Article Per Year (5 Year)
p-Index From 2020 - 2025
1.745
P-Index
This Author published in this journals
All Journal Al-Jabar : Jurnal Pendidikan Matematika Desimal: Jurnal Matematika Communication in Biomathematical Sciences International Journal of Hydrological and Environmental for Sustainability International Journal of Applied Mathematics, Sciences, and Technology for National Defense
Rachmawati, Ro'fah Nur
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Environmental Science Mathematics Physics Social Sciences

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Bayesian Spatial Modeling of Landslide Events Using Integrated Nested Laplace Approximation (INLA): A Study Case on Natural Conditions and Community Actions in East Java, Indonesia Alfarisi, Salman; Christina, Athalia; Naqiya, Sadiyana Yaqutna; Rachmawati, Ro'fah Nur; Machmud, Amir; Palupi, Endah Kinarya
International Journal of Hydrological and Environmental for Sustainability Vol 2, No 3 (2023): International Journal of Hydrological and Environmental for Sustainability
Publisher : CV FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/ijhes.v2i3.354

Abstract

Bayesian Spatial Modeling Using Integrated Nested Laplace Approximation (INLA) is an advanced statistical technique that can be used to model and analyze occurrences in geographic areas. Landslides are one of natural disasters that occur due to natural and human factors and pose a serious threat to East Java Province which has complex natural conditions. The disaster brings various losses, including economic, infrastructural, human life, and environmental. This study investigates the factors contributing to landslides across 29 districts and 9 cities in East Java, Indonesia, using spatial regression modeling by Integrated Nested Laplace Approximation (INLA). The factors include the number of seaside villages, the number of slope topography villages, and the area of temporarily uncultivated gardens and fields in 2021. The modeling results show that the number of seaside villages, the number of slope topography villages, and the area of fields that are temporarily uncultivated have a significant influence on the occurrence of landslides so that efforts to mitigate and prevent such disasters can be focused on the contributing factors. We conclude that the model might be able to identify potential landslide risk areas through mapping.
Mathematical modeling of basal body temperature influence on menstrual cycle, length of sleep, and stress levels as detection of fertile period (ovulation) in women Rahayu, Tiara; Hasudungan, Ardiman; Afiya, Rahmatul; Farradila M, Vinka; Rachmawati, Ro'fah Nur
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 1, No 3 (2023): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v1i3.234

Abstract

Basal body temperature is the temperature when the body reaches the resting phase or does not perform any activity. Basal body temperature is influenced by factors such as the quality of a person's length of sleep, stress, and menstrual cycle patterns. The benefit of checking and monitoring basal body temperature for a woman is to determine when a woman starts to enter the ovulation period, making it easier for couples who want a target pregnancy. The measurement method was carried out in the morning right after waking up using a thermometer flanked in the armpit area by applying three repetitions of measurements within a period of two months with the number of sample participants of 10 cadet students of military biology study program with an age range of 19-21 years. This type of research is carried out observationally and analytically using longitudinal data. The analysis used in this study was by conducting a statistical mathematical model trial by analyzing the p-value. The results showed that in the paired test there was a significant influence of the relationship of basal body temperature on the menstrual cycle at the time before menstruation and when menstruation. The researcher's suggestion is to conduct further research with larger participants of samples and the presence of sample variations in observations because not all biologists do significant research with the statistical method.
Modeling fuel consumption in various external vehicle conditions for military vehicle using mixed linear models Saputra, Muhammad Nuraliffudin; Arif, Samsul; Wijanarko, Fakhri; Panse, Vishal R; Lubis, Agnes Sprakezia; Rachmawati, Ro'fah Nur
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 1, No 2 (2023): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v1i2.183

Abstract

With the crisis in fuel consumption, it is necessary to save fuel consumption by selecting fuel that is quite efficient. The aim of the research is to examine the fuel consumption patterns of vehicles in an area that experiences various weathers so as to determine the level of efficiency of fuel use. both from the factor of fuel type, distance, speed, temperature, to the weather. testing was carried out using the linear model technique, the Linear Mixed effect model, and the Anova mathematical model. The results of the analysis if the different types of fuel have a very large significant effect in influencing the fuel consumption of a vehicle. With all the approaches analyzed from the linear mix model, analysis of variance and linear mix random effect model, the results show that other independent variables such as average speed, distance traveled and ambient temperature do not have much effect on fuel consumption. Based on modeling results that has been done, it can be identified if fuel consumption is strongly influenced by the type of fuel used by the vehicle. Meanwhile, other variables such as vehicle mileage, average speed, and ambient temperature at the time of data collection did not show any significant effect on fuel consumption.
Mixed linear model for investigating food security during the covid-19 pandemic: Panel data for rice consumption in indonesia Aisyah, Mutiara Aghnyn; Putri, Devita Amalia; Chandra, Yoshua; Syazali, Muhamad; Machmud, Amir; Rachmawati, Ro'fah Nur
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 1, No 1 (2023): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v1i1.174

