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All Journal JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI BAREKENG: Jurnal Ilmu Matematika dan Terapan
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

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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.
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.
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 Devianto, Dodi Dian Milvita Fiqi Diyona, Fiqi Fitri, Fadhilah Ketrin, Melin Wanike Mousa, Almahdi Nisa, Alvi Khairin Ramacos Fardela Ramadhani, Nia Rasyada, Latifah Aulia Ridho Saputra, Ridho