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This Author published in this journals
All Journal Jurnal Matematika 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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Estimasi Dampak Jangka Panjang Kebijakan Penurunan Emisi Terhadap Pertumbuhan Ekonomi Kalimantan Timur Nurul Ilma Hidayanty; Riki Herliansyah; Muhammad Azka
Jurnal Matematika Vol 9 No 1 (2019)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2019.v09.i01.p109

Abstract

Deforestation and forest degradation issue due to economic activity is a major problem in East Borneo. The results of data analysis from Environmental Agency stated East Borneo produced the biggest ammount of CO2 emissions after the Central Borneo and Riau. These emissions mostly come from land-based business sector, industry and transportation. In other side, these sectors are the highest contributor to the total number of Gross Domestic Regional Product (GDRP). The aim of this research is to identify the effect of the goverment policy to decrease CO2 emissions towards economic growth. The data in this research were analyzed using Principal Component Analysis (PCA) and Path Analysis (PA). The results showed that the decrease in the amount of CO2 emissions resulting in decrease in the amount of GDRP. Keywords: CO2 Emissions, Gross Domestic Regional Product (GDRP), Principal Component Analysis, Path Analysis.
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: 10.35580/2ane1252

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.
Co-Authors Indrawan Nurul Ilma Hidayanty Putri Amalia Riki Herliansyah