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All Journal Journal of Innovation and Applied Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Statistics and Its Applications
Rahmi, Nur Silviyah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Improving Statistical Analysis in Supporting Big Data Era for SMA Negeri 1 Krembung Teachers Rahmi, Nur Silviyah
Journal of Innovation and Applied Technology Vol 10, No 1 (2024)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2024.10.01.012

Abstract

Classroom Action Research (CAR) is a research type conducted within the classroom environment to enhance the learning process.  To collaborates in organizing community service through training to empower the educational community at SMA Negeri 1 Krembung. To assess the community service program's effectiveness, pre-test and post-test analyses were carried out using the t-test. By applying the Wilcoxon Signed Rank Test hypothesis, a significant difference was identified in statistical and data science-based analysis skills before and after the community service program implementation. The ability to analyze based on statistics and data science has improved, evident from the post-test mean rank value surpassing the pre-test mean rank value (11.08 > 6.33). Consequently, it can be concluded that repeating training activities is essential to enhance statistical and data science-based analysis skills in the big data era and to provide a deeper understanding of Classroom Action Research.
COX PROPORTIONAL HAZARD AND EXPONENTIAL SURVIVAL ANALYSIS IN PATIENTS WITH END-STAGE CHRONIC KIDNEY FAILURE AT BOJONEGORO Rahmi, Nur Silviyah; Rifai, Achmad; Islami, M. Irfan; Azifa, Annisa Andra
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/barekengvol19iss3pp2127-2140

Abstract

End-stage chronic renal failure is a condition that requires long-term treatment such as haemodialysis and poses a serious threat to patient survival. However, the survival time of each patient varies, depending on various clinical and demographic factors. Identifying variables that have a significant effect on survival time is important to help medical personnel prioritise patient care. Cox proportional hazard and exponential regression are statistical methods used to identify independent variables that affect the dependent variable, survival time. In this study, Cox proportional hazard and exponential regression survival analysis were modelled on end-stage chronic renal failure patients who were hospitalised in January-April 2024 at RSUD Dr. R. Sosodoro Djatikoesoemo Bojonegoro. This study aims to identify independent variables that have a significant influence on the survival rate of patients with end-stage chronic renal failure and the best model between Cox proportional hazard and exponential models. The Cox Proportional Hazard method is a semi-parametric method that analyses the influence of variables without having to know the specific shape of the failure time distribution. Meanwhile, the exponential model is a parametric model that assumes that the hazard function is constant over time. In this study, 10 variables were used to see their influence on the risk of occurrence. The results of Cox proportional hazard and exponential regression analysis obtained independent variables that have a significant effect, the variables of main complaint (X3), urea (X6), and diastolic blood pressure (X8) on the survival time of patients with chronic renal failure. The hazard ratio value on significant variables, the variable that can increase the risk of death, is urea. Every additional 1 mg/dL urea value will increase the risk of death of chronic renal failure patients by 0.9%. The exponential model of 383.4526 is the best model based on the AIC value.
GENERALIZED CONFIRMATORY FACTOR ANALYSIS FOR KNOWING IMPACT OF KNOWLEDGE, ATTITUDES, AND BEHAVIORAL FACTORS HIV/AIDS IN INDONESIA Rahmi, Nur Silviyah; Astutik, Suci; Astuti, Ani Budi; Muhammad, Alifiandi Rafi; Maisaroh, Ulfah; Handayani, Sri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0695-0706

