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All Journal ESTIMASI: Journal of Statistics and Its Application Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT)
Agusrawati, Agusrawati
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mathematics

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Journal : ESTIMASI: Journal of Statistics and Its Application

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Pemodelan Regresi Spasial pada Tingkat Kemiskinan di Pulau Sulawesi Said, Baharuddin; Agusrawati, Agusrawati; Laome, Lilis
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i1.40494

Abstract

In regression analysis, the independence assumption of the error terms is often violated when working with spatial data. The 2023 poverty incidence data across regencies/municipalities on Sulawesi Island indicate the presence of spatial autocorrelation. This study aims to compare the performance of classical regression, spatial autoregressive model (SAR), and spatial error model (SEM) in modeling poverty incidence on the island. The regency/municipality-level data used in the study is secondary data published by BPS-Statistics Indonesia. The findings reveal that the SEM model provides more accurate parameter estimates compared to classical regression and SAR model. Factors that have a significant influence on the poverty incidence (Y) in a regency/municipality are life expectancy (X1), expenditure per capita (X2), and the error terms for the nearest neighboring regions (λ).
Implementasi Model Long Short Term Memory (LSTM) Pada Proyeksi Harga Saham (Studi Kasus: PT. Pertamina Geothermal Energy (Persero)) Arisona, Dian Christien; Agusrawati, Agusrawati; Makkulau, Makkulau; Yahya, Irma; Wibawa, Gusti Ngurah Adhi; Baharuddin, Baharuddin; Fahyuni, Putri Riski
ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 2, Juli, 2025 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v6i2.44963

Abstract

This research presents a comprehensive analysis of the Long Short Term Memory (LSTM) method in projecting the stock price of PT. Pertamina Geothermal Energy (Persero). Utilizing daily stock price data, the LSTM model achieves a high level of accuracy with a Mean Absolute Percentage Error (MAPE) value of 0.84%. The LSTM's gate mechanism (input, forget, output) enables it to store long-term information, controlling the flow of information to update memory, delete irrelevant data, and generate predictions. Optimized with backpropagation through time (BPTT) and activation functions, the LSTM model proves effective in investment decision making, providing valuable insights for investors and market players to anticipate stock price fluctuations. This research demonstrates the great potential of machine learning in financial analysis, particularly in stock price projection and time series analysis. The results indicate that LSTM can be a valuable tool for investors and financial analysts, enhancing their ability to make informed decisions.
Co-Authors Adhi Wibawa, Gusti Ngurah Arisona, Dian Christien Baharuddin Baharuddin Baharuddin Bahriddin Abapihi Fahyuni, Putri Riski Irma Yahya Lilis Laome Makkulau Makkulau Wibawa, Gusti Ngurah Adhi