Abstract

The Covid-19 pandemic has affected human life behavior, starting from health, the economy to living habits, one of them is the rice consumption. This study aims to find out whether the Covid-19 pandemic can affect people's rice consumption, and what are the factors that can affect people's rice consumption before and during the pandemic. The independent factors studied in this study were harvested area, productivity, rice production, crime rate, and the ratio of household gas use, with rice consumption as the dependent variable. The data used is panel data for 2019 and 2020, from 34 provinces in Indonesia, which is one of the five countries with the highest rice consumption in the world. By using mixed linear models, the research results show that in general Covid-19 pandemic has not had a significant effect on rice consumption in Indonesia. Other facts also show that social factors, namely the crime rate during a pandemic, did not have a significant effect on rice consumption.However, this is different from economic factors such as productivity and harvested area which have a significant positive effect on rice consumption in Indonesia.
Modeling suspected malaria cases in Papua province with second order Besag-York-Mollie 2 spatial regression Azzahra, Kirana; Rachmawati, Ro'fah Nur; Syazali, Muhamad
International Journal of Applied Mathematics, Sciences, and Technology for National Defense Vol 2, No 2 (2024): International Journal of Applied Mathematics, Sciences, and Technology for Natio
Publisher : FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/app.sci.def.v2i2.433

Abstract

The number of malaria cases in Indonesia has increased in recent years. The highest malaria cases in Indonesia are in the eastern region, namely Papua Province, where in 2021 there were 86,022 cases. This study aims to model suspected malaria cases in Papua using the Integrated Nested Laplace Approximation (INLA) approach. Modelling is carried out with two different orders to see the difference in determining the best results. The results showed that second-order spatial modelling provides better results than first order modelling because the RMSE value is smaller than the first-order model. Based on these results, it is concluded that the INLA approach with second-order spatial modelling is effective for analysing and predicting suspected malaria cases in Papua. Therefore, these results can be used as a reference in developing malaria control strategies in the region.
Utilize imagery and crowdsourced data on spatial employment modelling Pusponegoro, Novi Hidayat; Rachmawati, Ro'fah Nur; Siallagan, Maria A. Hasiholan; Wicaksono, Ditto Satrio
Al-Jabar: Jurnal Pendidikan Matematika Vol 15 No 2 (2024): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v15i2.24518

Abstract

Background: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java recorded the lowest labor absorption rate in Indonesia, necessitating a new approach.Aim: This study combines imagery, crowdsourced data, and official statistics to identify factors influencing labor absorption in Java Island.Method: Geographically Weighted Regression (GWR) was employed to account for spatial effects in the data.Results: The model reveals that nighttime light intensity in urban and agricultural areas, along with environmental quality, significantly enhances labor absorption across Java. Internet facilities, universities, and the number of micro and small industries also positively influence most districts/cities.Conclusion: Incorporating new data sources offers valuable insights for understanding employment patterns and can enrich employment research frameworks.
The influence of climate change and country-based conflict on crop production: Evidence based on global panel data in the last decade Rahman, Juli Yandi; Rachmawati, Ro'fah Nur; Nugraha, Arya Muditama; Widjanarko, Farrell Tajusalatin
Desimal: Jurnal Matematika Vol. 6 No. 2 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i2.18928

Abstract

In the past decade, the food crisis has become a special concern for the international community. This is in the spotlight as the earth ages, increasingly changing climatic conditions lead to erratic crop yields and worsening crop quality. On the other hand, this condition is exacerbated by the increasingly tense dynamics of international politics which leads to conflict between countries. For this reason, we investigated the relationship between these conditions using the linear mixed model method. In this article, the model obtained is able to describe the real conditions currently occurring regarding the relationship between climate change, conflict between countries and crop production. Among other things, it is known that the majority of continents are carrying out agricultural extensions and intensifying efforts to reduce CO2 emissions, to increase crop production. On the other hand, as time goes by, the model shows that environmental temperature fluctuations are getting bigger. Apart from that, conflict factors apparently exacerbate the effects of climate change which directly affects crop production. This article also provides suggestions for countries on a continent to increase crop production while maintaining climate balance.
The effects of hydrometeorological disaster and potential conflicts on the human development index using linear mixed multilevel models Zevic, Farell Fillyanno; Rachmawati, Ro'fah Nur; Djunet, Ghina Nisrina; Almutawakkil, Fauzan Naufal
Desimal: Jurnal Matematika Vol. 6 No. 3 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i3.19514

Abstract

As is generally known, the human development index (HDI) is formed from three main factors, namely education, health, and income, which measure the population's access to a decent standard of living. Using the linear mixed multilevel models, this study indicates that other factors beyond these three basic dimensions of HDI, namely hydrometeorological disasters and potential conflicts, significantly affect the HDI value. This research focuses on longitudinal data analysis from 27 regencies and cities in West Java, Indonesia, in the last four years until 2022, with the level of hydrometeorological disasters consistently increasing every year and an increasing trend in the number of potential conflicts. The dimensions of human life and other factors can affect the human development index, namely the number of hydrometeorological disasters and potential conflicts, which have a negative correlation so that the value of the HDI can be reduced if the intensity of hydrometeorological disasters increases and possible conflicts can be controlled. Moreover, this study shows that uncontrolled potential conflicts in each regency or city from time to time can reduce HDI values. Therefore, this research can be a reference for the government, stakeholders, and the community in carrying out work programs that are right on target to increase HDI consistently every year.
Bayesian spatial data analysis: Application of pneumonia spread in west java Habsy, Muhammad Yusuf Al; Husein, Fulkan Kafilah Al; Yahya, Muhammad Harun; Rachmawati, Ro'fah Nur
Desimal: Jurnal Matematika Vol. 7 No. 1 (2024): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v7i1.23154

