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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Geuthee Journal of Applied Data Sciences Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Transcendent Journal of Mathematics and Applications Infolitika Journal of Data Science
RAHAYU, LATIFAH
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences

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Unraveling Geospatial Determinants: Robust Geographically Weighted Regression Analysis of Maternal Mortality in Indonesia Rahayu, Latifah; Ulfa, Elvitra Mutia; Sasmita, Novi Reandy; Sofyan, Hizir; Kruba, Rumaisa; Mardalena, Selvi; Saputra, Arif
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.133

Abstract

Maternal Mortality Rate (MMR) in Indonesia has experienced a concerning annual increase, reaching 4,627 deaths in 2020 compared to 4,221 in 2019. This upward trajectory underscores the urgency of investigating the factors contributing to MMR. Recognizing the spatial heterogeneity and outliers in the data, our study employs the Robust Geographically Weighted Regression (RGWR) method with the Least Absolute Deviation approach. Using secondary data from the 2020 Indonesian Health Profile publication, the research seeks to establish province-specific models for MMR in 2020 and identify the key influencing factors in each region. Standard regression analyses fall short in addressing the complexities present in the data, making the RGWR approach crucial for understanding the nuanced relationships. The chosen RGWR model utilizes the Least Absolute Deviation method and a fixed kernel exponential weighting function. Notably, this model maintains a consistent bandwidth value across all locations, showcasing its robustness. In evaluating the model variations, the exponential fixed kernel weighting function emerges as the most optimal, boasting the smallest Akaike Information Criterion (AIC) value of 23.990 and the highest coefficient of determination  value of 93.66%. The outcomes of this research yield 24 distinct models, each tailored to the unique characteristics of every province in Indonesia. This nuanced, location-specific approach is vital for developing effective interventions and policies to address the persistently high MMR. By providing insights into the complex interplay of factors influencing maternal mortality in different regions, the study contributes to the groundwork for targeted and impactful public health initiatives across Indonesia.
Spatial Estimation for Tuberculosis Relative Risk in Aceh Province, Indonesia: A Bayesian Conditional Autoregressive Approach with the Besag-York-Mollie (BYM) Model Sasmita, Novi Reandy; Arifin, Mauzatul; Kesuma, Zurnila Marli; Rahayu, Latifah; Mardalena, Selvi; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.185

Abstract

Tuberculosis (TB) remains a significant public health challenge globally, with Indonesia being the second-highest country in TB cases worldwide. Aceh Province has one of the highest TB incidence rates in Indonesia. This study aims to estimate and map the spatial distribution patterns of TB relative risk across districts in Aceh Province, Indonesia, to reveal significant variations. The study employed an ecological time-series study design, utilizing the Bayesian Conditional Autoregressive (CAR) approach with the Besag-York-Mollie (BYM) model for spatial estimation and mapping of TB relative risk. TB case data and population data for 23 districts/cities in Aceh Province from 2016 to 2022 were analyzed. Spatial analysis was used to estimate and map TB's relative risk, aiding in identifying areas with higher transmission risks. The results showed that the relative risk of TB varied across districts/cities in Aceh Province over the study period. However, Lhokseumawe and Banda Aceh consistently exhibited high to very high relative risks over the years. In 2022, Lhokseumawe City and Banda Aceh City had the highest relative risks by 2.26 and 2.17, respectively, while Sabang City and Bener Meriah District had the lowest by 0.43 and 0.32, respectively. This study provides valuable insights into the heterogeneous landscape of TB risk in Aceh Province, which can inform targeted interventions and planning strategies for effective TB control. Using the Bayesian CAR BYM model proved effective in estimating and mapping TB's relative risk, highlighting areas requiring prioritized attention in TB prevention and control efforts.
Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method Rahayu, Latifah; Sasmita, Novi Reandy; Adila, Wulan Farisa; Kesuma, Zurnila Marli; Kruba, Rumaisa
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.141

