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All Journal JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI BAREKENG: Jurnal Ilmu Matematika dan Terapan Rangkiang Mathematics Journal Indonesian Journal of Statistics and Its Applications
Almuhayar, Mawanda
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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TIME SERIES MODEL WITH LONG SHORT-TERM MEMORY EFFECT FOR GREENHOUSE GAS ESTIMATION IN INDONESIA Saputra, Ridho; Nisa, Alvi Khairin; Ramadhani, Nia; Almuhayar, Mawanda; Devianto, Dodi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp949-960

Abstract

Climate change is one of the major challenges in the world today, characterized by changes in meteorological values, such as rainfall and temperature, caused by the concentration of greenhouse gases in the atmosphere, such as CO2, N2O, and CH4. These accumulated greenhouse gases form a layer that prevents heat radiation from escaping, causing the greenhouse effect and global warming. Addressing the effects of greenhouse gas emissions requires appropriate strategies, one of which is to predict future greenhouse gas emissions for planning appropriate actions. Time series models such as the Autoregressive Integrated Moving Average (ARIMA) model are often used but have drawbacks due to their assumption of linear relationships. On the other hand, the Long Short-Term Memory (LSTM) model, introduced by Hochreiter and Schmidhuber in 1997, can learn complex and nonlinear relationships in data. This study uses LSTM to estimate greenhouse gas emissions in Indonesia based on emitting sectors, hoping to anticipate negative impacts and reduce greenhouse gas emissions. The results show that the LSTM model has good performance with an error below 20%, and it is predicted that greenhouse gas emissions will continue to increase.
Comparison of Linear Regression and Polynomial Local Regression in Modeling Prevalence of Stunting Fitri, Fadhilah; Almuhayar, Mawanda
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.81

Abstract

Stunting is one of the main focuses of the government in Indonesia. This is because nutritional status is one of the benchmarks of community welfare. Stunting can be influenced by various societal aspects such as health, economy, social status, and education. One factor that is thought to be closely related to stunting is the level of education. Therefore, the prevalence of stunting and the level of education will be modeled; in this case, the mean years of schooling is used. Modeling uses two approaches: parametric through linear regression and nonparametric through local polynomial regression. This study compares both models to see which method better explains the stunting phenomenon. The comparison is made through the determination coefficient value or R2, Root Mean Square Error or RMSE, and the fitted curve plot. The results of R2 and RMSE for both models were obtained. The linear regression model has an R2 of 32.94% and an RMSE of 4.84. Meanwhile, for the local polynomial model, it is R2 43.44% and RMSE 4.32. Based on these results, it can be concluded that local polynomial regression is better at modeling the relationship between the prevalence of stunting and mean years of schooling in Indonesia. This finding confirms that the polynomial local regression method can capture phenomena that occur for data that do not follow a particular pattern.
MODELING TOTAL FERTILITY RATE IN INDONESIA: A COMPARISON OF FOURIER SERIES REGRESSION AND ELASTIC NET REGRESSION Fitri, Fadhilah; Ketrin, Melin Wanike; Almuhayar, Mawanda
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/barekengvol19iss3pp2017-2028

Abstract

The Total Fertility Rate (TFR) describes population growth and socioeconomic development of a country. This statistic plays an important role in predicting future social and economic conditions. Indonesia has experienced a steady decline in TFR over the past few decades, which can be a serious problem if this trend continues. Therefore, the factor influencing the decline must be found. The independent variables include the percentage of women graduating high school, percentage of the poor population, poverty gap index, poverty severity index, prevalence of inadequate food consumption, proportion of people living below 50 percent of median income, unemployment rate, infant mortality rate, child mortality rate, and percentage of ever-married women aged 15–49 years using contraception methods. The aim of this study is to compare both Fourier Series Regression and Elastic Net Regression models to see which approximation can capture the TRF phenomenon that occurs in Indonesia and identify the causes of its decline. Fourier Regression is chosen because there is a repetition of patterns in several variables. Moreover, this data is experiencing multicollinearity; hence, Elastic-net Regression is the best way because this method overcomes the limitations of each Ridge and Lasso approach. These models are compared to see which is more suitable to capture the relationships between these factors and TFR. The best model obtained will provide a clearer understanding of Indonesia's underlying drivers of fertility decline. The result is that the Fourier Series Regression can model all variables better than the Elastic-net Regression, and the independent variables can explain the proportion of variance in the dependent variables by 97.91%, with all the independent variables significantly affecting the Total Fertility Rate.
Ordinal Logistic Regression Model of Micro, Small, and Medium-Sized Enterprises Income: A Case Study of Micro, Small and Medium-Sized Enterprises in Surabaya Alifah, Amalia Nur; Edina, Almira Ivah; Almuhayar, Mawanda
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p143-154

Abstract

Micro, Small, and Medium Enterprises (MSMEs) is a business sector that is able to make a significant contribution to economic recovery in Indonesia. In Surabaya, there are many MSMEs with various fields, both food and non-food sectors which include services, trade, etc. MSMEs actually have great potential to boost the economic growth of the people of Surabaya. Especially during the COVID-19 pandemic, MSMEs owners must be able to strategize how their income can be stable or even bigger. Therefore, it is very important to know what factors can boost MSMEs income in Surabaya. In this study, it will be examined what factors can affect the income of MSMEs in Surabaya. The method used in this study is Ordinal Logistic Regression which aims to determine which independent variables or factors affect the dependent variable which in this case is MSMEs income. Based on the results of the analysis, it can be seen that the variables that affect MSMEs income are MSMEs Location, MSME Activities, and MSME Outreach. Keywords: ordinal logistic regression, MSMEs, income.
Radiation Dose Evaluation for Radiotherapy Workers at Unand Hospital Using Four-Element Thermoluminescence Dosimetry Fardela, Ramacos; Milvita, Dian; Rasyada, Latifah Aulia; Almuhayar, Mawanda; Diyona, Fiqi; Mousa, Almahdi
Jurnal Ilmiah Pendidikan Fisika Al-Biruni Vol 12 No 2 (2023): Jurnal Ilmiah Pendidikan Fisika Al-Biruni
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/jipfalbiruni.v12i2.18101

Abstract

Radiotherapy is a non-surgery therapy that employs ionizing radiation like X-ray or even radiation to cure cancer as a curative activity. Radiation dose rate analysis is required for the person who worked on radiotherapy to strengthen safety precautions for radiation protection, notably in oncology radiation. The research attempted to disclose time trends and radiation dose rate exposure variations among personnel in radiotherapy installation. Radiation dose examination utilizing four-elements TLD received from 16 respondents grouped into six groups (radiation oncologist, medical physicist, radiotherapist, electromedicine, nurse, and sculptor). The number of occupancy exposures rose 55.5% from 2018 to 2022. The most significant annual radiation dose rate for 900 patient workloads attained by medical physicists was 0.996 mSv. In addition, electronics receive the lowest annual radiation dose at Unand Hospital. Annual effective dose exposure by radiation is still safe, below national or international regulations. However, a protective improvement process is vital to limit radiation interaction, particularly for medical physicists, who are the most vulnerable to radiation exposure.
Co-Authors Alifah, Amalia Nur Devianto, Dodi Dian Milvita Edina, Almira Ivah Fiqi Diyona, Fiqi Fitri, Fadhilah Ketrin, Melin Wanike Mousa, Almahdi Nisa, Alvi Khairin Ramacos Fardela Ramadhani, Nia Rasyada, Latifah Aulia Ridho Saputra, Ridho