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All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Nina Fitriyati
Universitas Islam Negeri Syarif Hidayatullah Jakarta
Decision Sciences, Operations Research & Management Education Mathematics

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PERBANDINGAN METODE BACKWARD DAN FORWARD PADA SELEKSI MIXED-EFFECTS MODEL: ANALISIS FRAGILE STATE INDEX ASIA TENGGARA Mega Maulina; Madona Yunita Wijaya; Nina Fitriyati
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (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.v5i1.495

Abstract

In the era of globalization, concerns about the stability and vulnerability of nations have become a primary focus in the field of international relations research. One of the indicators measuring a country’s vulnerability is the Fragile State Index. This research discusses the analysis of Fragile State Index in Southeast Asian countries using panel data observed from 2010 to 2021, focusing on economic impacts. The factors involved include Gross Domestic Product, General Government Net Lending/Borrowing, Inflation, and Life Expectancy. A mixed-effects model is employed to examine the influence of economic factors on the Fragile State Index. The selection of the best model is carried out by comparing Backward Elimination and Forward Selection procedures. The research findings indicate that the best model for interpreting the influence of economic factors on the Fragile State Index is the one using the Backward Elimination method. Significant variables in this model include Gross Domestic Product, General Government Net Lending/Borrowing, Life Expectancy, and the interaction between General Government Net Lending/Borrowing and Inflation
PREDIKSI KINERJA KEUANGAN PERUSAHAAN ASURANSI SYARIAH MENGGUNAKAN METODE ARIMA, EXPONENTIAL SMOOTHING, DAN HYBRID Devita Apriliani; Nina Fitriyati; Dhea Urfina Zulkifli
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): 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.v4i3.496

Abstract

Insurance is a contract made by an insurance company with a policy holder. One way to assess the stability of company is its financial performance. The aim of this study is to predict the financial performance of sharia insurance company using two factors, namely ROA (Return on Assets) and ROE (Return on Equity). The secondary data used in this research is derived from the quarterly financial reports of an Islamic insurance company from the year 2013 to 2022. The study used ARIMA (Autoregressive Integrated Moving Average), Exponential Smoothing (ETS), and Hybrid methods. The results showed that the ARIMA model is the best way to better predict both ROA and ROE. Compared to other methods that have been evaluated, the ARIMA model showed more accurate results in predicting financial  performance measured through ROAs and ROEs. Predictions show that the ROA value is relatively stable, indicating that the company is efficient in managing resouces while the ROE value tends to fail, which indicates that the identified company is less good at delivering returns to shareholders. Therefore, it can be one of considerations for people who wants to buy insurance or invest
ANALISIS LAJU PREDIKSI INFLASI DI INDONESIA: PERBANDINGAN MODEL GARCH/ARCH DENGAN LONG SHORT TERM MEMORY Agisna Mutiara; Nina Fitriyati; Mahmudi Mahmudi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (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.v5i1.508

Abstract

Inflation is a condition wherein the general level of prices for goods and services in an economy continually rises. Predicting inflation serves as a crucial link in establishing future inflation values. The dynamic nature of inflation allows for changes over time, forming a nonlinear model capable of providing more accurate inflation predictions. This research aims to examine and compare the effectiveness of GARCH/ARCH and LSTM models in predicting inflation data.The results of the study indicate that the LSTM model proves to be the superior choice for predicting inflation with higher accuracy when compared to the GARCH model. The GARCH model produces more accurate predictions of future inflation periods, as evidenced by the Mean Absolute Percentage Error (MAPE) value of 2.6814%. This value signifies the extent of the comparison between the actual and predicted model values. In contrast, the LSTM model yields a Mean Absolute Error (MAE) value of 0.00895358, demonstrating its superior accuracy.Therefore, the findings of this research can be considered a valuable reference for a country looking to predict inflation more effectively. Utilizing advanced time series models, such as LSTM, can enhance the accuracy of inflation predictions, providing policymakers with valuable insights for informed decision-making
PREDIKSI HARGA PENUTUPAN SAHAM BANK CENTRAL ASIA: IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY DAN PERBANDINGANNYA DENGAN SUPPORT VECTOR REGRESSION Rizky Azriel Fahrezi; Madona Yunita Wijaya; Nina Fitriyati
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (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.v5i1.582

Abstract

Stock is an instrument of the financial market that is very popular among other instruments because it has an attractive yield. The research discusses the prediction of Bank Central Asia shares, named BBCA, using the Long Short-Term Memory (LSTM) method. The LSTM model is a very popular deep learning algorithm that is suitable for predicting time-related data, historical data, and sequential data. We configure the LSTM model with the following hyperparameters: number of neurons of 60, batch_size of 64, timesteps of 32, epoch of 12, and dense layer of one unit while the configuration for SVM support vector machine model with Gaussian Radial Basic Function kernel and hyperparameter γ = 0.0001 and c = 1000. BBCA prediction results are quite good when compared to the SVM model with a MAPE of 1.07%.
Co-Authors Agisna Mutiara Devita Apriliani Mahmudi Mahmudi Mega Maulina Rizky Azriel Fahrezi Wijaya, Madona Yunita Zulkifli Zulkifli