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All Journal Specta Journal of Technology Journal of Mathematics, Computation and Statistics (JMATHCOS)
Muhammad Azka
Institut Teknologi Kalimantan
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Environmental Science Mathematics

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Frekuensi Hari Hujan Menurut Bulan di Kota Balikpapan dengan Rantai Markov Waktu Diskrit Muhammad Azka
SPECTA Journal of Technology Vol. 1 No. 2 (2017): SPECTA Journal of Technology
Publisher : LPPM ITK

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.862 KB) | DOI: 10.35718/specta.v1i2.75

Abstract

The problem proposed in this research is about the amount rainy day per a month at Balikpapan city and discretetime markov chain. The purpose is finding the probability of rainy day with the frequency rate of rainy at the next month if given the frequency rate of rainy at the prior month. The applied method in this research is classifying the amount of rainy day be three frequency levels, those are, high, medium, and low. If a month, the amount of rainy day is less than 11 then the frequency rate for the month is classified low, if a month, the amount of rainy day between 10 and 20, then it is classified medium and if it is more than 20, then it is classified high. The result is discrete-time markov chain represented with the transition probability matrix, and the transition diagram.
Forecasting Sharia Stock Prices Using Hybrid STL Decomposition–LSTM and SARIMA Models: A Case Study of PT Semen Indonesia (Persero) Tbk Indrawan Indrawan; Muhammad Azka; Putri Amalia
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/Jmathcos11934

Abstract

Stock price forecasting is a challenging task due to the complex, nonlinear, and dynamic nature of financial time series data. This study aims to develop a hybrid forecasting model by integrating Seasonal and Trend Decomposition using Loess (STL) with Long Short-Term Memory (LSTM) and to compare its performance with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model as a linear benchmark. The empirical analysis is conducted using daily closing price data of PT Semen Indonesia (Persero) Tbk (SMGR) over the period from March 2018 to March 2026. The proposed approach applies STL decomposition to separate the time series into trend, seasonal, and residual components, enabling the LSTM model to capture nonlinear patterns more effectively. Forecasting performance is evaluated using the Mean Absolute Scaled Error (MASE) on an out-of-sample testing dataset. The results show that the hybrid STL–LSTM model achieves superior accuracy, with a MASE value of 0.4738, significantly outperforming the SARIMA model, which yields a MASE value of 2.7073. In contrast, the SARIMA model produces overly smooth forecasts and fails to capture short-term fluctuations and nonlinear dynamics present in the data. These findings indicate that the integration of STL decomposition and LSTM provides a more effective and flexible framework for modeling complex financial time series. The proposed model not only improves forecasting accuracy but also produces stable and reliable predictions, making it suitable for practical applications in stock price forecasting.
Mortality Risk Modeling for Patients with Lung Disease Using Cox Regression and Gradient Boosting Survival at Pertamina Hospital in Balikpapan Nurul Maqfirah Rauf; Isti Kamila; Muhammad Azka; Indrawan Indrawan; Nur Qadri Bahar
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/137g3361

Abstract

Lung disease is a health issue associated with mortality risk; therefore, an analysis is needed to identify factors associated with the risk of death and to model patient survival risk. This study aims to identify factors associated with mortality risk in patients with lung disease using Cox Proportional Hazards Regression and to develop a predictive model using Gradient Boosting Survival. The study utilized medical records from 1,119 inpatients with lung disease at Pertamina Hospital in Balikpapan during the 2024–2025 period. Analyses were conducted using Kaplan-Meier, Cox Proportional Hazards Regression, and Gradient Boosting Survival. Hyperparameter optimization for Gradient Boosting Survival was performed using Grid Search with 5-fold stratified cross-validation on the training data. The results of the Cox Proportional Hazards Regression showed that BMI, diastolic blood pressure, and Chronic Obstructive Pulmonary Disease (COPD) were significantly associated with the risk of mortality, with a C-index of 0.6500. Gradient Boosting Survival yielded the optimal hyperparameters: a learning rate of 0.10, n_estimators of 200, max_depth of 2, and min_samples_leaf of 10, with a C-index of 0.6576 on the training data and 0.5575 on the testing data. Feature importance analysis shows that BMI, diastolic blood pressure, systolic blood pressure, SpO₂, and COPD make the largest contributions to the model. Cox Proportional Hazards Regression provides information on the direction and magnitude of the relationship between risk factors through the hazard ratio, while Gradient Boosting Survival provides information on the relative contributions of variables through feature importance. Thus, both approaches provide complementary information in modeling the risk of mortality in patients with lung disease.
Co-Authors Indrawan Isti Kamila Nur Qadri Bahar Nurul Ilma Hidayanty Nurul Maqfirah Rauf Putri Amalia Riki Herliansyah