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Performance Evaluation of the Gated Recurrent Unit Model in Predicting the Closing Stock Price of PT Aneka Tambang Tbk Wahyu Erinna Ratih; Safira Fitri Anggraini; Tri Maryono Rusadi
Contemporary Mathematics and Applications (ConMathA) Vol. 8 No. 1 (2026)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v8i1.86603

Abstract

Stock price prediction is a crucial task in financial market analysis due to its impact on investment decision-making. This study aims to apply the Gated Recurrent Unit (GRU) model to forecast the stock price of ANTM.JK using historical time series data. A total of 12 experimental models were developed by varying data split ratios, window sizes, epochs, and batch sizes to identify the optimal model configuration. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that the GRU model is capable of predicting stock prices with high accuracy, achieving an accuracy of 98.02%. The RMSE values ranged from 57.78 to 91.09, MAE values ranged from 38.20 to 62.87, and MAPE values ranged from 1.98% to 3.22%. The best-performing model was Model 7, with a 70:30 training–testing split, a window size of 30, 50 epochs, and a batch size of 16, which produced the lowest error values among all models. These findings indicate that GRU is an effective and reliable approach for modeling nonlinear and dynamic stock price time series and has strong potential for supporting financial market analysis and investment decision-making.
Kontrol Optimal untuk Model SIR Campak dengan Imunisasi Menggunakan Prinsip Minimum Pontryagin Dhea Wasila Rahmi; Elyin Fitrawati; Amilia Ulul Azmi; Bulqis Nebulla Syechah; Tri Maryono Rusadi
Jurnal Matematika Vol. 16 No. 1 (2026)
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Science, Udayana University Gedung UKM, Ruang UKM 8 Lt 1, Kampus Bukit Jimbaran, Badung-Bali.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2026.v16.i01.p197

Abstract

Measles is a contagious disease that continues to affect a significant portion of the population, particularly infants and children. The disease can be prevented through immunization programs, including both basic and booster immunizations, which are part of government public health initiatives. This study aims to reduce the spread of measles while minimizing immunization costs by incorporating a control variable into the SIR (Susceptible–Infected–Recovered) model of disease transmission. The method employed is Pontryagin’s Minimum Principle to determine the optimal immunization strategy that is both effective and cost-efficient. The results indicate that the inclusion of an immunization control in the model significantly decreases the susceptible population and reduces the growth rate of the infected compartment. Furthermore, the recovered population increases more rapidly compared to the model without control. The proportion of the immunized population demonstrates that a more optimal control strategy leads to greater effectiveness in suppressing disease transmission. Therefore, the application of optimal control in the SIR model provides a valuable mathematical framework to support immunization policies for measles prevention and control.
PERAMALAN SUHU UDARA RATA-RATA DI KOTA MATARAM MENGGUNAKAN METODE LEAST SQUARE Putri Amalia Wardani; Erin Anaras; Dewi Astuti; Nuzla Af’idatur Robbaniyyah; Tri Maryono Rusadi
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study focuses on forecasting the average air temperature in the city of Mataram by applying the Least Square method to determine data patterns based on historical data. The data used includes the average air temperature from 2019 to 2023, obtained from the BPS Mataram City. Based on the pattern used, the resulting forecasting model for the average air temperature is Ŷ = 27.44 - 0.143X. The forecasted results indicate a gradual decrease in the average air temperature from 2024 to 2030 in Mataram. This trend is reflected by the change in the forecasted average air temperature value in the model, with a rate of -0.143. The model's accuracy is demonstrated by a Mean Absolute Percentage Error (MAPE) of 3.22%, indicating a very good level of accuracy in forecasting the average air temperature for the predicted years. This research is expected to provide useful information for the community and relevant stakeholders in Mataram to anticipate temperature changes and to plan for better climate adaptation policies in the future.
COMPUTATIONAL ANALYSIS OF TOPOLOGICAL INDICES ON POWER GRAPHS OF MODULO PRIME POWER GROUPS USING PYTHON Rabbelia Tri Qudrani; Tri Maryono Rusadi; I Gede Adhitya Wisnu Wardhana; Abdul Gazir Syarifudin
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 3 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n3.p459-465

Abstract

This study uses Python to calculate and analyze three indices such as the first Zagreb, Wiener, and Gutman indices on the rank graph of the group modulo the power of a prime number. It relies on formulas that have been developed by previous research. By using Python libraries such as NetworkX, Matplotlib, and Tkinter, the calculation process becomes more efficient and allows visualization of index variations based on changes in the values of prime p and exponent k. The results show that the values of the three indices increase as the values of p and k increase, reflecting the increasing complexity of the graph structure. At large values of p and k, the graph visualization is too complex which causes the graph visualization to be less clear. This approach proves to be effective in supporting visual and quantitative exploration of algebraic structures.
Active Control Of Building Structures By Using Active Mass Dampers With Linear Quadratic Regulator Control Law Tri Maryono Rusadi; Yusuf Fuad; Siska Aprilia Hardiyanti
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p107-112

Abstract

This paper presents a theory of optimal control of building structures that are experienced in the earthquake load excitation by using an active mass damper. Control mechanism is done using active mass exerts a force on the control structure based on structural response is continuously measured. The concept of the Linear Quadratic Regulator (LQR) is used to calculate the required control force structure based on the input acceleration response that is measured using the accelerometer. optimal control system reliability was tested using shear building model is given Active Tuned Mass Damper (ATMD) five stories above the structure to provide the base acceleration excitation of disturbances in the form of a data record from the accelerogram. Test results for various load acceleration theory base, such as acceleration simulation of El-Centro N-S earthquake, Kobe earthquake, earthquake Pacoima, Northridge earthquake, Kern-County earthquake, and the Chichi earthquake showed optimal control is able to give good results. From the analysis and simulation results concluded that the optimal weighting matrix on the value of Q=1000 and the weighting matrix R=0.1 are able to reduce on the structural displacement of the top floor ranged from 19.80% - 58.90%, while for the reduction of the displacement velocity structure in the top floor between 18.24% - 54.18%.
PREDIKSI TINGKAT CURAH HUJAN KOTA MATARAM MENGGUNAKAN LONG SHORT-TERM MEMORY DIMAS ANGGRAWAN HADINATA; IRENE RAINBOW KEWA SOMI; LULUK KARTIKA; TRI MARYONO RUSADI; SISKA APRILIA HARDIYANTI
E-Jurnal Matematika Vol. 15 No. 1 (2026)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MTK.2026.v15.i01.p500

Abstract

Rainfall is an important weather parameter that significantly influences the agricultural sector and regional planning. The City of Mataram, as the center of social and economic activities in West Nusa Tenggara Province, requires an accurate rainfall prediction method. This study aims to predict daily rainfall in Mataram City using a multivariate long short-term memory (LSTM) approach. The data used consist of daily observations from BMKG, with input variables including average temperature, average humidity, average wind speed, and average air pressure. The dataset is structured as a time series using a sliding window approach with a 7-day lookback period and is divided into 80% training data and 20% testing data. The LSTM model is constructed with two LSTM layers containing 64 and 32 units, respectively, complemented by a 0,2 drop out layer and a Dense layer as the output for prediction. Evaluation using MAE and RMSE indicates that a configuration of 100 epochs and a batch size of 16 provides the best performance, achieving MAE of 4,020 mm and RMSE of 7,915 mm on the testing data, demonstrating the model’s capability to predict daily rainfall in a stable manner.