Abstract

Pneumonia has a notable influence on public health, especially among susceptible demographics like children and the elderly. This respiratory disease can be transmitted through human interaction. Analyzing the spread of the illness within a community requires assessing the characteristics of the community itself. The objective of this research is to describe the distribution of pneumonia cases and their causes in the West Java Province using RStudio software. The analytical method employed is the Integrated Nested Laplace Approximations (INLA) approach, a Bayesian statistical method used for estimation in complex Bayesian models, particularly in hierarchical or nested structure. The sample utilized comprises the entire population, totaling 27 Districts/Cities within West Java Province. The influence of differences in population size, number of people living in poverty, waste production, the quantity of primary healthcare facilities, total number of vehicles, and the count of HIV patients in Cities/Regencies in West Java on the spread of pneumonia will be analyzed. The result of analysis show that the population and number of health centers variables had a significant influence on the mapping of pneumonia disease in each location. And also, the Relative Risk (RR) and Standardized Incidence Ratio (SIR) maps show that some regions have a higher risk of pneumonia compared to other regions. These findings are expected to provide insights for public policies in addressing health issues, particularly in the efforts to prevent and control diseases like pneumonia. Moreover, these results serve as a foundation for further studies regarding other factors that might contribute to the spread of this disease at the local level.
Regresi Multiskala Tertimbang Geografis dan Temporal dengan LASSO dan Adaptif LASSO untuk Pemetaan Kejadian Tuberkulosis di Jawa Barat Habsy, Muhammad Yusuf Al; Rachmawati, Ro'fah Nur; Khotimah, Purnomo Husnul; Natari, Rifani Bhakti; Riswantini, Dianadewi; Munandar, Devi; Izzaturrahim, Muh. Hafizh
Communication in Biomathematical Sciences Vol. 8 No. 1 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.1.6

Abstract

Tuberculosis (TB) is a global health issue caused by Mycobacterium tuberculosis and can affect any organ of the body, especially the lungs. The trend of TB cases varies between regions, and analytic assessment is required to identify the predictor variables. The purpose of this research is to compare the Multiscale Geographically and Temporally Weighted Regression (MGTWR) and the Geographically and Temporally Weighted Regression (GTWR) method, which both use Gaussian, Exponential, Uniform, and Bi-Square kernel functions, to identify significant variables in each region annually. The MGTWR method has the advantage of using a flexible bandwidth for each observation, that results in more accurate coefficient estimates. The sample used was 27 districts and cities in West Java Province, involving 36 variables divided into 5 dimensions, namely global climate, health, demography, population, and government policy, with a time span of 2019–2022. To overcome the problem of multicollinearity, the approach was carried out using the Least Absolute Shrinkage Selection Operator (LASSO) and Adaptive LASSO methods. In determining the best model, the prioritized criteria are to achieve the highest R2, which indicates the optimal level of model fit, as well as the smallest AIC, which indicates the most efficient model goodness of fit. The best model is MGTWR with LASSO variable selection on the Bi-Square kernel. This model has an R2 of 91.25% and the smallest AIC of 139.868. From the best model, each region emerged with a cluster structure affected by various variables from 2019 to 2022, providing an in-depth understanding of TB mapping that can assist in formulating more effective intervention measures.
Co-Authors Afiya, Rahmatul Aisyah, Mutiara Aghnyn Almutawakkil, Fauzan Naufal Amir Machmud, Amir Azzahra, Kirana Chandra, Yoshua Christina, Athalia Devi Munandar, Devi Djunet, Ghina Nisrina Farradila M, Vinka Habsy, Muhammad Yusuf Al Hasudungan, Ardiman Husein, Fulkan Kafilah Al Izzaturrahim, Muh. Hafizh Khotimah, Purnomo Husnul Lubis, Agnes Sprakezia Muhamad Syazali Naqiya, Sadiyana Yaqutna Natari, Rifani Bhakti Novi Hidayat Pusponegoro Nugraha, Arya Muditama Palupi, Endah Kinarya Panse, Vishal R Putri, Devita Amalia Rahayu, Tiara Rahman, Juli Yandi Riswantini, Dianadewi Salman Alfarisi Samsul Arif Saputra, Muhammad Nuraliffudin Siallagan, Maria A. Hasiholan Wicaksono, Ditto Satrio Widjanarko, Farrell Tajusalatin Wijanarko, Fakhri Yahya, Muhammad Harun Zevic, Farell Fillyanno