Abstract

Dengue Fever (DHF) is a dangerous infectious disease that can cause death in an infected person. DHF is a disease transmitted by the Aedes Aegypti mosquito. Dengue cases have been reported in 449 districts/cities spread across 34 provinces with deaths spread across 162 districts/cities in 31 provinces, one of which is in Aceh Province. However, there are districts and cities in Aceh Province with a large number of cases and population at risk, and there are also districts and cities with fewer cases and population at risk. As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to do planning to see which districts and cities have a high chance of DHF. In this study, the type of data used is secondary data sourced from the Aceh Provincial Health Profile from 2016 to 2022. The approach used is the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). The results of this study showed that mortality in dengue cases in Aceh Province from 2016 to 2022 had the highest mortality values in 2016 and 2022. The results of estimating the relative risk of DHF cases using the Bayesian Conditional Autoregressive (CAR) approach of the Besag-York-Mollie (BYM) Model in Aceh Province fulfill all categories with their relative risk values. Some districts/cities have relative risk values. Some districts/cities have high relative risk values of DHF cases and low relative risk values of DHF cases. Sabang city had the highest relative risk value of 3.54 and Bener Meriah district had the lowest relative risk of 0.2.
Optimizing Long-Term Meteorological Data Completeness in North Aceh, Indonesia: A Comparative Analysis of Interpolation Methods Sasmita, Novi Reandy; Saragih, Novita Sari; Rahayu, Latifah; Malfirah, Malfirah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27929

Abstract

More data in meteorological records is needed to ensure the accuracy of meteorological modeling, particularly in long-term datasets. This study aims to identify the most effective interpolation method for addressing missing data in North Aceh's meteorological dataset from 2010 to 2023, with a focus on the accuracy of methods applied across various meteorological variables. The study analyzed data from North Aceh Regency, Indonesia, comprising 25,565 daily observations of temperature, humidity, rainfall, sunshine duration, and wind speed. Missing values were interpolated using three methods: spline, stineman, and moving average interpolation. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Logarithmic Error (MSLE) across 10%, 20%, and 30% levels of simulated missing data. All analysis in this study were carried out using R-4.4.2 software. While spline interpolation performed reasonably well, it showed increased variability, especially for high-variance variables like rainfall. Moving average interpolation was less reliable, with error rates increasing alongside higher levels of missing data. In contrast, stineman interpolation consistently achieved the lowest error metrics across all levels of missing data, with MAE ranging from 0.219 to 0.6691, MSLE from 0.035 to 0.109, and RMSE from 1.247 to 2.245, demonstrating superior robustness. Stineman interpolation offers a highly effective approach for managing missing meteorological data in North Aceh’s long-term dataset, enhancing data reliability for meteorological modeling and decision-making in meteorological-sensitive sectors. This study provides practical recommendations for selecting optimal interpolation techniques, especially in regions with variable meteorological data quality.
Application of The Exponential Smoothing Method in Predicting The Visit of Foreign Tourists to Indonesia Rusdiana, Siti; Rahayu, Latifah; Asmanidar, Asmanidar
Transcendent Journal of Mathematics and Applications Vol 1, No 1 (2022)
Publisher : Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/tjoma.v1i1.28843

Abstract

Indonesia is rich in natural beauty, diverse ethnic groups, cuisines, and languages, making it one of the most popular destinations for both domestic and international tourists. The purpose of this study is to forecast the number of foreign tourist visitors from 2020 to 2021. The government can improve facilities or infrastructure while also preserving the beauty and culture of Indonesia's various ethnic groups. This study will investigate 101 foreign countries that visited Indonesia using one of the Exponential Smoothing methods. In forecasting, the Triple Exponential Smoothing method has three smoothing times. Forecasting in 101 foreign tourists visiting Indonesia yields different parameter results because each result has a different smoothing value. Once the parameters ranging from 0.1 to 0.9 is close to forecasting results, there are close to the actual value. The search ends at one of these parameters because it already yields the expected results to calculate the error value using the MAPE method. There were 20 foreign tourists selected based on the average number of visits to Indonesia.
A Stochastic Projection for Tuberculosis Elimination in Indonesia by 2030 Sasmita, Novi Reandy; Ramadani, Maya; Ikhwan, Muhammad; Munawwarah, Munawwarah; Rahayu, Latifah; Mardalena, Selvi; Ischaq Nabil Asshiddiqi, M.; Suyanto, Suyanto; Safira, Nanda
Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Vol. 8 No. 11 (2025): November 2025
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v8i11.8548