Abstract

The cumulative number of detected HIV/AIDS cases in the January – March 2021 period is 9,327, consisting of 7,650 HIV and 1,677 AIDS reported by 498 districts and cities from 514 districts and cities in Indonesia. Human Immunodeficiency Virus (HIV) is the virus that causes Acquired Immunodeficiency Syndrome (AIDS). Several factors that influence the spread of HIV/AIDS include knowledge, attitudes and behavior about HIV/AIDS. Someone who gains knowledge about HIV/AIDS will have high self-confidence and a positive outlook on life and be more optimistic in taking HIV/AIDS prevention actions. The main objective of this study is to determine the influence of external factors which include demographic, social and economic aspects, as well as internal factors which include knowledge, attitudes and behavior to the level of transmission of HIV/AIDS. By using the CFA approach, it can be seen which indicators have the greatest influence on the latent variables of knowledge, attitudes, and behavior or called loading factors. The data used is secondary data from a 5-year survey from the Central Statistics Agency, namely the 2017 Indonesian Demographic and Health Survey (IDHS) published at the end of 2018. The CFA results show that the P11 variable (about known infections) has the largest loading factor value, which is 0.613 in the variable. . hidden. knowledge. In the latent variable of attitude, the S1 variable (about identifying how the respondent knows someone is infected with HIV-AIDS) has the largest loading factor value of 0.514. While the behavioral latent variable, the variable R8 (whether men have been infected with sexually transmitted diseases (STI) with symptoms) has the largest loading factor value, which is 0.954.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Astutik, Suci; Rahmi, Nur Silviyah; Irsandy, Diego; Saniyawati, Fang You Dwi Ayu Shalu; Mashfia, Fidia Raaihatul; Lusiana, Evelin Dewi; Risda, Intan Fadhila; Susanto, Mohammad Hilmi
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/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
META-REGRESSION OF SOCIOECONOMIC FACTORS AND THE PREVALENCE OF PHYSICAL DISORDERS IN HYPERTENSIVE PATIENTS Rahmi, Nur Silviyah; Astutik, Suci; Surya Wardhani, Ni Wayan; Maharani, Adinda Gita; Fakhrunnisa, Atmadani Rahayu; Khatimah, Husnul; Aulia, Silvia Intan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2275-2286

Abstract

Hypertension is a common degenerative disease with a high mortality rate and a significant impact on quality of life and productivity. Education level plays a crucial role in understanding and managing hypertension, where higher education levels can contribute to reducing the risk of hypertension. This study utilized meta-analysis and meta-regression to explore the relationship between education level and hypertension prevalence. Secondary data from eight previous studies conducted between 2015 and 2023 were analyzed. Heterogeneity analysis was performed to determine the appropriate meta-analysis model, with a random-effect model selected based on the test results. Of the eight studies analyzed, five showed a negative odds ratio, indicating that individuals with higher education levels have a lower likelihood of developing hypertension compared to those with lower education levels. The heterogeneity test showed significant variability among the studies (I2 = 91.38%). The random-effect model estimated a combined effect size with an ln odds ratio of -0.1777 and a 95% confidence interval of -0.3228 to -0.0326. These findings suggest that higher education levels are associated with a lower risk of hypertension. This underscores the importance of improving access to quality education as part of public health strategies to reduce the incidence of hypertension and enhance overall community well-being.
Identification of Social Support and Knowledge of Covid-19 Survivors with Structural Equation Modeling in R Rahmi, Nur Silviyah; Masruro Pimada, Laila; Yesica, Reza; Nur Cahaya Ningsih, Devi
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p287-295

Abstract

COVID-19 cases in Indonesia have finally reached a second peak amounting to 4 million cases. A number of the death rate was 3.4 percent, yet the recovery rate was 95.9 percent. The Health Ministry of Republic Indonesia through the Covid-19 Task Force has issued guidelines for preventing and controlling Covid-19 to decrease the death rate and increase the recovery rate. According to the guidelines, a person who undergoes quarantine needs to be provided with health care, and social and psychosocial support. This study seeks to identify the influence of external factors including social support, as well as internal factors including patient motivation, and knowledge on the recovery rate of Covid-19 survivors. The research methods use Structural Equation Modelling to determine the indicators that have the most significant influence on the latent variables of social support, knowledge, and motivation for healing Covid-19. Primary data collection was carried out online with a sample of 176 Covid-19 survivors across Indonesia in August 2021. The methods of the Shapiro-Wilk test for normal multivariate show the p-value at 0.00 significantly satisfies the assumption. The result shows that social support has a significant effect on knowledge with a regression coefficient is 0.263. Knowledge has a regression coefficient is 0.645 for the Healing of Covid-19. In conclusion, the higher social support provided by the patient's external parties: family, surrounding environment, and public health center officers, will impact the higher patient's knowledge and healing of Covid-19 disease. Meanwhile, social support has no significant effect on healing actions.
Co-Authors ACHMAD RIFAI Ani Budi Astuti Aulia, Silvia Intan Azifa, Annisa Andra Fakhrunnisa, Atmadani Rahayu Handayani, Sri Husnul Khatimah Irsandy, Diego Islami, M. Irfan Lusiana, Evelin Dewi Maharani, Adinda Gita Maisaroh, Ulfah Mashfia, Fidia Raaihatul Masruro Pimada, Laila Muhammad, Alifiandi Rafi Ni Wayan Surya Wardhani Nur Cahaya Ningsih, Devi Risda, Intan Fadhila Saniyawati, Fang You Dwi Ayu Shalu Suci Astutik Susanto, Mohammad Hilmi Yesica, Reza