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All Journal Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP) Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Sari, Meylita
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Computer Science & IT Mathematics

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GWO-Enhanced Hybrid Deep Learning with SHAP for Explainable TLKM.JK Stock Forecasting Bukhori, Hilmi Aziz; Bukhori, Saiful; Anam, Syaiful; Yusuf, Feby Indriana; Sari, Meylita
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5205

Abstract

This study presents an innovative Grey Wolf Optimization (GWO)-enhanced hybrid deep learning model integrating Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer, combined with SHAP for interpretable stock price forecasting of TLKM.JK from July 29, 2024, to July 29, 2025. Addressing non-linear market dynamics, the model evaluates seven experimental cases, with the GWO-optimized configuration (Case 2) achieving superior performance, with a Root Mean Squared Error (RMSE) of 75.23, Mean Absolute Error (MAE) of 58.14, and Directional Accuracy (DA) of 76.2%, surpassing the baseline by 17.4% in RMSE and 8.1% in DA. Notably, Case 2 excels during the April 2025 surge (11.8% increase, MAE 53, DA 82%) and the high-volume day of May 28, 2025 (531,309,500 shares, MAE 48), leveraging Volume (SHAP 0.45) and RSI (0.28) as key predictors. With a 4-hour convergence time on an NVIDIA RTX 3060 GPU, the model ensures computational efficiency and interpretability, making it a robust tool for traders. Despite limitations in single-stock focus and GPU dependency, this framework advances AI-driven financial forecasting by offering transparent, high-accuracy predictions, paving the way for multi-stock applications and real-time SHAP updates.
Discrete-Time Dynamics of Deposit-Loan Volumes Model with Repayment Rate: Standard and Non-Standard Approaches Musafir, Raqqasyi Rahmatullah; Sari, Meylita
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.35039

Abstract

In the banking system, the repayment rate of loans, which is influenced by interest rates and nonperforming loans, plays an important role in the bank’s cash flow. In this paper, we propose a discrete model of deposit–loan volumes by considering the repayment rate. The proposed model involves the standard forward Euler discretization and the non-standard finite difference (NSFD) scheme. The numerical schemes of both models are explicitly defined. Both models have three fixed points, i.e., the transaction-free point, the loan-free point, and the active-transaction point. The transaction-free fixed point is unstable, while the other two are locally asymptotically stable under certain conditions. The stability of the Euler model’s fixed point depends on the stepsize h. This indicates that the NSFD model is dynamically more consistent since it does not depend on h. Numerical simulations also confirm that the stability property of the NSFD model’s fixed points does not depend on h. Meanwhile, the stability of the fixed points of the Euler model depends on h. The simulations also show that the Euler model undergoes period-doubling and Neimark–Sacker bifurcations. This is indicated by changes in the stepsize that cause the convergence of the solution to shift into oscillations or even chaos. The chaotic condition is an undesired or even avoided situation in the banking sector. High and irregular fluctuations lead to the failure of policy control and liquidity projection. We also performed a case study using weekly loan data from September 2022 to March 2025 via parameter estimation. We use two performance metrics, i.e., the coefficient of determination (R2) and the root mean square error (RMSE). Both models produce realistic parameter values and provide a good fit to the data trend, as observed visually and from R2. Based on RMSE, the NSFD model performs better than the Euler model. Moreover, the larger the h, the better the performance. These results suggest the use of the NSFD model, which has better relevance and accuracy than the Euler model.
PENERAPAN GENERALIZED LINEAR MODEL DALAM MENANGANI OVERDISPERSI PADA DATA PENGANGGURAN DI INDONESIA Sari, Meylita
Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 17 No 2 (2025): Jurnal Ilmiah Matematika dan Pendidikan Matematika (JMP)
Publisher : Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jmp.2025.17.2.19005

Abstract

Unemployment remains a major problem in Indonesia. In 2023, the open unemployment rate was recorded at 5.32%, or approximately 7.86 million people. Low absorption of productive-age workers and limited job opportunities have led to a potential increase in unemployment in several regions. This situation requires in-depth analysis to reduce the increase in the open unemployment rate. Various socio-economic factors in Indonesia influence the unemployment rate and can be analyzed using the Generalized Linear Model (GLM) framework. Because unemployment data is count data, the approach used is Poisson Regression. This model has the basic assumption of equidispersion, meaning that the variance is equal to the mean. However, observations indicate that the variance is greater than the mean, resulting in overdispersion. To address this, a GLM development with an additional dispersion parameter, namely Poisson Generalized Inverse Gaussian Regression (PGIGR), was used. This model was chosen because it can capture greater data variation and represent factors that influence the unemployment rate. The results of this study indicate that the number of unemployed between provinces in Indonesia is influenced by the variables of Provincial Minimum Wage, GRDP Growth Rate on a Constant Basis, Literacy Rate, and TPAK, with an AIC value of 33,577.45.
Co-Authors Bukhori, Hilmi Aziz Feby Indriana Yusuf Musafir, Raqqasyi Rahmatullah Saiful Bukhori Syaiful Anam