Abstract

Introduction: Indonesia, with the world's second-highest tuberculosis (TB) burden, has targeted TB elimination (65 cases per 100,000) by 2030. This study aimed to evaluate the feasibility of achieving this goal by projecting TB incidence trends using a stochastic epidemic model that accounts for the uncertainties inherent in TB transmission dynamics in latent TB infections. Methods: The initial values for state variables and parameters were derived from a comprehensive literature review and calibrated against publicly available epidemiological data from the Indonesian Ministry of Health reports from 2018-2022. A Susceptible, Vaccinated, Three Exposed, Three Infectious, Recovered (SVE3I3R) model was developed, incorporating Gaussian noise into the exposed compartments to simulate real-world unpredictability in latent infection dynamics. The model was solved numerically using the fourth-order Runge-Kutta (RK4) method in R software. Key outcomes measured were the projected incidence of drug-susceptible TB (DS-TB), multidrug-resistant TB (MDR-TB), and extensively drug-resistant TB (XDR-TB). Results: Model projections suggest that the overall TB incidence rate will fall from 387 cases per 100,000 people in 2023 to a projected 320 cases per 100,000 by 2030. However, this remains far above the national target. While DS-TB cases decreased to 730,283, MDR-TB and XDR-TB cases were projected to surge dramatically to 120,939 cases and 104,651 individuals, respectively. The estimation signals a critical shift in the epidemic's profile. Conclusions: Indonesia is not on track to achieve its 2030 TB elimination target under current interventions. The alarming rise of drug-resistant TB necessitates an urgent, aggressive, and multifaceted policy response. This study underscores the critical value of incorporating stochasticity into epidemiological models for more realistic forecasting and public health planning in high-burden settings.
Determination of career preferences among marine science students through discriminant analysis Umam, Khairul; Mastura, Ayu; Ansari, Marzuki; Nurita, Fonna; Rahayu, Latifah; Yuni, Cut; Ramadhan, Riski
Jurnal Geuthèë: Penelitian Multidisiplin Vol 8, No 1 (2025): Jurnal Geuthèë: Penelitian Multidisiplin
Publisher : Geuthèë Institute, Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52626/jg.v8i1.392

Abstract

The process of selecting a profession frequently poses a challenge for students transitioning into the workforce, including those in Marine Science. This decision necessitates careful consideration due to its direct correlation with their career trajectory and future prospects. This research aims to predict the professional preferences of Marine Science students towards careers as Conservation Experts, Environmental Impact Analysts, and Aquaculture Technicians, based on their academic course grades. This study employs a descriptive quantitative approach using literature research. The population of this research consists of Marine Science students from the 2020 cohort. A sample of 50 students was selected using simple random sampling techniques. Data were collected through documentation techniques, specifically student transcript records, and analyzed using Fisher's discriminant analysis with SPSS to formulate predictive functions. The results indicate that the formulated discriminant functions can accurately predict professional preferences based on course grades, achieving an accuracy rate of 88%. This high accuracy rate suggests that the derived discriminant function model has the potential to serve as a valuable tool for Marine Science students in making informed career decisions that align with their academic abilities, thereby minimizing uncertainty in determining suitable career paths.
Comparison of Spatial Interpolation Methods: Inverse Distance Weighted and Kriging for Earthquake Intensity Mapping in Aceh, Indonesia Rahayu, Latifah; Utami, Cut Chairilla Yolanda; Fauzi, Rahmatul; Sasmita, Novi Reandy
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.347

