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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Indo-MathEdu Intellectuals Journal
Arisanti, Restu
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Extreme Gradent Boosting Method Forecasting Rainfall in Lembang District, West Java Province Putri, Salma Azzahra; Darmawan , Gumgum; Arisanti, Restu; Clarissa Clorinda, Chrysentia
Indo-MathEdu Intellectuals Journal Vol. 4 No. 3 (2023): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v4i3.452

Abstract

Lembang is a notable regional tourism destination that bears considerable significance within the urban area of Bandung. Lembang is widely recognized for its flourishing agricultural sector, which supports a significant community of farmers engaged in the cultivation of fruits, vegetables, and ornamental plants, in addition to its intrinsic scenic beauty. Therefore, the acquisition of precipitation data is of considerable significance for individuals live in the area to maintain their economic endeavors. This study employs daily historical data from the period of 2018 to 2021, wherein approximately 70% of the data is categorized as sparse. This discourse aims to examine the utilization of the Extreme Gradient Boosting (XGboost) technique for predicting rainfall in the Lembang region, specifically emphasizing its effectiveness in handling limited data. The findings indicate that the model, when trained and tested using a 7:3 data split ratio, achieved a mean absolute error (MAE) of 1.834 for training and 4.473 for testing. Additionally, the root mean square error (RMSE) was calculated to be 3.319 for training and 7.637 for testing. The optimal hyperparameters consist of a learning rate of 0.005, a max_depth value of 10, and the utilization of 300 decision trees as n_estimators. The model effectively captures the pattern of sparse time series data and non-rainy days data, as evidenced by its low error metrics. However, it slightly underestimates the rainfall rate on the days with intense precipitation
ANALYTICAL APPROACH OF GENERALIZED LINEAR MODELS FOR HANDLING OVERDISPERSION IN POVERTY DATA OF INDONESIA Arisanti, Restu; Pontoh, Resa Septiani; Winarni, Sri; Wibowo, Fellita Odelia; Khairunnisa, Hanifah; Pratama, Raissheva Andika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1575-1586

Abstract

Poverty is one of the complex phenomena that occurs in Indonesia. Various socio-economic variables in Indonesia influence poverty, which we can mathematically model using the Generalized Linear Model (GLM) framework. In this study, we modeled data on the number of poor people per province in 2023 taken from the Badan Pusat Statistik of Indonesia website. The response variable in this study was initially assumed to exhibit equidispersion, where the variance equals the mean. However, the observed variance exceeded the mean, indicating overdispersion. Consequently, Negative Binomial Regression, an extension of the GLM that introduces an additional dispersion parameter, was applied to account for this overdispersion. This approach accommodates overdispersed count data by incorporating a gamma-distributed latent variable. The aim of this study is to determine the best model using Negative Binomial Regression in handling overdispersion in Indonesia's poverty data. This model was chosen for its robustness in capturing increased data variability, enabling the identification of factors that influence poverty. The results of this study offer a mathematically rigorous approach to better understand the underlying dynamics of poverty across provinces in Indonesia.
Identification and Modelling Tuberculosis Incidence Risk Factors in West Java with Negative Binomial Mixed Model Random Forest Arisanti, Restu; Pontoh, Resa Septiani; Winarni, Sri; Putri, Nisa Akbarilah; Maurin, Stefany
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.v10i2.29750

Abstract

Tuberculosis (TB) remains a major public health problem in many parts of the world, including in West Java Province, Indonesia. By guiding targeted medication, an accurate assessment of TB risk factors can enhance overall efforts to control tuberculosis. This study introduces modelling by integrating Negative Binomial Mixed Models (NBMM) and Random Forest (RF) called the Negative binomial mixed model random forest (NBMMRF) model.  This model is used to identify and assess risk factors associated with the incidence of tuberculosis. First, utilized NBMM to add fixed effects and random effects in the model and compensate for overdispersion. Modelling count data with overdispersion is a crucial problem in epidemiological studies, and the NBMM component in this model provides a flexible. Afterward, we include a Random Forest component in the model, which helps us detect relevant predictive features and change model weights accordingly. The resulting Negative Binomial Mixed Model Random Forest (NBMMRF) has a high accuracy value of up to 0.915. In contrast to simpler models, the NBMMRF model can capture complex and nonlinear interactions between predictors and outcomes.
APPLICATION OF YATES METHOD FOR MISSING DATA ESTIMATION IN YOUDEN SQUARE DESIGN AND ANALYSIS Hadiputri, Ratna Melati; Alifaa, Syifa Nayla; Arisanti, Restu; Winarni, Sri
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/barekengvol18iss4pp2413-2422

Abstract

The Youden square design is widely used in experimental research to control two sources of variability, but missing data can compromise the results. Addressing missing data is critical to maintaining the integrity and reliability of such experiments. This paper proposes to adapt the Yates method to handle missing data specifically in Youden square designs. We begin by outlining the structure of the Youden square design and the challenges posed by missing data. The Yates method, known for its robustness in estimating missing data, is adapted to fit this design. We demonstrate its effectiveness through simulations and real-world case studies. The simulation involved generating experimental data with one missing value, and the case study analyzed chemical process research with critical missing data points. The results show that the Yates method maintains statistical validity and improves data completeness compared to traditional methods. Its advantage lies in utilizing Youden's quadratic structure for more accurate estimation. This study highlights the Yates method as a solution to handle missing data, improving the quality and reliability of experimental research.
Co-Authors Alifaa, Syifa Nayla Clarissa Clorinda, Chrysentia Darmawan , Gumgum Hadiputri, Ratna Melati Khairunnisa, Hanifah Maurin, Stefany Pratama, Raissheva Andika Putri, Nisa Akbarilah Putri, Salma Azzahra Resa Septiani Pontoh Sri Winarni Wibowo, Fellita Odelia