likelihood function does not yield a closed-form solution. Therefore, parameter estimation is performed using the iterative Berndt-Hall-Hall-Hausman (BHHH) algorithm. Subsequently, hypothesis testing is conducted to evaluate the parameter similarity between the global model (CCBPR) and the spatiotemporal model (GTWCCBPR), as well as to assess the significance of each predictor variable. Simultaneous testing is carried out using the Maximum Likelihood Ratio Test (MLRT), while partial testing is conducted using the Z-test. The scope of this study is limited to theoretical formulation and methodological development, without empirical or simulation-based validation. Future research may extend this work by applying the GTWCCBPR model to practical datasets exhibiting spatio-temporal heterogeneity, particularly in areas such as public health (e.g., maternal and postneonatal mortality), epidemiology, or regional planning.
SEM-PLS Training at Universitas Islam Negeri Maulana Malik Ibrahim Otok, Bambang Widjanarko; Astuti, Cindy Cahyaning; Mulyanto, Angga Dwi; Purhadi, Purhadi; Andari, Shofi; Choiruddin, Achmad; Purnami, Santi Wulan
JRCE (Journal of Research on Community Engagement) Vol 7, No 1 (2025): Journal of Research on Community Engagement
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrce.v7i1.32959

Abstract

At Universitas Islam Negeri Maulana Malik Ibrahim Malang in 2024 SEM-PLS training will develop data analysis capabilities for lecturers and students to enhance their work on quality scientific publications. The Department of Mathematics at Faculty of Science and Technology conducted the session on May 21, 2024, where 40 people participated. Training and mentoring stands as the service method which features instruction about SEM-PLS theory alongside practical utilization of SmartPLS software for implementation. Observation activities together with documentation assessment and satisfaction questionnaire responses determine the program's outcome. Participant satisfaction reached an exceptional level because they showed positive feedback about the material presented. Time constraints together with a constrained space area negatively affected  this event. This training achieved success in providing extensive SEM-PLS understanding to students and lecturers. The activity builds campus research capacity. The organization of similar consecutive training courses is highly suggested because it will boost academic knowledge in data analysis fields.
Generalized Poisson Spatial Autoregressive Modeling of Pneumonia Cases in Children Under Five in Tuban Sihombing, Joshua Capri Gunawan; Sutikno, Sutikno; Choiruddin, Achmad
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.v10i2.37288

Abstract

The number of pneumonia cases in children under five in Tuban Regency presents two significant data challenges, namely, overdispersion and spatial dependency. This study aims to develop and apply the Generalized Poisson Spatial Autoregressive (GPSAR) model to address both issues simultaneously. The model was estimated using a MLE-BHHH procedure and validated using 10-fold cross-validation (CV). The results confirm the model's validity and superiority. The GPSAR model outperformed the non-spatial GPR model in terms of goodness-of-fit (AIC: 1301.09 vs. 1312.67) and predictive accuracy (Out-of-Sample CV-RMSE: 8.451 vs. 8.716). Statistically, the structural parameters for spatial lag and overdispersion were highly significant. Two predictor variables, exclusive breastfeeding and complete basic immunization, were also found to be statistically significant factors. This research provides a robust regression framework for spatial count data exhibiting overdispersion and offers new insights into pneumonia case determinants in the region.
Neural Networks-Based Forecasting Platform for EV Battery Commodity Price Prediction Togatorop, Andrew Reinhard Marulak; Bahari, Annisa Indira Larashati; Choiruddin, Achmad
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 2 (2023): August 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i2.19999

Abstract

This study explores the impact of green energy-based economies on the growing use of electric vehicle (EV) batteries in transportation and electronic devices. Despite the environmental benefits, concerns have emerged regarding the supply, pricing, and volatility of raw materials used in battery manufacturing, exacerbated by geopolitical events such as the Russian-Ukrainian war. Given the high uncertainty surrounding EV commodity materials, this research aims to develop forecasting tools for predicting the prices of essential lithium-based EV battery commodities, including Lithium, Cobalt, Nickel, Aluminum, and Copper. The study builds on previous research on commodity price forecasting. Using Neural Networks such as LSTM that run using analytics platforms like RapidMiner, a robust and accurate models is able to be produced while require little to no programming ability. This will solve the needs to produce advanced predictions models for making decisions. As the results from the research, the models that are produced are successful in generating good prediction models, in terms of RMSE of 0,03 – 0,09 and relative errors of 4-14%.
Co-Authors Angga Dwi Mulyanto Bahari, Annisa Indira Larashati Bambang Widjanarko Otok Cindy Cahyaning Astuti Hargandi, Priyanka Ratulangi Purhadi Purhadi Santi Wulan Purnami Shofi Andari Sihombing, Joshua Capri Gunawan Sutikno Sutikno Togatorop, Andrew Reinhard Marulak