Abstract

Aceh Province, located in the Sumatra megathrust zone of Indonesia, is one of the most seismically active regions in Southeast Asia. Understanding the spatial distribution of earthquake magnitudes is essential for disaster mitigation and risk management. This study compares two spatial interpolation methods Inverse Distance Weighted (IDW) and Kriging to determine the most accurate approach for mapping earthquake intensity in Aceh Province. A total of 2,255 earthquake events with magnitudes of 2.5 M and above, recorded between 1990 and 2024 by the United States Geological Survey (USGS), were analyzed. IDW was tested using five power parameters (p = 1–5), while Kriging applied three semivariogram models (spherical, exponential, and Gaussian). The interpolation accuracy was assessed through Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). Results indicated that Kriging with the exponential semivariogram achieved the highest accuracy, with RMSE = 0.0848, MSE = 0.0072, and MAPE = 1.14%, outperforming IDW (RMSE = 0.2288, MSE = 0.0523, MAPE = 1.24%). The Kriging model effectively represented the gradual spatial decay of seismic energy, identifying Aceh Singkil and northern Simeulue as the most earthquake-prone zones, consistent with regional tectonic patterns. These findings confirm that incorporating spatial autocorrelation enhances interpolation accuracy and geophysical interpretation. The study establishes Kriging as a reliable tool for seismic hazard mapping and provides valuable insights for disaster preparedness, infrastructure planning, and future geostatistical applications in earthquake risk assessment.
Can Indonesia Eliminate Tuberculosis by 2030? A Deterministic Epidemic Model Approach Sasmita, Novi Reandy; Ramadani, Maya; Ikhwan, Muhammad; Rahayu, Latifah; Mardalena, Selvi; Suyanto, Suyanto; Safira, Nanda; Huy, Le Ngoc; Myint, Ohnmar
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.v10i1.35252

Abstract

Indonesia, bearing the world’s second-highest tuberculosis (TB) burden, has mandated a national target to eliminate TB by 2030, aiming for an incidence rate of 65 per 100,000 population. This study aims not only to project future transmission dynamics but also to systematically explore the specific epidemiological barriers, namely, drug resistance and relapse mechanisms, that hinder achieving this goal. To address the heterogeneity of TB transmission, we developed a novel deterministic SVE3I3R model. This framework stratifies the population into vaccinated, latent Tuberculosis Infection (LTBI), and infectious compartments, explicitly distinguishing among Drug-Susceptible (DS-TB), Multidrug-Resistant (MDR-TB), and Extensively Drug-Resistant (XDR-TB) strains. The resulting system of ordinary differential equations was solved numerically using the fourth-order Runge-Kutta (RK4) method to ensure stability and accuracy in simulating long-term epidemiological trends from 2023 to 2030. Parameters were calibrated using national reports and literature specific to the Indonesian context. Projections indicate that Indonesia will miss the 2030 elimination target by a significant margin. The model forecasts a TB incidence rate of 321 per 100,000 population by 2030, nearly five times the national benchmark. The analysis reveals that failure to reach the target is mechanistically driven by a "relapse trap" among recovered individuals and an alarming exponential surge in resistant strains (MDR-TB and XDR-TB). These findings suggest that current control strategies are insufficient not merely in scale but in structure. Evidence-based policy must urgently shift from standard intervention to aggressive interruption of resistance pathways and enhanced management of the latent reservoir to prevent the projected demographic resurgence.
Co-Authors Adila, Wulan Farisa Ansari, Marzuki Arif Saputra, Arif Arifin, Mauzatul Asmanidar, Asmanidar Fauzi, Rahmatul Hizir Sofyan Huy, Le Ngoc Ischaq Nabil Asshiddiqi, M. Khairul Umam Kruba, Rumaisa Malfirah, Malfirah Mardalena, Selvi Mastura, Ayu Muhammad Ikhwan Muhammad Ikhwan Myint, Ohnmar Nanda Safira Nurita, Fonna Raden Mohamad Herdian Bhakti Ramadani, Maya Ramadhan, Riski Saragih, Novita Sari Sasmita, Novi Reandy Siti Rusdiana Suyanto Suyanto Ulfa, Elvitra Mutia Utami, Cut Chairilla Yolanda Yuni, Cut Zurnila Marli Kesuma