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All Journal REKAYASA Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Safitri, Emeylia
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Physics

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Air Temperature Prediction System Using Long Short-Term Memory Algorithm Faulina, Ria; Nuramaliyah, Nuramaliyah; Safitri, Emeylia
Rekayasa Vol 17, No 3: Desember, 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v17i3.28229

Abstract

Air temperature is a highly essential parameter in weather forecasting methods and a critical variable for predicting future weather patterns. An accurate temperature prediction system can assist individuals and organizations in preparing for activities heavily influenced by weather conditions. Therefore, developing a precise temperature prediction model requires a reliable and effective algorithm. In this study, the Long Short-Term Memory (LSTM) algorithm, a type of artificial neural network (Recurrent Neural Network - RNN), is implemented with time series data decomposition for variable input processing. LSTM is specifically designed to handle sequential data or time series data, such as weather data. Additionally, LSTM-GRU and LSTM-Conv1D models are utilized. The dataset used in this research comprises air temperature data provided by the Meteorology, Climatology, and Geophysics Agency (BMKG) in the DKI Jakarta region. Model evaluation is conducted using criteria for the smallest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Experiments show that the prediction system based on LSTM-GRU achieves the lowest MAE and RMSE values compared to LSTM and LSTM-Conv1D, across 10, 20, and 30-step predictions. It can be concluded that the LSTM-GRU algorithm provides the most accurate predictions compared to the LSTM and LSTM-Conv1D models for sequential temperature data, given sufficient data and a properly configured model. This is also graphically demonstrated by prediction results closely aligning with the actual data. 
PENGARUH MATEMATIS JUMLAH MAHASISWA UNIVERSITAS TERBUKA TERHADAP ANGKA PARTISIPASI KASAR PERGURUAN TINGGI TIAP PROVINSI DI INDONESIA Nuramaliyah, Nuramaliyah; Safitri, Emeylia; Faulina, Ria
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.830

Abstract

Education is a key determinant of the quality of human resources in Indonesia. One indicator used to measure educational participation in a region is the Gross Enrolment Ratio (GER). This study focuses on analyzing the influence of the socio-economic conditions of the population and the number students of Universitas Terbuka on the Gross Enrolment Ratio for higher education (GER-HE) in Indonesia. The aim of this research is to analyze the factors that influence the Gross Enrolment Ratio for higher education in several regions. This study uses a quantitative research design with a regression panel data approach. The study area covers all provinces in Indonesia, comprising 34 provinces. The data used in this study is secondary data obtained from the Badan Pusat Statistik (BPS) for the years 2018-2022, including data on GER-HR, poverty indicator, the number of higher education institutions, and expenditure per capita for each province. Additionally, data was sourced from DAAK-UT to obtain the number of Universitas Terbuka students for the years 2018-2022. Based on the results of the Fixed Effect Model (FEM) panel data regression with individual/cross section effects, the factors that influence the GER-HR value are the number of new UT students and per capita expenditure. The number of new UT students has a positive effect while per capita expenditure has a negative effect on GER-HE in Indonesia. Then for variables that have no effect are the number of universities, and per capita expenditure.
MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS Fitriana, Ika Nur Laily; Leviany, Fonda; Faulina, Ria; Nuramaliyah, Nuramaliyah; Safitri, Emeylia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.844

Abstract

The agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies greatly. Hence, the suitable statistical methods are needed, namely cluster analysis, to group 38 provinces in Indonesia based on similar characteristics in the agricultural sector. Cluster analysis in this research uses the Self-organizing Maps (SOM) method. Before cluster analysis is carried out, Principal Component Analysis (PCA) is carried out to reduce the dimensions of the variables so that the data is easier to process and avoids the curse of dimensionality. The PCA results obtained 2 main components formed from 9 agricultural sector variables, which were then used as input data for clustering analysis with SOM. The results of clustering with SOM showed that the optimal number of provincial groups was 3 with a Davies-Boulden Index (DBI) value of 0.544 and a Silhouette of 0.623. The results of grouping the provinces can then be categorized into cluster 1 with a high average value of agricultural sector variables, cluster 2 with a medium average value of agricultural sector variables, and cluster 3 with a low average value of agricultural sector variables.
Co-Authors Faulina, Ria Fitriana, Ika Nur Laily Leviany, Fonda Nuramaliyah, Nuramaliyah